Fuzzy object deduplication using surface-based representations in content creation systems and applications

Abstract

The methods presented herein are used to de-duplicate object representations in a three-dimensional (3D) scene. One or more features may be extracted from an object in a three-dimensional (3D) scene to compute an individual object signature, which may be a representation of an appearance of the object. The object signatures may then be compared using one or more similarity metrics in order to determine whether the object is duplicated and / or nearly duplicated. For duplicated objects, the common representation may be used for each duplicated object rendered at a given location and orientation within the scene, thereby rendering objects within the scene using less memory and computing resources.

Description

Background Technology

[0001] Content creation programs can use a variety of different surface-based representations, such as meshes, to represent three-dimensional (3D) objects. Meshes can be associated with individual objects within a scene. For some scenes, objects may be duplicates or near-duplicates. For example, a warehouse might consist of several shelves with boxes, where each shelf and / or box may be substantially the same size. However, when the mesh is transformed or generated within the environment, the geometry may be distorted due to factors such as export settings, import settings, transformations, etc. Therefore, objects are described with unique mesh information, but the information differs only slightly between near-duplicate entries. This results in wasted and / or inefficient use of computational resources. For example, multiple representations might be stored and tracked instead of storing a single object representation and then rendering that object as needed. Additionally, loading times may increase and rendering efficiency may decrease due to the additional representations. Attached Figure Description

[0002] Various embodiments according to this disclosure will be described with reference to the accompanying drawings, in which:

[0003] Figure 1 The illustration shows example environments for importing and processing scenes within an interactive environment, according to various embodiments.

[0004] Figure 2 The illustration shows example representations of objects in a scene in two different orientations according to various embodiments;

[0005] Figure 3 The illustration shows example environments for deduplicating scene-related object representations according to various embodiments;

[0006] Figure 4 The illustration shows example environments for deduplicating scene-related object representations according to various embodiments;

[0007] Figure 5A The illustration shows example processes for identifying and replacing duplicate objects in a scene, according to various embodiments;

[0008] Figure 5B The illustration shows example processes for identifying and replacing duplicate objects in a scene, according to various embodiments;

[0009] Figure 5C The illustration shows example processes for identifying and replacing duplicate objects in a scene, according to various embodiments;

[0010] Figure 6The illustration shows components of a distributed system that can be used to generate map data of an environment, according to at least one embodiment;

[0011] Figure 7A The inference and / or training logic according to at least one embodiment is illustrated;

[0012] Figure 7B The inference and / or training logic according to at least one embodiment is illustrated;

[0013] Figure 8 An example data center system according to at least one embodiment is shown;

[0014] Figure 9 A computer system according to at least one embodiment is shown;

[0015] Figure 10 A computer system according to at least one embodiment is shown;

[0016] Figure 11 At least a portion of a graphics processor according to one or more embodiments is shown;

[0017] Figure 12 At least a portion of a graphics processor according to one or more embodiments is shown;

[0018] Figure 13 This is an example data flow diagram of an advanced computing pipeline according to at least one embodiment;

[0019] Figure 14 This is a system diagram of an example system for training, adapting, instantiating, and deploying machine learning models in an advanced computing pipeline, according to at least one embodiment; and

[0020] Figure 15A and Figure 15B A data flow diagram of the process for training a machine learning model according to at least one embodiment is shown, as well as a client-server architecture for enhancing annotation tools using a pre-trained annotation model. Detailed Implementation

[0021] In the following description, various embodiments will be described. Specific configurations and details are set forth for illustrative purposes in order to provide a thorough understanding of the embodiments. However, those skilled in the art will also understand that the embodiments may be practiced without specific details. Furthermore, well-known features may be omitted or simplified so as not to obscure the described embodiments.

[0022] The systems and methods described herein may be used, but are not limited to, non-autonomous vehicles or machines, semi-autonomous or autonomous vehicles or machines (e.g., in one or more advanced driver assistance systems (ADAS), one or more in-vehicle infotainment systems, one or more emergency vehicle detection systems), manned and unmanned robots or robotic platforms, warehouse vehicles, off-road vehicles, vehicles coupled to one or more trailers, aircraft, boats, shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, engineering vehicles, trains, underwater vehicles, remotely controlled vehicles (such as drones), and / or other vehicle types. Furthermore, the systems and methods described herein may be used for a variety of purposes, by way of example and not limitation, for machine control, machine motion, machine driving, synthetic data generation, generative AI, model training or updating, perception, augmented reality, virtual reality, mixed reality, robotics, security and supervision, simulation and digital twins, autonomous or semi-autonomous machine applications, deep learning, environmental simulation, data center processing, conversational AI, optical transport simulation (e.g., ray tracing, path tracing, etc.), collaborative content creation of 3D assets, generative AI, cloud computing, and / or any other suitable application.

[0023] The disclosed embodiments can be included in a variety of different systems, such as automotive systems (e.g., in-vehicle infotainment systems for autonomous or semi-autonomous machines, perception systems for autonomous or semi-autonomous machines), systems implemented using robots, aviation systems, medical systems, boating systems, smart area monitoring systems, systems for performing deep learning operations, systems for performing simulation operations, systems for performing digital twin operations, systems implemented using edge devices, systems containing one or more virtual machines (VMs), systems for performing synthetic data generation operations, systems implemented at least partially in a data center, systems for performing conversational AI operations, systems implementing one or more language models (such as Large Language Model (LLM), Visual Language Model (VLM), etc.), systems for performing generative AI operations (e.g., using one or more language models, converter models, etc.), systems for performing optical transmission simulation, systems for performing collaborative content creation of 3D assets, systems implemented at least partially using cloud computing resources, and / or other types of systems.

[0024] The approaches described in various embodiments can be used to deduplicate objects within a scene. Objects can be represented as three-dimensional (3D) descriptions, such as meshes or point clouds, and a signature can be computed for a given object. As an example, for a mesh, the vertices of triangles can be used to compute the covariance matrix, and then singular value decomposition (SVD) can be used to compute the signature (e.g., a 3D vector) and rotation / or orientation information. The signatures of each object in the scene can then be compared to identify duplicate and / or nearly duplicate objects, such as within a KD-tree or some other structure. Objects within a threshold similarity can then be replaced by a single representation and rendered at the appropriate location and orientation within the scene. Various embodiments can also extend the signature to include additional dimensions, such as normals, color, etc., to provide more granularity for defining objects and identifying duplicate and / or nearly duplicate objects. The systems and methods of this disclosure can be implemented as part of a scene optimizer, enabling more efficient storage of the input 3D scene, which can improve rendering, loading, and resource utilization.

[0025] Various embodiments address and overcome problems in 3D scene rendering and storage where the scene may contain multiple identical or nearly identical copies of various object representations (e.g., meshes, point clouds, etc.). In at least one embodiment, the copies may be associated with objects that may exist in the scene environment (e.g., an industrial environment that may include robotic equipment, mechanical parts, components of parts, etc.). Object representations may also be referred to as surface-based representations and may include structures such as triangles, quadrilateral meshes, and / or other representations. In operation, while objects in the scene may be “repeating objects” (e.g., having one or more similarity metrics within a threshold), variables exist in the transformations or topologies when storing and rendering various objects, so different data may be used to represent individual objects. For example, some instances of a mesh may be transformed to different locations with minor deformations, or other details added and / or missing. As another example, objects may be different colors but may have the same surface structure; therefore, existing systems can store objects separately rather than storing multiple objects as a single representation and then applying the desired color. Describing each object in these objects using unique mesh information (data with only minor variations) is inefficient and can lead to excessive memory usage, increased loading times, and inefficient rendering on graphics processing units (GPUs). The systems and methods disclosed herein address and overcome the problems of existing methods for identifying and removing duplicates. For example, one existing approach assumes that duplicate objects are exact copies that have undergone rigorous transformation. These methods find matches by simply matching on the topological data and efficiently deduplicate and share mesh data. However, in practice, object instances often do not match topologically. The systems and methods address and overcome this drawback by implementing fuzzy matching, which the user can control to deduplicate mesh or other object representations.

[0026] In view of the teachings and suggestions contained herein, it will be apparent to those skilled in the art that various other such functions may also be used within the scope of the various embodiments.

[0027] Figure 1 An example environment 100 in which the various aspects of this disclosure can be executed is illustrated. In this example, an interactive environment 102 receives and processes a scene 104 for rendering and use within the interactive environment 102. The interactive environment 102 may be a distributed environment accessible via one or more network and / or local hosting programs that may receive the scene 104 as input for rendering and presentation on one or more client devices, which may allow users of one or more client devices to interact with and / or modify aspects of the scene.

[0028] Scene 104 can be transmitted to interactive environment 102 as a data file and can include various different formats. For example, scene 104 can be rendered in one or more content creation programs (such as computer-aided drafting (CAD) programs). Therefore, interactive environment 102 can include import engine 106, which can be used to evaluate one or more parts of scene 104 and, if necessary, transform different parts of scene 104 into different file types. In at least one embodiment, interactive environment 102 can use the Universal Scene Description (USD) format. However, it should be understood that other formats can be used within interactive environment 102 and / or can be compatible with interactive environment 102. For example, different objects within scene 104 can be associated with different file formats, but one or more features (such as positional information of different surfaces) can be compatible with interactive environment 102 and / or can be used by interactive environment 102.

[0029] Import engine 106 can be used to extract different features from scene 104 for use by rendering engine 108, which can render one or more objects 110 on a display associated with a user device. Objects 110 can correspond to 3D objects that include features such as geometric representation, texture, shading, color, etc. As discussed herein, objects 110 can be represented by meshes or other representations such as NURBS, Bezier patches, subdivided surfaces, etc. One or more objects 110 can be similar or different objects. For example, in a warehouse scene, one or more objects 110 can include duplications, such as generic robots used within warehouses, shelving, boxes, etc. In this example, object A 110A can be a duplication of object B 110B, while object N 110N can be different. Although three objects 110 are shown in this example, more or fewer objects can be associated with a scene, objects can be compiled into a shared scene, multiple objects can be rendered from multiple scenes, and / or various other combinations.

[0030] In at least one embodiment, object 110 may be defined by different object data 112. For individual object 110, object data 112 may be statically predetermined or baked in, and may be provided at least partially from scene 104. For example, the file format associated with scene 104 may include different metadata that can be associated with the object, such as information about how to render the representation within the original creation platform. Object data 112 may include information such as a list of meshes (e.g., vectors, points, etc.), color information, normals, histograms, etc. Each object 110 may have its own associated object data description 114. That is, object A 110A may have object A data description 114A, etc. Object data 112 of individual object 110 can be used to render the scene. By using object data 112, individual components can be represented within the view area, can be moved / rotated, can be modified, etc. However, as discussed herein, in at least one embodiment, there may be object data 112 that substantially describes multiple objects 110, but with minor differences due to warping or other changes in transformation. The system and method of this disclosure can be used to generate a signature for a specific object 110, and then determine whether the objects are within a threshold similarity to each other. If so, duplicate object data descriptions 114 can be removed, and then the object data descriptions 114 can be used to represent multiple different objects 110. In this way, the system and method of this disclosure can be used to deduplicate duplicate and / or nearly duplicate data to improve memory resource utilization, improve rendering operations, and reduce computational resource consumption.

[0031] Even if the topology is not exactly the same, various embodiments can be used to identify matches between objects (e.g., determining duplicate data within a threshold). Systems and methods can compute mesh “feature vectors” through statistical combinations. For example, information such as vertex normals, vertex positions, visual characteristics (e.g., texture coordinates, color, etc.) can be used to generate vectors that can be compared to each other to determine whether an object can be considered a duplicate object and / or a near-duplicate object. In operation, near-duplicate may be sufficient for rendering and usage purposes, and therefore the object is considered a duplicate object. When computing vectors and / or making near-duplicate determinations, various embodiments can prioritize or weight different characteristics. As an example, vertex normals may be sufficient to identify instances of complex meshes (e.g., tens of thousands of meshes) within a large-scale scene. Therefore, if sufficient information is available, systems and methods can tune various parameters to prioritize vertex normals, thus reducing computational resource usage by prioritizing parameters specifically chosen in a particular manner. Additionally or alternatively, if desired or priority information is incomplete or unavailable, various embodiments can use object data (e.g., mesh data) to compute priority information. Returning to the example of vertex normals, information can be computed in parallel by one or more kernel-based programming frameworks (e.g., NVIDIA Warp from NVIDIA), where two kernels run sequentially to produce the vertex normals of the mesh.

[0032] Continuing with the non-limiting example of vertex normals in a triangular mesh, the systems and methods of this disclosure can accumulate the normals (or other identification and / or selection priority information) of all triangles associated with each mesh vertex. The accumulation of normals can be parallelized across all mesh triangles in the scene (e.g., using one or more GPUs), and can be implemented by atomic addition of a temporary 3D vector array used to store the sum of the triangle normals at the respective vertex indices. The 3D vector sum for each vertex can then be normalized to produce the vertex normal. As discussed herein, the systems and methods are not limited to 3D feature vectors such as vertex normals, and can be applied to higher-dimensional features. For example, other feature descriptors that can be used in embodiments of this disclosure include point feature histograms (PFHs). In operation, PFHs take into account vertex neighborhoods to account for the curvature of the vertex neighborhoods. Similarly, as discussed herein, the systems and methods can also implement one or more neural networks to learn feature vectors for different objects and / or scenes. For example, a self-supervised learning algorithm can be employed to optimize neural feature descriptors, maximizing the accuracy of the fuzzy matching process on large mesh sets. Thus, feature descriptors can be specifically learned for mesh sets encountered in a particular domain employing embodiments of this disclosure (such as meshes of machine parts from CAD programs or meshes of objects used by a particular user).

[0033] The systems and methods disclosed herein can provide improvements over various existing deduplication techniques. For example, one or more embodiments may not require precise matching between topological information. For example, when there are minor deviations in the topology or vertex positions cannot be matched with sufficient precision, many similar matches go undetected and remain as unique meshes in the scene, resulting in reduced storage utilization efficiency. Embodiments of this disclosure address and overcome this problem by providing a tunable method to one or more proximity thresholds used to identify duplicate objects. For example, one or more extended or adjustable parameters can be provided to control the weighting of feature descriptors with respect to mesh signatures on matching (e.g., the contribution of certain features). Furthermore, embodiments can provide further improvements to existing systems by combining one or more visual tools that a user can use to identify or select objects within a scene that should be considered duplicates. For example, an initial automated processing step (such as using one or more machine learning techniques) can highlight or mark potentially duplicate and / or nearly duplicate objects, and then the user can provide feedback or approval for some or all of the marked duplicate objects. User feedback can be received quickly, thus providing training information or fine-tuning the matching algorithm to customize it for the specific scenario and grid that the user aims to optimize.

[0034] Figure 2 The illustration shows representations 200 of object 202 in a first configuration 204 and a second configuration 206. For simplicity, the example object 202 is shown as a rectangular prism or cuboid, but the system and method can be applied to any object with different shapes and more complex meshes or other representations. In this example, object 202 includes different slanted regions 208. Slanted regions 208 may be the result of rounding errors and / or other variations when object 202 is imported and loaded into an interactive environment. Slanted region 208A in the first configuration 204 illustrates a non-90-degree angle of the rectangle, thus modifying the length of the corresponding side. Different corners are shown as slanted regions 208B in the second configuration 206. Conventional methods may incorrectly identify object 202 as two separate objects, thus potentially consuming memory capacity with duplicate information. The system and method of this disclosure can be used to address and overcome these problems by generating a feature vector that can be used to compute a signature associated with the object using object information 210. The signature can then be compared for similarity using one or more metrics against a tunable threshold, thus removing duplicate or near-duplicate information.

[0035] The illustrated object information 210 may define or provide information related to the appearance, location, geometry, etc., of object 202. In this example, object information 210 illustrates triangle 212 and its associated vertex identifier (ID) 214. Additional information, such as a vertex list, color information, etc., may also be present. In this example, object 202 is illustrated as having a triangular mesh corresponding to triangle 212 within the associated object information 210. In at least one embodiment, the object may be represented by additional and / or alternative information, such as a vertex list and / or some other textual representation providing information about the location of one or more features of the object (such as points, vertices, faces, etc.). As an example, if a vertex list is provided, the vertex list may include vertex positions represented in an xyz coordinate system.

[0036] In one example, some triangles 212 between object orientations 204 and 206 may have overlapping and / or substantially equal information. For example, triangles along a face (e.g., the triangle closest to the plane of the page) may be substantially identical, but their orientations may differ slightly due to rotations between object orientations 204 and 206. However, tilted regions 208A and 208B will contain different information about vertex IDS and / or associated vectors. Therefore, instead of storing each individual object 202 associated with a corresponding object orientation 204 or 206 as a single file, it might be necessary to store each object 202 and then apply one or more transformations to rotate, shift, etc., for a given orientation. Consequently, individual object information could be loaded separately and then rendered during rendering, thus reducing rendering speed and overall rendering efficiency.

[0037] Figure 3 An example environment 300 that can be used according to embodiments of the present disclosure is illustrated. In one or more embodiments, this configuration may be included as part of an interactive environment and / or as part of a component associated with the interactive environment. Additionally, various features may be invoked or used in response to one or more input commands. For example, if the interactive environment is being used for central storage and management, rather than for rendering, one or more components will not be invoked until the rendering operation uses the input object. As another example, rendering at a "read-only" level or a lower specified resolution may not perform one or more actions, while higher-level rendering applications may use the additional features discussed herein.

[0038] In this example, object 110 can be evaluated by signature engine 302 to generate a signature associated with one or more features of object 110. For example, features can be associated with different parameters of object 110, which can be provided as part of a mesh or other geometric descriptions of object 110 (such as vector IDs, vertices, normals, colors, histograms, etc.). The input format associated with object 110 can be evaluated, and then extraction engine 304 can be used to extract feature information associated with the object. In at least one embodiment, only certain types of information can be extracted, which can be tuned or selected based on user preferences, file types, etc. For example, in at least one embodiment, it can be determined that certain feature information and / or one or more feature information types may be sufficient (e.g., achieving a specified quality level) for signature computation. Thus, only that information can be extracted from the object information. In one example, normals may be sufficient to generate a signature. However, in various embodiments, additional information can be extracted to provide a fine-tuned signature or a more specific signature, which may result in higher accuracy. For example, color information can be extracted to distinguish objects that may have similar geometric appearances but different colors. Similarly, texture information can be extracted to distinguish objects that may share one or more features but differ in other aspects. As an example, flooring materials may have substantially similar features in terms of geometry, but other factors (such as color, texture, reflectivity, etc.) can be used to differentiate between carpet, tile, wood, and concrete flooring materials. In this way, additional dimensions can be added. Dimensions can be selected based on user-specified parameters, domains, etc.

[0039] As discussed herein, one or more embodiments may also utilize one or more machine learning systems for feature extraction. For example, a neural network may be trained using several different objects to determine which features are relevant to signature generation (e.g., exceeding a threshold of relevance) in order to identify duplicate and nearly duplicate objects. In at least one embodiment, the neural network may identify a threshold number of features, combinations of features, the most relevant features, and / or combinations thereof. In this way, feature selection and extraction can be based on object and available feature information, thereby increasing the number of input file types that can be used in an interactive environment.

[0040] The extracted features can then be used to determine the signature of the object, in this example, which could be a vector computed by vector engine 306. The system and method can use covariance descriptors to compress high-dimensional data into a symmetric matrix representation that encodes pairwise covariances between feature dimensions. The descriptor can quantify the magnitude and direction of the multivariate data distribution. An example configuration of this disclosure can compress the extracted features (such as vertex statistics) into a 3x3 covariance matrix (K). Because the feature vectors themselves are 3D, a 3×3 matrix may be advantageous. In at least one embodiment, K can be defined as shown in Equation 1:

[0041] K ij =E[(X i -E[X i ])(X j -E[X j ])], (1)

[0042] Where X i Let E represent the vector of all features at dimension i, and let operator E calculate the mean of its independent variable.

[0043] Additionally, the instantiation of this disclosure can also utilize higher-order statistics (such as skewness or kurtosis) instead of the described covariance descriptor. In at least one embodiment, SVD with one or more algorithms having minimum branching and floating-point operations can be used to determine the mesh signature. As shown in Equation 2, SVD decomposes K:

[0044] K=U∑V T (2)

[0045] Where U and V are orthogonal matrices, and Σ is a diagonal matrix whose diagonal terms are the singular values ​​of K arranged in descending order of magnitude. T is the centroid of the object. In at least one embodiment, singular values ​​are used as mesh signatures, which are used to evaluate the similarity between meshes. Furthermore, in at least one embodiment, the rigid rotation (R) of the mesh can be calculated (e.g., using orientation engine 308), as shown in Equation 3:

[0046] R = VU T (3)

[0047] During operation, each object 110 in the set of objects forming the scene can be evaluated, and each object 110 can be associated with a calculated signature. The signature can be determined when the scene is loaded into the scene and / or when the scene is selected for rendering. Additionally, in at least one embodiment, the signature can be calculated on demand or on-the-fly (e.g., based on camera view). Therefore, a large scene with millions of objects can only determine a subset of object signatures, which are then added to the signature as different parts of the scene are viewed. The deduplication engine 310 can be used to compare different signatures using the comparison engine 312 based on one or more evaluation parameters provided from the parameter data store 314. For example, the evaluation engine 316 can receive input from one or more tuning services 318 (such as interfaces to receive user instructions from the configuration data store 320 or one or more configuration files), which can be used to establish parameters to determine signatures and / or evaluate signatures. For example, threshold similarity or factors that can be selected for comparison can be tuned.

[0048] In the example using a grid as a representation, the distance between the first grid (a) and the second grid (b) is determined by calculating the corresponding signature (S) and then evaluating the distance between the vectors corresponding to the signatures. The distance (d) (e.g., the distance metric) can be determined using the squared vector normal form, as shown in Equation 4:

[0049] d square =‖S b -S a || 2 (4)

[0050] The distance calculation shown in Equation 4 is an example, and the scope of this disclosure is not limited to using only the squared vector normal form. As another non-limiting example, the cosine similarity function can be used. While the cosine similarity function has the opposite meaning to distance, it can return a value normalized to the interval [-1, 1], where a value of -1 is associated with opposites and a value of +1 is associated with equality. Similarity can be adapted to measure distance, as shown in Equation 5:

[0051]

[0052] At least one embodiment can use a user-defined threshold. This ensures that for the distance metric, the distance falls into d(S) a ,S b Within the range of )≤∈.

[0053] One or more embodiments can also be scaled for complex scenes involving a large number of diverse meshes. For example, a scene for an industrial setting (such as a warehouse) might contain tens of thousands of objects. In at least one embodiment, in addition to the signature (S), the system and method can also compute the centroid (T) and rigid rotation (R). The centroid can be associated with the center of the 3D bounding box of the object's vertical position, which can be a mesh. The centroid and rigid rotation can then be stored in an accelerated structure with a unique index for tracking the object, where queries for the signature are accelerated. In at least one embodiment, a KD-tree can be used as a data structure to allow queries of multidimensional vectors with logarithmic complexity. However, as discussed herein, while providing a KD-tree as a non-limiting example, other data structures can be used in place of or to complement the KD-tree.

[0054] As discussed herein, one or more embodiments can preprocess the scene prior to rendering, for example, by determining the signatures of objects and then performing deduplication to remove duplicate objects and represent them with a single stored object. For example, a signature can be selected from a set of signatures, and a KD-tree can be queried based on that signature (e.g., by using the Euclidean distance between the signature vector and the radius) to identify signatures considered to have threshold similarity. Given a matching signature from the query, the deduplication routine can remove each duplicate object mesh (or other data representation) from the scene and replace the removed object with an instance of a reference object at the corresponding 3D location and orientation. In this way, large scenes can be evaluated, deduplicated, and then rendered.

[0055] Figure 4 An example environment 400 that can be used according to embodiments of the present disclosure is illustrated. In this example, a machine learning system 402 may be included to identify features that can be used for deduplication in a large scene. For example, information stored in a training data store 406 may be used to train one or more neural networks 404 (e.g., models) to identify parameters 408 used for deduplication. In at least one embodiment, one or more neural networks 404 may be a classifier that receives information from a previous deduplication process and identifies relevant information such as identifying dimensions within a feature vector, identifying different parameters for different types of input files or objects, etc. One or more neural networks 404 may also refer to a visual model that analyzes a scene or a portion thereof and then uses user data to refine the identification of duplicate items within the scene. Parameters 408 may be stored in a parameter data store 410, which in some embodiments may be incorporated into or associated with a deduplication engine 310, as discussed herein.

[0056] In at least one embodiment, the signature engine 302 and / or the deduplication engine 310 may receive one or more parameters 408 identified using the machine learning system 402 to perform different deduplication operations. For example, parameters 408 may be used to facilitate the extraction of features from one or more objects associated with scene 104 for generating different signatures. In another embodiment, one or more parameters may be used to specify methods for determining duplicate objects, different settings for distance thresholds, etc.

[0057] The systems and methods disclosed herein may also include one or more user-tuned options. For example, tuning engine 318 may be used to receive user input to change one or more parameters, such as adjusting a duplication threshold, specifying specific parameters for generating signatures, providing settings to machine learning system 402, etc. Tuning engine 318 may also apply preset configurations that may be user-specific, such as preset configurations from user data storage 412. Users can specify specific salient features or parameters for their deduplication process, and tuning engine 318 may be used to identify, extract, and apply preferred settings. In at least one embodiment, type data storage 414 may store parameters for specific types of input data types, scenario domains, etc. For example, certain parameters that are consistently found within a specific file type can be identified. Similarly, certain types of scenarios (such as factory settings) may have different specified parameters. As an example, in a retail setting, color may be important for identifying different clothing items in a model, while in a warehouse, color may not be important if the boxes are different colors. Tuning engine 318 may be used to tune various settings to deduplicate based on different configurations.

[0058] During operation, scene 104 is provided to signature engine 302 to generate one or more signatures 416 associated with objects within scene 104. Signatures 416 can be stored in various data storage devices and / or used for training purposes, among other options. Signatures 416 can then be used by deduplication engine 310 to identify and remove duplicate object representations, thereby generating a deduplicated scene 418, which can be used to render scene 104 while reducing memory consumption and providing computational resources.

[0059] Figure 5AAn example process 500 for deduplicating representations of one or more objects is illustrated. It should be understood that, with respect to this process and others presented herein, unless specifically stated otherwise, additional, fewer, or alternative operations may be performed in a similar or alternative order or at least partially in parallel within the scope of various embodiments. In this example, a signature 502 of the object is calculated. The signature may correspond to one or more features associated with the object, such as features describing the object's geometry, visual appearance, etc. In at least one embodiment, features are extracted from one or more input characteristics provided with the object (such as a scene file including the object).

[0060] Various embodiments can compute a signature for each object within a given scene. For example, when a scene is provided to an interactive environment, various objects representing the scene can be identified, and a related signature for each object can be computed, then stored, for example, in one or more tables and / or as metadata associated with the object. In at least one embodiment, a signature among a plurality of signatures can be compared with other signatures 504 of a set of additional objects represented within the 3D scene. For example, a first signature can be selected from a list, and then the first signature can be compared with other signatures in the scene. An additional signature among the additional signatures of a corresponding additional object in the scene can be determined to be similar to the signature 506. For example, a similarity metric can be used to compare the similarity between different signatures. As discussed herein, similarity can correspond to a threshold similarity, thus allowing objects that are not perfectly similar to be identified as duplicate objects. In at least one embodiment, when duplicate objects are identified, one object and / or additional object associated with the signature can be replaced with a reference object 508. For example, the reference object can correspond to the signature. The reference object can then be used to render each of the corresponding similar objects within the scene.

[0061] Figure 5B An example process 520 for deduplicating object representations associated with a scene is illustrated. In this example, a covariance matrix 522 representing objects within a 3D scene is calculated. The covariance matrix may be based at least in part on one or more extracted features from metadata associated with the object. The covariance matrix can be used to calculate a signature 524 for the object. In at least one embodiment, this signature can then be used for comparison with other signatures of additional objects in the scene. In 526, the presence of similar objects can be determined based on the signature comparison. Thus, at least one embodiment can store and / or maintain a single representation 528 for both the object and the identified similar objects. The single representation can be maintained in memory for rendering both the similar objects and the object.

[0062] Figure 5CAn example process 540 for deduplicating a set of object representations is illustrated. In this example, signatures 542 of multiple objects associated with a 3D scene are determined. The signatures can be calculated using one or more features associated with the corresponding appearance of the objects. In at least one embodiment, an object 544 is selected from the set of objects. The object can be selected randomly, based on scene parameters, based on user input, and / or a combination thereof. The signature of the selected object can be compared with the signature of the selected object used for comparison 546. For example, a first object can be selected, and then the first object can be compared with each of the remaining objects in the set. In some embodiments, one or more factors can be used to eliminate comparisons of certain objects. For example, specific metadata or features can be used (such as based on geometric features) to quickly determine which objects are unlikely to be duplicates.

[0063] In this example, a similarity metric 548 is evaluated based on the comparison. The similarity metric can be associated with a threshold (e.g., based on a calculated distance between two vectors). If the similarity metric is satisfied, it can be determined that the object is similar to the compared object 550. Therefore, two separate representations may unnecessarily consume memory space. Thus, in at least one embodiment, both the selected object and the compared object can be associated with a shared object representation 552.

[0064] If the similarity metric is not satisfied, it can be determined whether additional objects remain among the multiple objects 554. If so, a new object can be selected to be compared with the selected object 556. This process can be iterated until it is determined that no object exists, then a stopping condition can be reached 558. In this way, objects in the scene can be evaluated and deduplicated before rendering, so that rendering can be performed faster and memory usage can be reduced.

[0065] All aspects of the various approaches proposed herein can be lightweight enough to be implemented in real time in various locations, such as on devices (including client devices including personal computers or game consoles). Such processing can be performed on or for content generated on the client device or received by the client device from external sources (such as streaming data or other content received via at least one network from a cloud server 620 or a third-party service 660, and other such options). In some instances, at least a portion of the processing, generation, synthesis, and / or determination of the content can be performed by one of these other devices, systems, or entities and then provided to the client device (or another such receiver) for presentation or another such purpose.

[0066] As an example, Figure 6An example network configuration 600 is illustrated that can be used to provide, generate, modify, encode, process, and / or transmit image data or other such content. In at least one embodiment, client device 602 can use components of control application 604 on client device 602 and data locally stored on the client device to generate or receive session data. In at least one embodiment, content application 624, implemented on server 620 (e.g., cloud server or edge server), can initiate a session associated with at least one client device 602, such as by utilizing a session manager and user data stored in user data storage 636, and can cause content manager 626 to determine content such as one or more digital assets (e.g., object representations) from asset repository 634. Content manager 626 can work with image compositing module 628 to generate or composite new objects, digital assets, or other such content to be provided for presentation via client device 602. In at least one embodiment, image compositing module 628 can use one or more neural network or machine learning models, which can be trained or updated using training module 632 or a system on or communicating with server 620. This may include training and / or using diffusion model 630 to generate content tiles, which can be used by image compositing module 628 to, for example, apply non-repeating textures to regions of an environment for which image or video data is rendered via client device 602. At least a portion of the generated content can be transmitted to client device 602 via download, streaming, or other such transmission channels using appropriate transmission manager 622. Encoders can be used to encode and / or compress at least some of this data before transmitting it to client device 602. In at least one embodiment, client device 602 receiving such content can provide it to a corresponding control application 604, which may also or alternatively include a graphical user interface 610, a content manager 612, and an image compositing or diffusion module 614 for providing, compositing, modifying, or using the content for rendering on or through client device 602 (or for other purposes). The decoder can also be used to decode data received over network 640 for presentation via client device 602, such as displaying images or video content via display 606, and presenting audio (such as sound and music) via at least one audio playback device 608 (such as a speaker or headphones).In at least one embodiment, at least some of the content may have been stored on, rendered on, or accessible by the client device 602, such that at least this portion of the content does not need to be transmitted over the network 640, for example, the content may have been previously downloaded or locally stored on a hard drive or optical disc. In at least one embodiment, a transmission mechanism such as data streaming may be used to transmit the content from the server 620 or user database 636 to the client device 602. In at least one embodiment, at least a portion of the content may be obtained, enhanced, and / or streamed from another source (such as a third-party service 660 or other client device 650), which may also include a content application 662 for generating, enhancing, or providing the content. In at least one embodiment, multiple computing devices or multiple processors within one or more computing devices, such as a combination of CPU and GPU, may be used to perform portions of the functionality.

[0067] In this example, these client devices can include any suitable computing device, such as desktop computers, laptops, set-top boxes, streaming devices, game consoles, smartphones, tablets, VR headsets, AR goggles, wearable computers, or smart TVs. Each client device can submit requests across at least one wired or wireless network, which can include the Internet, Ethernet, a local area network (LAN), or a cellular network, among other such options. In this example, these requests can be submitted to an address associated with a cloud provider that operates or controls one or more electronic resources within a cloud provider environment, such as a data center or server cluster. In at least one embodiment, the request can be received or processed by at least one edge server located at the network edge and outside at least one security layer associated with the cloud provider environment. In this way, latency can be reduced by enabling client devices to interact with servers in closer proximity, while also improving the security of resources within the cloud provider environment.

[0068] In at least one embodiment, such a system can be used to perform graphics rendering operations. In other embodiments, such a system can be used for other purposes, such as providing image or video content to test or validate autonomous machine applications, or for performing deep learning operations. In at least one embodiment, such a system can be implemented using edge devices, or can be combined with one or more virtual machines (VMs). In at least one embodiment, such a system can be implemented at least partially in a data center or at least partially using cloud computing resources.

[0069] In some examples, one or more machine learning models described herein (e.g., deep neural networks, language models, LLMs, VLMs, multimodal language models, perceptual models, tracking models, fusion models, transformer models, diffusion models, encoder-only models, decoder-only models, encoder-decoder models, neural rendering field (NERF) models, etc.) can be packaged into microservices (such as inference microservices (e.g., NVIDIA NIM)), which may include containers (e.g., operating system (OS) level virtualization packages) that may include an application programming interface (API) layer, a server layer, a runtime layer, and / or at least one model "engine". For example, an inference microservice may include the container itself and one or more models (e.g., weights and biases). In some cases, such as when one or more machine learning models are small enough (e.g., have a sufficiently few parameters), the one or more models may be included in the container itself. In other examples, such as when one or more models are large, the one or more models may be hosted / stored in the cloud (e.g., in a data center) and / or may be hosted on-premises and / or at the edge (e.g., on a local server or computing device, but outside the container). In such embodiments, one or more models may be accessible via one or more APIs (such as REST APIs). Therefore, and in some embodiments, one or more machine learning models described herein may be deployed as inference microservices to accelerate the deployment of one or more models on any cloud, data center, or edge computing system while ensuring data security. For example, an inference microservice may include one or more APIs, pre-configured containers for simplified deployment, an optimized inference engine (e.g., execution software built using standardized AI model deployments, such as NVIDIA's Triton Inference Server) and / or one or more APIs for high-performance deep learning inference, which may include inference runtime and model optimizations providing low latency and high throughput for production applications (such as NVIDIA's TensorRT), and / or enterprise management data for telemetry (e.g., including identity, metrics, health checks, and / or monitoring).

[0070] Reasoning and training logic

[0071] Figure 7A Inference and / or training logic 715 is shown for performing inference and / or training operations associated with one or more embodiments. The following is in conjunction with... Figure 7A and / or Figure 7B Provide details about reasoning and / or training logic 715.

[0072] In at least one embodiment, inference and / or training logic 715 may include, but is not limited to, code and / or data storage 701 for storing forward and / or output weights and / or input / output data, and / or other parameters configuring neurons or layers of a neural network trained for and / or used for inference in one or more embodiments. In at least one embodiment, training logic 715 may include or be coupled to code and / or data storage 701 for storing graph code or other software to control timing and / or sequence, wherein weight and / or other parameter information is loaded to configure logic, including integer and / or floating-point units (collectively, arithmetic logic units (ALUs)). In at least one embodiment, code (such as graph code) loads weight or other parameter information into the processor ALU based on the architecture of the neural network to which the code corresponds. In at least one embodiment, code and / or data storage 701 stores weight parameters and / or input / output data of each layer of a neural network trained or used in one or more embodiments during forward propagation of input / output data and / or weight parameters during training and / or inference using one or more embodiments. In at least one embodiment, any portion of the code and / or data storage 701 may be included within other on-chip or off-chip data storage, including the processor's L1, L2, or L3 cache or system memory.

[0073] In at least one embodiment, any portion of the code and / or data storage 701 may be internal or external to one or more processors or other hardware logic devices or circuits. In at least one embodiment, the code and / or data storage 701 may be a cache memory, dynamic random access memory (“DRAM”), static random access memory (“SRAM”), non-volatile memory (e.g., flash memory), or other storage. In at least one embodiment, the choice of whether the code and / or data storage 701 is internal or external to the processor, for example, or composed of DRAM, SRAM, flash memory, or some other storage type, may depend on the available on-chip or off-chip storage space, the latency requirements of the training and / or inference functions being performed, the batch size of the data used in the inference and / or training of the neural network, or some combination of these factors.

[0074] In at least one embodiment, inference and / or training logic 715 may include, but is not limited to, code and / or data storage 705 for storing backpropagation and / or output weights and / or input / output data corresponding to neurons or layers of a neural network trained and / or used for inference in one or more embodiments. In at least one embodiment, during training and / or inference using one or more embodiments, code and / or data storage 705 stores weight parameters and / or input / output data for each layer of a neural network trained or used in one or more embodiments during backpropagation of input / output data and / or weight parameters. In at least one embodiment, training logic 715 may include or be coupled to code and / or data storage 705 for storing graph code or other software to control timing and / or sequence, wherein weight and / or other parameter information is loaded to configure logic including integer and / or floating-point units (collectively, an arithmetic logic unit (ALU)). In at least one embodiment, code (such as graph code) loads weight or other parameter information into the processor ALU based on the architecture of the neural network to which the code corresponds. In at least one embodiment, any portion of the code and / or data storage 705 may be included together with other on-chip or off-chip data storage, including the processor's L1, L2, or L3 cache or system memory. In at least one embodiment, any portion of the code and / or data storage 705 may be internal or external to one or more processors or other hardware logic devices or circuits. In at least one embodiment, the code and / or data storage 705 may be cache memory, DRAM, SRAM, non-volatile memory (e.g., flash memory), or other storage. In at least one embodiment, the choice between the code and / or data storage 705 being internal or external to the processor, for example, whether it consists of DRAM, SRAM, flash memory, or some other type of storage, depends on whether the available storage is on-chip or off-chip, the latency requirements of the training and / or inference functions being performed, the data batch size used in the inference and / or training of the neural network, or some combination of these factors.

[0075] In at least one embodiment, code and / or data storage 701 and code and / or data storage 705 may be separate storage structures. In at least one embodiment, code and / or data storage 701 and code and / or data storage 705 may be the same storage structure. In at least one embodiment, code and / or data storage 701 and code and / or data storage 705 may be partially identical and partially separate storage structures. In at least one embodiment, any portion of code and / or data storage 701 and code and / or data storage 705 may be included with other on-chip or off-chip data storage, including the processor's L1, L2, or L3 cache or system memory.

[0076] In at least one embodiment, the inference and / or training logic 715 may include, but is not limited to, one or more arithmetic logic units (“ALUs”) 710 (including integer and / or floating-point units) for performing logical and / or mathematical operations at least in part based on or instructed by training and / or inference code (e.g., graph code), the results of which may produce activations (e.g., output values ​​from layers or neurons within a neural network) stored in activation storage 720, which are functions of input / output and / or weight parameter data stored in code and / or data storage 701 and / or code and / or data storage 705. In at least one embodiment, activation is activated in response to execution instructions or other code, and linear algebraic and / or matrix-based mathematical generation performed by ALU 710 is stored in activation storage 720, wherein weight values ​​stored in code and / or data storage 705 and / or code and / or data storage 701 are used as operands with other values, such as bias values, gradient information, momentum values, or other parameters or hyperparameters, and any or all of these can be stored in code and / or data storage 705 or code and / or data storage 701 or other on-chip or off-chip storage.

[0077] In at least one embodiment, one or more processors or other hardware logic devices or circuits include one or more ALUs 710, while in another embodiment, one or more ALUs 710 may be located outside the processor or other hardware logic device or the circuitry using them (e.g., a coprocessor). In at least one embodiment, one or more ALUs 710 may be included within an execution unit of a processor, or otherwise included in a group of ALUs accessible by the execution unit of the processor, which may be within the same processor or distributed among different processors of different types (e.g., a central processing unit, a graphics processing unit, a fixed-function unit, etc.). In at least one embodiment, code and / or data storage 701, code and / or data storage 705, and activation storage 720 may be on the same processor or other hardware logic device or circuitry, while in another embodiment, they may be on different processors or other hardware logic devices or circuitries, or in some combination of the same and different processors or other hardware logic devices or circuitries. In at least one embodiment, any portion of activation storage 720 may be included together with other on-chip or off-chip data storage, including the processor's L1, L2, or L3 cache or system memory. Furthermore, inference and / or training code may be stored together with other code accessible to the processor or other hardware logic or circuitry, and may be retrieved and / or processed using the processor’s fetch, decode, schedule, execute, exit, and / or other logic circuitry.

[0078] In at least one embodiment, the active memory 720 may be a cache memory, DRAM, SRAM, non-volatile memory (e.g., flash memory), or other memory. In at least one embodiment, the active memory 720 may be wholly or partially located inside or outside one or more processors or other logic circuits. In at least one embodiment, the choice of whether the active memory 720 is internal to or external to the processor may depend on the available on-chip or off-chip storage, the latency requirements for training and / or inference functions, the batch size of data used in inference and / or training the neural network, or some combination of these factors. For example, it may include DRAM, SRAM, flash memory, or other memory types. In at least one embodiment, Figure 7A The inference and / or training logic 715 shown can be used in conjunction with an application-specific integrated circuit (“ASIC”), such as those from Google. Processing unit, from Graphcore TM Inference processing unit (IPU) or from Intel Corp. (e.g., "LakeCrest") processor. In at least one embodiment, Figure 7A The inference and / or training logic 715 shown can be used in conjunction with central processing unit (“CPU”) hardware, graphics processing unit (“GPU”) hardware, or other hardware such as field programmable gate array (“FPGA”)

[0079] Figure 7B Inference and / or training logic 715 according to at least one or more embodiments is illustrated. In at least one embodiment, the inference and / or training logic 715 may include, but is not limited to, hardware logic, wherein computational resources are dedicated or otherwise uniquely used in conjunction with weight values ​​or other information corresponding to one or more layers of neurons within a neural network. In at least one embodiment, Figure 7B The inference and / or training logic 715 shown can be used in conjunction with an application-specific integrated circuit (ASIC), such as those from Google. Processing unit, from Graphcore TM Inference processing unit (IPU) or from Intel Corp. (e.g., "LakeCrest") processor. In at least one embodiment, Figure 7BThe inference and / or training logic 715 shown can be used in conjunction with central processing unit (CPU) hardware, graphics processing unit (GPU) hardware, or other hardware (e.g., field-programmable gate array (FPGA)). In at least one embodiment, the inference and / or training logic 715 includes, but is not limited to, code and / or data storage 701 and code and / or data storage 705, which can be used to store code (e.g., graph code), weight values, and / or other information, including bias values, gradient information, momentum values, and / or other parameter or hyperparameter information. Figure 7B In at least one embodiment shown, each of code and / or data storage 701 and code and / or data storage 705 is associated with dedicated computing resources (e.g., computing hardware 702 and computing hardware 706), respectively. In at least one embodiment, each of computing hardware 702 and computing hardware 706 includes one or more ALUs that perform mathematical functions (e.g., linear algebraic functions) only on the information stored in code and / or data storage 701 and code and / or data storage 705, respectively, and the results of the function execution are stored in activation storage 720.

[0080] In at least one embodiment, each of the code and / or data storage 701 and 705 and the corresponding computing hardware 702 and 706 corresponds to a different layer of the neural network, such that activation obtained from one “store / computation pair 701 / 702” of the code and / or data storage 701 and computing hardware 702 provides input as input to the next “store / computation pair 705 / 706” of the code and / or data storage 705 and computing hardware 706, in order to reflect the conceptual organization of the neural network. In at least one embodiment, each store / computation pair 701 / 702 and 705 / 706 may correspond to more than one neural network layer. In at least one embodiment, additional store / computation pairs (not shown) may be included in the inference and / or training logic 715 after or in parallel with the store / computation pairs 701 / 702 and 705 / 706.

[0081] Data Center

[0082] Figure 8 An example data center 800 that can be used with at least one embodiment is shown. In at least one embodiment, the data center 800 includes a data center infrastructure layer 810, a framework layer 820, a software layer 830, and an application layer 840.

[0083] In at least one embodiment, such as Figure 8As shown, the data center infrastructure layer 810 may include a resource coordinator 812, grouped computing resources 814, and node computing resources (“nodes CR”) 816(1)-816(N), where “N” represents any positive integer. In at least one embodiment, nodes CR 816(1)-816(N) may include, but are not limited to, any number of central processing units (“CPUs”) or other processors (including accelerators, field-programmable gate arrays (FPGAs), graphics processors, etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid-state drives or disk drives), network input / output (“NWI / O”) devices, network switches, virtual machines (“VMs”), power modules, and cooling modules, etc. In at least one embodiment, one or more nodes CR 816(1)-816(N) may be servers having one or more of the aforementioned computing resources.

[0084] In at least one embodiment, the grouped computing resources 814 may include individual groups (not shown) of node CRs housed in one or more racks, or a plurality of racks (also not shown) housed in data centers in various geographic locations. Individual groups of node CRs within the grouped computing resources 814 may include computing, networking, memory, or storage resources that can be configured or allocated to support groups of one or more workloads. In at least one embodiment, several node CRs, including CPUs or processors, may be grouped within one or more racks to provide computing resources to support one or more workloads. In at least one embodiment, the one or more racks may also include any number of power modules, cooling modules, and network switches, in any combination.

[0085] In at least one embodiment, resource coordinator 812 may configure or otherwise control one or more nodes CR816(1)-816(N) and / or grouped computing resources 814. In at least one embodiment, resource coordinator 812 may include a software design infrastructure (“SDI”) management entity for data center 800. In at least one embodiment, resource coordinator 812 may include hardware, software, or some combination thereof.

[0086] In at least one embodiment, such as Figure 8As shown, framework layer 820 includes a job scheduler 822, a configuration manager 824, a resource manager 826, and a distributed file system 828. In at least one embodiment, framework layer 820 may include a framework of software 832 supporting software layer 830 and / or one or more applications 842 supporting application layer 840. In at least one embodiment, software 832 or application 842 may respectively include web-based service software or applications, such as services or applications provided by Amazon Web Services, Google Cloud, and Microsoft Azure. In at least one embodiment, framework layer 820 may be, but is not limited to, a free and open-source software web application framework, such as Apache Spark™ (hereinafter referred to as "Spark") which can leverage distributed file system 828 for large-scale data processing (e.g., "big data"). In at least one embodiment, job scheduler 832 may include Spark drivers to facilitate the scheduling of workloads supported by the various layers of data center 800. In at least one embodiment, configuration manager 824 may be able to configure different layers, such as software layer 830 and framework layer 820 including Spark and distributed file system 828 for supporting large-scale data processing. In at least one embodiment, resource manager 826 is capable of managing cluster or group computing resources mapped to or allocated to support distributed file system 828 and job scheduler 822. In at least one embodiment, cluster or group computing resources may include group computing resources 814 on data center infrastructure layer 810. In at least one embodiment, resource manager 826 may coordinate with resource coordinator 812 to manage these mapped or allocated computing resources.

[0087] In at least one embodiment, the software 832 included in the software layer 830 may include software used by at least a portion of the nodes CR816(1)-816(N), the grouped computing resources 814, and / or the distributed file system 828 of the framework layer 820. One or more types of software may include, but are not limited to, Internet web page search software, email virus scanning software, database software, and streaming video content software.

[0088] In at least one embodiment, the application layer 840 may include one or more applications 842 that can be used by at least a portion of nodes CR816(1)-816(N), grouped computing resources 814, and / or the distributed file system 828 of the framework layer 820. The one or more types of applications may include, but are not limited to, any number of genomics applications, cognitive computing and machine learning applications, including training or inference software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.), or other machine learning applications used in conjunction with one or more embodiments.

[0089] In at least one embodiment, any of the configuration manager 824, resource manager 826, and resource coordinator 812 can implement any number and type of self-modification actions based on any amount and type of data acquired in any technically feasible manner. In at least one embodiment, self-modification actions can mitigate potentially poor configuration decisions by data center operators of data center 800 and can prevent underutilization and / or poor performance of the data center.

[0090] In at least one embodiment, data center 800 may include tools, services, software, or other resources to train one or more machine learning models or to use one or more machine learning models to predict or infer information according to one or more embodiments described herein. For example, in at least one embodiment, a machine learning model can be trained by calculating weight parameters based on a neural network architecture using the software and computing resources described above with respect to data center 800. In at least one embodiment, information can be inferred or predicted using trained machine learning models corresponding to one or more neural networks using the resources described above with respect to data center 800 by using weight parameters calculated through one or more training techniques described herein.

[0091] In at least one embodiment, the data center may use a CPU, application-specific integrated circuit (ASIC), GPU, FPGA, or other hardware to utilize the aforementioned resources to perform training and / or inference. Furthermore, one or more of the aforementioned software and / or hardware resources may be configured as a service to allow a user to train or perform information inference, such as image recognition, speech recognition, or other artificial intelligence services.

[0092] Inference and / or training logic 715 is used to perform inference and / or training operations associated with one or more embodiments. This document combines... Figure 7A and / or Figure 7BDetails are provided regarding the inference and / or training logic 715. In at least one embodiment, the inference and / or training logic 715 can be implemented in the system. Figure 8 Used in systems for reasoning or predicting operations based at least in part on weight parameters calculated using neural network training operations, neural network functions and / or architectures or neural network use cases described herein.

[0093] Such components can be used to generate a single, consistent, tokenized description of at least a portion of the physical environment, based in part on a set of observed and aligned map data.

[0094] Computer System

[0095] Figure 9 This is a block diagram illustrating an exemplary computer system 900 according to at least one embodiment. The exemplary computer system may be a system of interconnected devices and components, a system-on-a-chip (SOC), or some combination thereof formed with a processor, which may include an execution unit to execute instructions. In at least one embodiment, according to this disclosure, such as the embodiments described herein, the computer system 900 may include, but is not limited to, components such as processor 902, whose execution unit includes logic to execute algorithms for process data. In at least one embodiment, the computer system 900 may include a processor, such as one available from Intel Corporation of Santa Clara, California. Processor family, Xeon TM , XScale TM and / or StrongARM TM , Core TM or Nervana TM A microprocessor may be used, although other systems (including PCs, engineering workstations, set-top boxes, etc.) with other microprocessors may also be used. In at least one embodiment, computer system 900 may execute a version of the Windows operating system available from Microsoft Corporation of Redmond, Washington, although other operating systems (such as UNIX and Linux), embedded software, and / or graphical user interfaces may also be used.

[0096] The embodiments can be used in other devices, such as handheld devices and embedded applications. Some examples of handheld devices include cellular phones, Internet Protocol (IP) devices, digital cameras, personal digital assistants (“PDAs”), and handheld PCs. In at least one embodiment, the embedded application may include a microcontroller, a digital signal processor (“DSP”), a system-on-a-chip (SoC), a network computer (“NetPC”), a set-top box, a network hub, a wide area network (“WAN”) switch, or any other system that can execute one or more instructions according to at least one embodiment.

[0097] In at least one embodiment, the computer system 900 may include, but is not limited to, a processor 902, which may include, but is not limited to, one or more execution units 908, to perform machine learning model training and / or inference according to the techniques described herein. In at least one embodiment, the computer system 900 is a single-processor desktop or server system, but in another embodiment, the computer system 900 may be a multiprocessor system. In at least one embodiment, the processor 902 may include, but is not limited to, a Complex Instruction Set Computing (“CISC”) microprocessor, a Reduced Instruction Set Computing (“RISC”) microprocessor, a Very Long Instruction Word (“VLIW”) microprocessor, a processor implementing instruction set combination, or any other processor device, such as a digital signal processor. In at least one embodiment, the processor 902 may be coupled to a processor bus 910, which can transmit data signals between the processor 902 and other components in the computer system 900.

[0098] In at least one embodiment, processor 902 may include, but is not limited to, a Level 1 (“L1”) internal cache memory (“cache”) 904. In at least one embodiment, processor 902 may have a single internal cache or multiple levels of internal cache. In at least one embodiment, cache 904 may reside outside of processor 902. Depending on specific implementation and requirements, other embodiments may also include a combination of internal and external caches. In at least one embodiment, register file 906 may store different types of data in various registers, including but not limited to integer registers, floating-point registers, status registers, and instruction pointer registers.

[0099] In at least one embodiment, one or more execution units 908, including but not limited to logic performing integer and floating-point operations, are also located in processor 902. In at least one embodiment, processor 902 may also include a microcode (“ucode”) read-only memory (“ROM”) for storing microcode of certain macro instructions. In at least one embodiment, one or more execution units 908 may include logic for processing a packaged instruction set 909. In at least one embodiment, by including the packaged instruction set 909 in the instruction set of a general-purpose processor, along with the associated circuitry for executing the instructions, packaged data in processor 902 can be used to perform operations used by many multimedia applications. In one or more embodiments, many multimedia applications can be executed more quickly and efficiently by using the full width of processor data bus 910 to perform operations on the packaged data, which may eliminate the need to transfer smaller data units on processor data bus 910 to perform one or more operations on one data element at a time.

[0100] In at least one embodiment, one or more execution units 908 may also be used in microcontrollers, embedded processors, graphics devices, DSPs, and other types of logic circuits. In at least one embodiment, the computer system 900 may include, but is not limited to, memory 920. In at least one embodiment, memory 920 may be implemented as a dynamic random access memory (“DRAM”) device, a static random access memory (“SRAM”) device, a flash memory device, or other storage device. In at least one embodiment, memory 920 may store instructions 919 and / or data 921 represented by data signals that can be executed by processor 902.

[0101] In at least one embodiment, the system logic chip may be coupled to the processor bus 910 and the memory 920. In at least one embodiment, the system logic chip may include, but is not limited to, a memory controller hub (“MCH”) 916, and the processor 902 may communicate with the MCH 916 via the processor bus 910. In at least one embodiment, the MCH 916 may provide a high-bandwidth memory path 918 to the memory 920 for instruction and data storage, as well as for storage of graphics commands, data, and textures. In at least one embodiment, the MCH 916 may initiate data signals between the processor 902, the memory 920, and other components in the computer system 900, and bridge data signals between the processor bus 910, the memory 920, and the system I / O 922. In at least one embodiment, the system logic chip may provide a graphics port for coupling to a graphics controller. In at least one embodiment, the MCH 916 may be coupled to the memory 920 via the high-bandwidth memory path 918, and the graphics / video card 912 may be coupled to the MCH 916 via an Accelerated Graphics Port (“AGP”) interconnect 914.

[0102] In at least one embodiment, computer system 900 may use system I / O 922, which is a proprietary hub interface bus, to couple MCH 916 to I / O controller hub (“ICH”) 930. In at least one embodiment, ICH 930 may provide direct connectivity to certain I / O devices via a local I / O bus. In at least one embodiment, the local I / O bus may include, but is not limited to, a high-speed I / O bus for connecting peripheral devices to memory 920, chipset, and processor 902. Examples may include, but are not limited to, an audio controller 929, a firmware hub (“Flash BIOS”) 928, a wireless transceiver 926, a data storage 924, a conventional I / O controller 923 including user input and one or more keyboard interfaces 925, a serial expansion port 927 (e.g., a Universal Serial Bus (USB) port), and a network controller 934. Data storage 924 may include a hard disk drive, floppy disk drive, CD-ROM device, flash memory device, or other mass storage device.

[0103] In at least one embodiment, Figure 9 A system including interconnected hardware devices or "chips" is shown, while in other embodiments, Figure 9An exemplary system-on-a-chip (SoC) may be illustrated. In at least one embodiment, the device may be interconnected with a proprietary interconnect, a standardized interconnect (e.g., PCIe), or some combination thereof. In at least one embodiment, one or more components of the computer system 900 are interconnected using a compute fast link (CXL) interconnect.

[0104] The inference and / or training logic 715 is used to perform inference and / or training operations related to one or more embodiments. (The following is in conjunction with...) Figure 7A and / or Figure 7B Details are provided regarding the inference and / or training logic 715. In at least one embodiment, the inference and / or training logic 715 can be... Figure 9 Used in systems for reasoning or predicting operations based at least in part on weight parameters calculated using neural network training operations, neural network functions and / or architectures or neural network use cases described herein.

[0105] Such components can be used to generate a single, consistent, tokenized description of at least a portion of the physical environment, based in part on a set of observed and aligned map data.

[0106] Figure 10 This is a block diagram illustrating an electronic device 1000 using a processor 1010 according to at least one embodiment. In at least one embodiment, the electronic device 1000 may be, for example, but not limited to, a laptop computer, tower server, rack server, blade server, laptop computer, desktop computer, tablet computer, mobile device, telephone, embedded computer, or any other suitable electronic device.

[0107] In at least one embodiment, the electronic device 1000 may, but is not limited to, a processor 1010 communicatively coupled to any suitable number or type of components, peripherals, modules, or devices. In at least one embodiment, the processor 1010 uses a bus or interface coupling, such as an I2C bus, a system management bus (“SMBus”), a low pin count (LPC) bus, a serial peripheral interface (“SPI”), a high-definition audio (“HDA”) bus, a serial advanced technology accessory (“SATA”) bus, a universal serial bus (“USB”) (versions 1, 2, and 3), or a universal asynchronous receiver / transmitter (“UART”) bus. In at least one embodiment, Figure 10 An electronic device 1000 is shown, which includes interconnected hardware devices or "chips," while in other embodiments, Figure 10 An exemplary system-on-a-chip (SoC) can be illustrated. In at least one embodiment, Figure 10 The device shown can be interconnected with proprietary interconnects, standardized interconnects (e.g., PCIe), or some combination thereof. In at least one embodiment, Figure 10One or more components are interconnected using Computational Fast Link (CXL) interconnects.

[0108] In at least one embodiment, Figure 10 It may include a display 1024, a touch screen 1025, a touchpad 1030, a near field communication unit (“NFC”) 1045, a sensor hub 1040, a thermal sensor 1046, a fast chipset (“EC”) 1035, a trusted platform module (“TPM”) 1038, a BIOS / firmware / flash (“BIOS, FW Flash”) 1022, a DSP 1060, a drive 1020 (e.g., a solid-state drive (“SSD”) or a hard disk drive (“HDD”)), a wireless local area network unit (“WLAN”) 1050, a Bluetooth unit 1052, a wireless wide area network unit (“WWAN”) 1056, a global positioning system (GPS) 1055, a camera (“USB 3.0 camera”) 1054 (e.g., a USB 3.0 camera) and / or a low-power double data rate (“LPDDR”) memory unit (“LPDDR3”) 1015 implemented in, for example, the LPDDR3 standard. These components can each be implemented in any suitable way.

[0109] In at least one embodiment, other components may be communicatively coupled to processor 1010 via the components described above. In at least one embodiment, accelerometer 1041, ambient light sensor (“ALS”) 1042, compass 1043, and gyroscope 1044 may be communicatively coupled to sensor hub 1040. In at least one embodiment, thermal sensor 1039, fan 1037, keyboard 1036, and touchpad 1030 may be communicatively coupled to EC 1035. In at least one embodiment, speaker 1063, earphone 1064, and microphone (“mic”) 1065 may be communicatively coupled to audio unit (“audio codec and Class D amplifier”) 1062, which in turn may be communicatively coupled to DSP 1060. In at least one embodiment, audio unit 1062 may include, for example, but not limited to, audio encoder / decoder (“codec”) and Class D amplifier. In at least one embodiment, SIM card (“SIM”) 1057 may be communicatively coupled to WWAN unit 1056. In at least one embodiment, components such as WLAN unit 1050, Bluetooth unit 1052, and WWAN unit 1056 can be implemented as next-generation form factor (NGFF).

[0110] Inference and / or training logic 715 is used to perform inference and / or training operations associated with one or more embodiments. The following is combined with... Figure 7A and / or Figure 7BDetails are provided regarding the inference and / or training logic 715. In at least one embodiment, the inference and / or training logic 715 can be... Figure 10 The system is used to infer or predict operations based at least in part on weight parameters calculated using neural network training operations, neural network functions and / or architectures or neural network use cases described herein.

[0111] Such components can be used to generate a single, consistent, tokenized description of at least a portion of the physical environment, based in part on a set of observed and aligned map data.

[0112] Figure 11 This is a block diagram of a processing system according to at least one embodiment. In at least one embodiment, the processing system 1100 includes one or more processors 1102 and one or more graphics processors 1108, and may be a single-processor desktop system, a multi-processor workstation system, or a server system having a large number of processors 1102 or processor cores 1107. In at least one embodiment, the processing system 1100 is a processing platform incorporated within a system-on-a-chip (SoC) integrated circuit for use in mobile, handheld, or embedded devices.

[0113] In at least one embodiment, the processing system 1100 may include or be integrated into a server-based gaming platform, including a game console, mobile game console, handheld game console, or online game console, which are game and media consoles. In at least one embodiment, the processing system 1100 is a mobile phone, smartphone, tablet computing device, or mobile internet device. In at least one embodiment, the processing system 1100 may also include components coupled to or integrated into a wearable device, such as a smartwatch, smart glasses, augmented reality, or virtual reality device. In at least one embodiment, the processing system 1100 is a television or set-top box device having one or more processors 1102 and a graphical interface generated by one or more graphics processors 1108.

[0114] In at least one embodiment, each of the one or more processors 1102 includes one or more processor cores 1107 for processing instructions that, when executed, perform operations against the system and user software. In at least one embodiment, each of the one or more processor cores 1107 is configured to process a particular set of instructions 1109. In at least one embodiment, the instruction set 1109 may facilitate Complex Instruction Set Computing (CISC), Reduced Instruction Set Computing (RISC), or computation via Very Long Instruction Word (VLIW). In at least one embodiment, the one or more processor cores 1107 may each process a different set of instructions 1109, which may include instructions that facilitate the emulation of other instruction sets. In at least one embodiment, the one or more processor cores 1107 may also include other processing devices, such as digital signal processors (DSPs).

[0115] In at least one embodiment, one or more processors 1102 include a cache memory (“cache”) 1104. In at least one embodiment, one or more processors 1102 may have a single internal cache or multiple levels of internal caches. In at least one embodiment, the cache 1104 is shared among the various components of one or more processors 1102. In at least one embodiment, one or more processors 1102 also use an external cache (e.g., a Level 3 (L3) cache or a Last Level Cache (LLC)) (not shown), which can be shared among one or more processor cores 1107 using known cache coherence techniques. In at least one embodiment, one or more processors 1102 further include a register file 1106, and the processor may include different types of registers (e.g., integer registers, floating-point registers, status registers, and instruction pointer registers) for storing different types of data. In at least one embodiment, the register file 1106 may include general-purpose registers or other registers.

[0116] In at least one embodiment, one or more processors 1102 are coupled to one or more interface buses 1110 to transmit communication signals, such as address, data, or control signals, between the processors 1102 and other components in the processing system 1100. In at least one embodiment, the one or more interface buses 1110 may be a processor bus, such as a version of the Direct Media Interface (DMI) bus. In at least one embodiment, the one or more interface buses 1110 are not limited to the DMI bus and may include one or more peripheral component interconnect buses (e.g., PCI, PCI Express), memory buses, or other types of interface buses. In at least one embodiment, the processor 1102 includes an integrated memory controller 1116 and a platform controller hub 1130. In at least one embodiment, the memory controller 1116 facilitates communication between the memory device 1120 and other components of the processing system 1100, while the platform controller hub (PCH) 1130 provides connectivity to I / O devices via a local I / O bus.

[0117] In at least one embodiment, memory device 1120 may be a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, a flash memory device, a phase-change memory device, or a device with suitable performance for use as processor memory. In at least one embodiment, memory device 1120 may be used as system memory of processing system 1100 to store data 1122 and instructions 1121 for use when one or more processors 1102 execute an application or process. In at least one embodiment, memory controller 1116 is also coupled to an optional external graphics processor 1112, which may communicate with one or more graphics processors 1108 of one or more processors 1102 to perform graphics and media operations. In at least one embodiment, display device 1111 may be connected to processor 1102. In at least one embodiment, display device 1111 may include one or more internal display devices, such as in mobile electronic devices or laptop devices, or external display devices connected via a display interface (e.g., DisplayPort). In at least one embodiment, the display device 1111 may include a head-mounted display (HMD), such as a stereoscopic display device for virtual reality (VR) or augmented reality (AR) applications.

[0118] In at least one embodiment, the platform controller hub 1130 enables peripheral devices to connect to the memory device 1120 and one or more processors 1102 via a high-speed I / O bus. In at least one embodiment, the I / O peripheral devices include, but are not limited to, an audio controller 1146, a network controller 1134, a firmware interface 1128, a wireless transceiver 1126, a touch sensor 1125, and a data storage device 1124 (e.g., a hard disk drive, flash memory, etc.). In at least one embodiment, the data storage device 1124 may be connected via a storage interface (e.g., SATA) or via a peripheral bus, such as a peripheral component interconnect bus (e.g., PCI, PCIe). In at least one embodiment, the touch sensor 1125 may include a touchscreen sensor, a pressure sensor, or a fingerprint sensor. In at least one embodiment, the wireless transceiver 1126 may be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile network transceiver, such as a 3G, 4G, or LTE transceiver. In at least one embodiment, the firmware interface 1128 enables communication with the system firmware and may be, for example, a Unified Extensible Firmware Interface (UEFI). In at least one embodiment, network controller 1134 may enable network connectivity to a wired network. In at least one embodiment, a high-performance network controller (not shown) is coupled to one or more interface buses 1110. In at least one embodiment, audio controller 1146 is a multi-channel high-definition audio controller. In at least one embodiment, processing system 1100 includes an optional legacy I / O controller 1140 for coupling legacy (e.g., Personal System 2 (PS / 2)) devices to system 1100. In at least one embodiment, platform controller hub 1130 may also be connected to one or more Universal Serial Bus (USB) controllers 1142 that connect input devices, such as a keyboard and mouse combination 1143, a camera 1144, or other USB input devices.

[0119] In at least one embodiment, instances of the memory controller 1116 and platform controller hub 1130 may be integrated into a discrete external graphics processor, such as external graphics processor 1112. In at least one embodiment, the platform controller hub 1130 and / or the memory controller 1116 may be external to one or more processors 1102. For example, in at least one embodiment, the processing system 1100 may include an external memory controller 1116 and a platform controller hub 1130, which may be configured as a memory controller hub and a peripheral controller hub in a system chipset communicating with the processor 1102.

[0120] Inference and / or training logic 715 is used to perform inference and / or training operations associated with one or more embodiments. The following is combined with... Figure 7Aand / or Figure 7B Details regarding the inference and / or training logic 715 are provided. In at least one embodiment, some or all of the inference and / or training logic 715 may be incorporated into the processing system 1100. For example, in at least one embodiment, the training and / or inference techniques described herein may use one or more ALUs embodied in a graphics processor. Furthermore, in at least one embodiment, the inference and / or training operations described herein may use, in addition to Figure 7A and / or Figure 7B The logic is performed using logic other than that shown. In at least one embodiment, the weight parameters may be stored in on-chip or off-chip memory and / or registers (shown or not shown), which configure the ALU of the graphics processor to execute one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

[0121] Such components can be used to generate a single, consistent, tokenized description of at least a portion of the physical environment, based in part on a set of observed and aligned map data.

[0122] Figure 12 This is a block diagram of a processor 1200 having one or more processor cores 1202A-1202N, an integrated memory controller 1214, and an integrated graphics processor 1208 according to at least one embodiment. In at least one embodiment, the processor 1200 may include additional cores, up to and including additional cores 1202N indicated by dashed boxes. In at least one embodiment, each of the one or more processor cores 1202A-1202N includes one or more internal cache units 1204A-1204N. In at least one embodiment, each processor core may also access one or more shared cache units 1206.

[0123] In at least one embodiment, one or more internal cache units 1204A-1204N and one or more shared cache units 1206 represent a cache memory hierarchy within the processor 1200. In at least one embodiment, one or more cache memory units 1204A-1204N may include at least one level of instruction and data cache within each processor core and one or more levels of cache in a shared intermediate cache, such as Level 2 (L2), Level 3 (L3), Level 4 (L4), or other levels of cache, wherein the highest level of cache preceding external memory is classified as LLC. In at least one embodiment, cache coherence logic maintains coherence between the various cache units 1206 and 1204A-1204N.

[0124] In at least one embodiment, the processor 1200 may further include a set of one or more bus controller units 1216 and a system agent core 1210. In at least one embodiment, one or more bus controller units 1216 manage a set of peripheral buses, such as one or more PCI or PCIe buses. In at least one embodiment, the system agent core 1210 provides management functions for various processor components. In at least one embodiment, the system agent core 1210 includes one or more integrated memory controllers 1214 to manage access to various external memory devices (not shown).

[0125] In at least one embodiment, one or more processor cores 1202A-1202N include support for concurrent multithreading. In at least one embodiment, system agent core 1210 includes components for coordinating and operating one or more processor cores 1202A-1202N during multithreaded processing. In at least one embodiment, system agent core 1210 may additionally include a power control unit (PCU) including logic and components for regulating one or more power states of one or more processor cores 1202A-1202N and graphics processor 1208.

[0126] In at least one embodiment, processor 1200 further includes a graphics processor 1208 for performing graph processing operations. In at least one embodiment, graphics processor 1208 is coupled to one or more shared cache units 1206 and a system proxy core 1210 including one or more integrated memory controllers 1214. In at least one embodiment, system proxy core 1210 further includes a display controller 1211 for driving graphics processor outputs to one or more coupled displays. In at least one embodiment, display controller 1211 may also be a separate module coupled to graphics processor 1208 via at least one interconnect, or it may be integrated within graphics processor 1208.

[0127] In at least one embodiment, the ring-based interconnect unit 1212 is used to couple internal components of the processor 1200. In at least one embodiment, alternative interconnect units, such as point-to-point interconnects, switched interconnects, or other technologies, may be used. In at least one embodiment, the graphics processor 1208 is coupled to the ring-based interconnect unit 1212 via I / O link 1213.

[0128] In at least one embodiment, I / O link 1213 represents at least one of a variety of I / O interconnects, including packaged I / O interconnects that facilitate communication between various processor components and high-performance embedded memory module 1218 (e.g., eDRAM module). In at least one embodiment, each of one or more processor cores 1202A-1202N and graphics processor 1208 uses embedded memory module 1218 as a shared last-level cache.

[0129] In at least one embodiment, one or more processor cores 1202A-1202N are homogeneous cores executing a common instruction set architecture. In at least one embodiment, one or more processor cores 1202A-1202N are heterogeneous in terms of instruction set architecture (ISA), with one or more processor cores 1202A-1202N executing a common instruction set, while one or more other cores of one or more processor cores 1202A-1202N execute a subset of the common instruction set or a different instruction set. In at least one embodiment, in terms of microarchitecture, one or more processor cores 1202A-1202N are heterogeneous, with one or more cores having relatively high power consumption coupled to one or more power cores having lower power consumption. In at least one embodiment, processor 1200 may be implemented on one or more chips or implemented as a SoC integrated circuit.

[0130] Inference and / or training logic 715 is used to perform inference and / or training operations associated with one or more embodiments. The following is combined with... Figure 7A and / or Figure 7B Details regarding the inference and / or training logic 715 are provided. In at least one embodiment, some or all of the inference and / or training logic 715 may be incorporated into the processor 1200. For example, in at least one embodiment, the training and / or inference techniques described herein may use one or more ALUs embodied in... Figure 12 The graphics processor 1208, one or more processor cores 1202A-1202N, or other components are used. Furthermore, in at least one embodiment, the inference and / or training operations described herein can use, except... Figure 7A and / or Figure 7B The logic is performed using logic other than that shown. In at least one embodiment, the weight parameters may be stored in on-chip or off-chip memory and / or registers (shown or not shown), which configure the ALU of the graphics processor 1200 to execute one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

[0131] Such components can be used to generate a single, consistent, tokenized description of at least a portion of the physical environment, based in part on a set of observed and aligned map data.

[0132] Virtualization computing platform

[0133] Figure 13 This is an example data flow diagram of process 1300 for generating and deploying an image processing and inference pipeline according to at least one embodiment. In at least one embodiment, process 1300 may be deployed for use with imaging devices, processing devices, and / or other device types at one or more facilities 1302. Process 1300 may be executed within training system 1304 and / or deployment system 1306. In at least one embodiment, training system 1304 may be used to train, deploy, and implement machine learning models (e.g., neural networks, object detection algorithms, computer vision algorithms, etc.) for use in deployment system 1306. In at least one embodiment, deployment system 1306 may be configured to offload processing and computing resources in a distributed computing environment to reduce the infrastructure requirements of one or more facilities 1302. In at least one embodiment, one or more applications in the pipeline may use or invoke services of deployment system 1306 (e.g., inference, visualization, computation, AI, etc.) during application execution.

[0134] In at least one embodiment, some applications used in the advanced processing and inference pipeline may use machine learning models or other AI to perform one or more processing steps. In at least one embodiment, machine learning models may be trained at one or more facilities 1302 using data 1308 (e.g., imaging data) generated at one or more facilities 1302 (and stored on one or more Picture Archiving and Communication System (PACS) servers at one or more facilities 1302), imaging or sequencing data 1308 from another or more facilities 1302, or a combination thereof. In at least one embodiment, training system 1304 may be used to provide applications, services, and / or other resources to generate a deployable machine learning model for the work of deploying system 1306.

[0135] In at least one embodiment, the model registry 1324 may be supported by an object storage system that supports version control and object metadata. In at least one embodiment, the object storage may be accessed from within a cloud platform via, for example, a cloud storage-compatible application programming interface (API). In at least one embodiment, machine learning models within the model registry 1324 may be uploaded, listed, modified, or deleted by the developer or partner of a system interacting with the API. In at least one embodiment, the API may provide access to methods that allow users with appropriate credentials to associate models with applications, enabling the models to be executed as part of the containerized instantiation of the application.

[0136] In at least one embodiment, training system 1304 ( Figure 13 This can include situations where one or more facilities 1302 are training their own machine learning models, or have existing machine learning models that need optimization or updating. In at least one embodiment, imaging data 1308 generated by imaging devices, sequencing devices, and / or other types of devices can be received. In at least one embodiment, once the imaging data 1308 is received, AI-assisted annotation 1310 can be used to help generate annotations corresponding to the imaging data 1308 for use as ground-based data for machine learning models. In at least one embodiment, AI-assisted annotation 1310 can include one or more machine learning models (e.g., convolutional neural networks (CNNs)) that can be trained to generate annotations corresponding to certain types of imaging data 1308 (e.g., from certain devices). In at least one embodiment, AI-assisted annotation 1310 can then be used directly, or it can be adjusted or fine-tuned using annotation tools to generate ground-based data. In at least one embodiment, AI-assisted annotation 1310, labeled data 1312, or a combination thereof can be used as ground-based data for training machine learning models. In at least one embodiment, the trained machine learning model may be referred to as (one or more) output models 1316 and may be used by deployment system 1306 as described herein.

[0137] In at least one embodiment, the training pipeline may include situations where one or more facilities 1302 require a machine learning model to perform one or more processing tasks for deploying one or more applications of system 1306, but one or more facilities 1302 may not currently have such a machine learning model (or may not have an efficient or effective model optimized for this purpose). In at least one embodiment, an existing machine learning model may be selected from model registry 1324. In at least one embodiment, model registry 1324 may include machine learning models trained to perform various inference tasks on imaging data. In at least one embodiment, the machine learning model in model registry 1324 may be trained on imaging data from different facilities (e.g., facilities located at a distance) rather than one or more facilities 1302. In at least one embodiment, the machine learning model may have already been trained on imaging data from one location, two locations, or any number of locations. In at least one embodiment, when training on imaging data from a particular location, training may be performed at that location, or at least in a manner that protects the confidentiality of the imaging data or restricts the off-site transfer of the imaging data. In at least one embodiment, once a model has been trained or partially trained at one location, a machine learning model can be added to model registry 1324. In at least one embodiment, the machine learning model can then be retrained or updated at any number of other facilities, and the retrained or updated model can be used in model registry 1324. In at least one embodiment, a machine learning model (referred to as output model 1316) can then be selected from model registry 1324, and can be used in deployment system 1306 to perform one or more processing tasks for one or more applications of the deployment system.

[0138] In at least one embodiment, the scenario may include one or more facilities 1302 that require machine learning models to perform one or more processing tasks for deploying one or more applications in system 1306, but one or more facilities 1302 may not currently have such a machine learning model (or may not have an optimized, efficient, or effective model). In at least one embodiment, the machine learning model selected from the model registry 1324 may not be fine-tuned or optimized for the imaging data 1308 generated at one or more facilities 1302 due to population variability, robustness, anomalous diversity of the training data, and / or other problems with the training data used to train the machine learning model. In at least one embodiment, AI-assisted annotation 1310 may be used to help generate annotations corresponding to the imaging data 1308 for use as ground-based data for training or updating the machine learning model. In at least one embodiment, labeled clinical data 1312 may be used as ground-based data for training the machine learning model. In at least one embodiment, retraining or updating the machine learning model may be referred to as model training 1314. In at least one embodiment, model training 1314 (e.g., AI-assisted annotation 1310, labeled data 1312, or a combination thereof) can be used as ground-based real-world data to retrain or update the machine learning model. In at least one embodiment, the trained machine learning model can be referred to as output model 1316 and can be used by deployment system 1306, as described herein.

[0139] In at least one embodiment, deployment system 1306 may include software 1318, service 1320, hardware 1322, and / or other components, features, and functions. In at least one embodiment, deployment system 1306 may include a software "stack" such that software 1318 can be built on top of service 1320 and can be used to perform some or all of the processing tasks, and service 1320 and software 1318 can be built on top of hardware 1322 and use hardware 1322 to perform the deployment system's processing, storage, and / or other computational tasks. In at least one embodiment, software 1318 may include any number of different containers, each of which can perform an instantiation of an application. In at least one embodiment, each application can perform one or more processing tasks (e.g., inference, object detection, feature detection, segmentation, image enhancement, calibration, etc.) in a high-level processing and inference pipeline. In at least one embodiment, in addition to receiving and configuring imaging data for use by each container and / or for use by one or more facilities 1302 after processing through the pipeline, advanced processing and inference pipelines (e.g., to convert output back to available data types) can also be defined based on the selection of different containers desired or required for processing imaging data 1308. In at least one embodiment, a combination of containers within software 1318 (e.g., constituting a pipeline) may be referred to as a virtual instrument (as described in more detail herein), and the virtual instrument may utilize service 1320 and hardware 1322 to perform some or all of the processing tasks of an application instantiated within the container.

[0140] In at least one embodiment, the data processing pipeline may receive input data (e.g., imaging data 1308) in a specific format in response to an inference request (e.g., a request from a user of deployment system 1306). In at least one embodiment, the input data may represent one or more images, videos, and / or other data representations generated by one or more imaging devices. In at least one embodiment, the data may be preprocessed as part of the data processing pipeline to prepare it for processing by one or more applications. In at least one embodiment, post-processing may be performed on the output of one or more inference tasks or other processing tasks of the pipeline to prepare output data for the next application and / or to prepare output data for user transmission and / or use (e.g., as a response to an inference request). In at least one embodiment, the inference task may be performed by one or more machine learning models, such as trained or deployed neural networks, which may include the output model 1316 of training system 1304.

[0141] In at least one embodiment, the tasks of the data processing pipeline can be encapsulated in containers, each container representing a discrete, fully functional instantiation of an application and a virtualized computing environment capable of referencing a machine learning model. In at least one embodiment, containers or applications can be published to a private (e.g., limited access) area of ​​a container registry (described in more detail herein), and trained or deployed models can be stored in a model registry 1324 and associated with one or more applications. In at least one embodiment, an image of an application (e.g., a container image) can be used in the container registry, and once a user selects an image from the container registry for deployment in the pipeline, that image can be used to generate containers for instantiation of the application for use by the user's system.

[0142] In at least one embodiment, a developer (e.g., a software developer, clinician, physician, etc.) can develop, publish, and store an application (e.g., as a container) for performing image processing and / or inference on provided data. In at least one embodiment, a software development kit (SDK) associated with the system can be used to perform development, publication, and / or storage (e.g., to ensure that the developed application and / or container conforms to or is compatible with the system). In at least one embodiment, the developed application can be tested locally using the SDK (e.g., at a first facility, testing data from a first facility), the SDK serving as a system (e.g.,...). Figure 12 The processor 1200 in the container may support at least some services 1320. In at least one embodiment, since a DICOM object may contain one to hundreds of images or other data types, and due to variations in the data, the developer may be responsible for managing (e.g., setting up constructs for preprocessing and building into the application, etc.) the extraction and preparation of incoming data. In at least one embodiment, once verified by process 1300 (e.g., for accuracy), the application becomes available in the container registry for user selection and / or implementation to perform one or more processing tasks on data at the user's facility (e.g., a second facility).

[0143] In at least one embodiment, the developer can then share the application or container over a network for the system (e.g., Figure 13The process 1300 involves user access and use. In at least one embodiment, a completed and validated application or container may be stored in a container registry, and the associated machine learning model may be stored in a model registry 1324. In at least one embodiment, a requesting entity (which provides an inference or image processing request) may browse the container registry and / or model registry 1324 to obtain applications, containers, datasets, machine learning models, etc., select the desired combination of elements to include in the data processing pipeline, and submit an image processing request. In at least one embodiment, the request may include input data necessary to perform the request (and, in some examples, patient-related data), and / or may include a selection of the application and / or machine learning model to be performed when the request is processed. In at least one embodiment, the request may then be passed to one or more components of deployment system 1306 (e.g., the cloud) to perform processing in the data processing pipeline. In at least one embodiment, the processing performed by deployment system 1306 may include referencing elements (e.g., applications, containers, models, etc.) selected from the container registry and / or model registry 1324. In at least one embodiment, once the results are generated through the pipeline, the results can be returned to the user for reference (e.g., for viewing in a suite of viewing applications executed locally, on a local workstation, or on a terminal).

[0144] In at least one embodiment, service 1320 may be utilized to assist in processing or executing applications or containers in the pipeline. In at least one embodiment, service 1320 may include computing services, artificial intelligence (AI) services, visualization services, and / or other service types. In at least one embodiment, service 1320 may provide functionality common to one or more applications in software 1318, thus abstracting functionality into services that can be invoked or utilized by applications. In at least one embodiment, the functionality provided by service 1320 can operate dynamically and more efficiently, while also scaling well by allowing applications to process data in parallel (e.g., using a parallel computing platform). In at least one embodiment, it is not required that each application sharing the same functionality provided by service 1320 must have a corresponding instance of service 1320; rather, service 1320 can be shared between and among various applications. In at least one embodiment, as a non-limiting example, service 1320 may include an inference server or engine that can be used to perform detection or segmentation tasks. In at least one embodiment, a model training service may be included, which can provide machine learning model training and / or retraining capabilities. In at least one embodiment, a data augmentation service may be further included, which can provide GPU-accelerated data extraction, resizing, scaling, and / or other enhancements (e.g., DICOM, RIS, CIS, REST-compliant, RPC, raw, etc.). In at least one embodiment, a visualization service may be used, which can add image rendering effects (e.g., ray tracing, rasterization, denoising, sharpening, etc.) to add realism to two-dimensional (2D) and / or three-dimensional (3D) models. In at least one embodiment, a virtual instrument service may be included, which provides beamforming, segmentation, inference, imaging, and / or support for other applications within the virtual instrument pipeline.

[0145] In at least one embodiment, where service 1320 includes an AI service (e.g., an inference service), as part of application execution, one or more machine learning models can be executed by invoking (e.g., as an API call) the inference service (e.g., an inference server) to execute one or more machine learning models or their processing. In at least one embodiment, where another application includes one or more machine learning models for a segmentation task, the application can invoke the inference service to execute the machine learning models for performing one or more processing operations associated with the segmentation task. In at least one embodiment, software 1318 implementing advanced processing and inference pipelines, including a segmentation application and an anomaly detection application, can be pipelined because each application can invoke the same inference service to execute one or more inference tasks.

[0146] In at least one embodiment, hardware 1322 may include a GPU, CPU, graphics card, AI / deep learning system (e.g., an AI supercomputer, such as NVIDIA's DGX), cloud platform, or a combination thereof. In at least one embodiment, different types of hardware 1322 may be used to provide efficient, specially built support for software 1318 and services 1320 in deployment system 1306. In at least one embodiment, GPU processing can be used to perform local processing (e.g., at one or more facilities 1302) within the AI / deep learning system, in the cloud system, and / or other processing components of deployment system 1306 to improve the efficiency, accuracy, and performance of image processing and generation. In at least one embodiment, as a non-limiting example, software 1318 and / or services 1320 may be optimized for GPU processing in relation to deep learning, machine learning, and / or high-performance computing. In at least one embodiment, at least some of the computing environment of deployment system 1306 and / or training system 1304 may be executed in a data center, one or more supercomputers, or high-performance computing systems with GPU-optimized software (e.g., a hardware and software combination of an NVIDIA DGX system). In at least one embodiment, as described herein, hardware 1322 may include any number of GPUs that can be invoked to perform data processing in parallel. In at least one embodiment, the cloud platform may also include GPU-optimized execution for deep learning tasks, GPU processing for machine learning tasks, or other computational tasks. In at least one embodiment, an AI / deep learning supercomputer and / or GPU-optimized software (e.g., as provided on NVIDIA's DGX systems) may be used as a hardware abstraction and scaling platform to execute the cloud platform (e.g., NVIDIA's NGC). In at least one embodiment, the cloud platform may integrate application container cluster systems or coordination systems (e.g., Kubernetes) across multiple GPUs to achieve seamless scaling and load balancing.

[0147] Figure 14 This is a system diagram of an example system 1400 for generating and deploying an imaging deployment pipeline according to at least one embodiment. In at least one embodiment, system 1400 can be used to implement Figure 13 The process 1300 and / or other processes include advanced processing and inference pipelines. In at least one embodiment, system 1400 may include training system 1304 and deployment system 1306. In at least one embodiment, training system 1304 and deployment system 1306 may be implemented using software 1318, service 1320 and / or hardware 1322, as described herein.

[0148] In at least one embodiment, system 1400 (e.g., training system 1304 and / or deployment system 1306) may be implemented in a cloud computing environment (e.g., using cloud 1426). In at least one embodiment, system 1400 may be implemented locally (in relation to a healthcare facility) or as a combination of cloud computing resources and local computing resources. In at least one embodiment, access to the API in cloud 1426 may be restricted to authorized users by establishing security measures or protocols. In at least one embodiment, the security protocol may include a network token, which may be signed by an authentication service (e.g., AuthN, AuthZ, Gluecon, etc.) and may carry appropriate authorization. In at least one embodiment, the API of the virtual instrument (described herein) or other instances of system 1400 may be restricted to a set of public IPs that have been audited or authorized for interaction.

[0149] In at least one embodiment, the various components of system 1400 may communicate with each other using any of a variety of different network types, including but not limited to local area networks (LANs) and / or wide area networks (WANs) via wired and / or wireless communication protocols. In at least one embodiment, communication between facilities and components of system 1400 (e.g., for sending inference requests, for receiving the results of inference requests, etc.) may be transmitted via one or more data buses, wireless data protocols (Wi-Fi), wired data protocols (e.g., Ethernet), etc.

[0150] In at least one embodiment, similar to the description herein. Figure 13 As described, training system 1304 can execute one or more training pipelines 1404. In at least one embodiment, wherein deployment system 1306 uses one or more machine learning models in one or more deployment pipelines 1410, one or more training pipelines 1404 can be used to train or retrain one or more (e.g., pre-trained) models, and / or implement one or more pre-trained models 1406 (e.g., without retraining or updating). In at least one embodiment, one or more output models 1316 can be generated as a result of one or more training pipelines 1404. In at least one embodiment, one or more training pipelines 1404 can include any number of processing steps, such as, but not limited to, transformation or adaptation of imaging data (or other input data). In at least one embodiment, different training pipelines 1404 can be used for different machine learning models used by deployment system 1306. In at least one embodiment, similar to the description of... Figure 13 One or more training pipelines 1404 described in the first example can be used for the first machine learning model, similar to the one described above. Figure 13One or more training pipelines 1404 described in the second example can be used for a second machine learning model, similar to the one described above. Figure 13 One or more training pipelines 1404 of the described third example can be used for a third machine learning model. In at least one embodiment, any combination of tasks within the training system 1304 can be used according to the requirements of each corresponding machine learning model. In at least one embodiment, one or more machine learning models may have already been trained and are ready for deployment, so the training system 1304 may not perform any processing on the machine learning models, and one or more machine learning models may be implemented by the deployment system 1306.

[0151] In at least one embodiment, depending on the implementation or embodiment, the output model 1316 and / or one or more pre-trained models 1406 may include any type of machine learning model. In at least one embodiment, and not limited thereto, the machine learning model used by system 1400 may include models using linear regression, logistic regression, decision trees, support vector machines (SVM), Naive Bayes, k-nearest neighbors (Knn), k-means clustering, random forests, dimensionality reduction algorithms, gradient boosting algorithms, neural networks (e.g., autoencoders, convolutions, recursion, perceptrons, long / short-term memory (LSTM), Hopfield, Boltzmann, deep belief, deconvolution, generative adversarial, liquid state machines, etc.), and / or other types of machine learning models.

[0152] In at least one embodiment, one or more training pipelines 1404 may include AI-assisted annotations, as described herein regarding at least Figure 14More specifically, in at least one embodiment, labeled clinical data 1312 can be generated using any number of techniques (e.g., conventional annotation). In at least one embodiment, in some examples, labels or other annotations can be generated by drawing programs (e.g., annotation programs), computer-aided design (CAD) programs, tagging programs, another type of application suitable for generating annotations or labels for ground reality, and / or can be hand-drawn. In at least one embodiment, ground reality data can be synthetically generated (e.g., generated from computer models or renderings), realistically generated (e.g., designed and generated from real-world data), machine-generated (e.g., extracting features from data using feature analysis and learning, and then generating labels), human-annotated (e.g., taggers or annotation experts, defining the placement of labels), and / or combinations thereof. In at least one embodiment, for each instance of imaging data 1308 (or other data types used by machine learning models), there may be corresponding ground reality data generated by training system 1304. In at least one embodiment, AI-assisted annotation 1310 can be performed as part of deploying one or more pipelines 1410; supplementing or replacing the AI-assisted annotation 1310 included in training one or more pipelines 1404. In at least one embodiment, system 1400 may include a multi-layer platform, which may include a software layer (e.g., software 1318) of a diagnostic application (or other application type) capable of performing one or more medical imaging and diagnostic functions. In at least one embodiment, system 1400 may be communicatively coupled (e.g., via an encrypted link) to a network of PACS servers in one or more facilities. In at least one embodiment, system 1400 may be configured to access and reference data from PACS servers to perform operations such as training machine learning models, deploying machine learning models, image processing, inference, and / or other operations.

[0153] In at least one embodiment, the software layer may be implemented as a secure, encrypted, and / or certified API through which an application or container can be invoked (e.g., called) from an external environment (e.g., one or more facilities 1302). In at least one embodiment, the application may then invoke or execute one or more services 1320 to perform computational, AI, or visualization tasks associated with their respective applications, and the software 1318 and / or services 1320 may utilize the hardware 1322 to perform processing tasks efficiently and effectively. In at least one embodiment, a pair of DICOM adapters 1402A, 1402B may be used to send or receive communications to or from the training system 1304 and deployment system 1306.

[0154] In at least one embodiment, deployment system 1306 may execute one or more deployment pipelines 1410. In at least one embodiment, one or more deployment pipelines 1410 may include any number of applications, which may be sequential, non-sequential, or otherwise applied to imaging data (and / or other data types) – including AI-assisted annotation, the imaging data being generated by imaging devices, sequencing devices, genomics devices, etc., as described above. In at least one embodiment, as described herein, deployment pipeline 1410 for an individual device may be referred to as a virtual instrument for the device (e.g., a virtual ultrasound instrument, a virtual CT scanner, a virtual sequencing instrument, etc.). In at least one embodiment, for a single device, there may be more than one deployment pipeline 1410, depending on the desired information from the data generated from the device. In at least one embodiment, one or more first deployment pipelines 1410 may exist if it is desired to detect an anomaly from an MRI machine, and one or more second deployment pipelines 1410 may exist if it is desired to perform image enhancement from the output of the MRI machine.

[0155] In at least one embodiment, the image generation application may include processing tasks that utilize machine learning models. In at least one embodiment, a user may wish to use their own machine learning model or select a machine learning model from the model registry 1324. In at least one embodiment, a user may implement their own machine learning model or select a machine learning model to be included in the application performing the processing tasks. In at least one embodiment, the application may be optional and customizable, and by defining the application's construction, the deployment and implementation of the application for a specific user is presented as a more seamless user experience. In at least one embodiment, by leveraging other features of system 1400 (e.g., service 1320 and hardware 1322), one or more deployment pipelines 1410 may be more user-friendly, provide easier integration, and produce more accurate, efficient, and timely results.

[0156] In at least one embodiment, deployment system 1306 may include a user interface (“UI”) 1414 (e.g., a graphical user interface, a web interface, etc.) that can be used to select applications to be included in deployment pipeline 1410, deploy applications, modify or change applications or their parameters or configurations, use and interact with deployment pipeline 1410 during setup and / or deployment, and / or otherwise interact with deployment system 1306. In at least one embodiment, although not shown with respect to training system 1304, UI 1414 (or different user interfaces) can be used to select models to be used in deployment system 1306, to select models to be trained or retrained in training system 1304, and / or to otherwise interact with training system 1304.

[0157] In at least one embodiment, in addition to the application coordination system 1428, a pipeline manager 1412 may be used to manage interactions between applications or containers deployed through the pipeline 1410 and services 1320 and / or hardware 1322. In at least one embodiment, the pipeline manager 1412 may be configured to facilitate interactions from application to application, from application to service 1320, and / or from application or service to hardware 1322. In at least one embodiment, although shown as included in software 1318, this is not intended to be limiting, and in some examples, the pipeline manager 1412 may be included in service 1320. In at least one embodiment, the application coordination system 1428 (e.g., Kubernetes, DOCKER, etc.) may include a container coordination system that can group applications into containers as logical units for coordination, management, scaling, and deployment. In at least one embodiment, by associating applications from the deployment pipeline 1410 (e.g., rebuilding applications, splitting applications, etc.) with individual containers, each application can execute in a self-contained environment (e.g., at the kernel level) to improve speed and efficiency.

[0158] In at least one embodiment, each application and / or container (or its image) can be developed, modified, and deployed independently (e.g., a first user or developer can develop, modify, and deploy a first application, and a second user or developer can develop, modify, and deploy a second application separate from the first user or developer). This allows focus on the tasks of a single application and / or container without being hindered by the tasks of another application or container. In at least one embodiment, the pipeline manager 1412 and the application coordination system 1428 can facilitate communication and collaboration between different containers or applications. In at least one embodiment, the application coordination system 1428 and / or the pipeline manager 1412 can facilitate communication and resource sharing between and within each application or container, provided that the expected inputs and / or outputs of each container or application are known to the system (e.g., based on the construction of the application or container). In at least one embodiment, since one or more applications or containers in the deployment pipeline 1410 can share the same services and resources, the application coordination system 1428 can coordinate, load balance, and determine the sharing of services or resources between and within the various applications or containers. In at least one embodiment, the scheduler can be used to track the resource requirements of applications or containers, the current or planned use of these resources, and resource availability. Therefore, in at least one embodiment, the scheduler can allocate resources to different applications and distribute resources between and among applications, taking into account the system's needs and availability. In some examples, the scheduler (and / or other components of the application coordination system 1428) can determine resource availability and distribution based on constraints imposed on the system (e.g., user constraints), such as Quality of Service (QoS), the urgency of data output (e.g., to determine whether to perform real-time processing or delayed processing), etc.

[0159] In at least one embodiment, service 1320, utilized and shared by applications or containers in deployment system 1306, may include computing service 1416, AI service 1418, visualization service 1420, and / or other service types. In at least one embodiment, an application may invoke (e.g., execute) one or more services 1320 to perform processing operations for the application. In at least one embodiment, an application may utilize computing service 1416 to perform supercomputing or other high-performance computing (HPC) tasks. In at least one embodiment, one or more computing services 1416 may be utilized to perform parallel processing (e.g., using parallel computing platform 1430) to process data substantially simultaneously through one or more applications and / or one or more tasks of a single application. In at least one embodiment, parallel computing platform 1430 (e.g., NVIDIA's CUDA) may implement general-purpose computing on a GPU (GPGPU) (e.g., GPU / graphics 1422). In at least one embodiment, the software layer of parallel computing platform 1430 may provide access to the GPU's virtual instruction set and parallel computing elements to execute computing kernels. In at least one embodiment, the parallel computing platform 1430 may include memory, and in some embodiments, memory may be shared between and within multiple containers, and / or between and within different processing tasks within a single container. In at least one embodiment, inter-process communication (IPC) calls may be generated for multiple containers and / or multiple processes within containers to enable the use of the same data (e.g., multiple different stages of one or more applications processing the same information) from a shared memory segment of the parallel computing platform 1430. In at least one embodiment, instead of copying data and moving it to different locations in memory (e.g., read / write operations), the same data in the same memory location can be used for any number of processing tasks (e.g., at the same time, at different times, etc.). In at least one embodiment, this information about the new location of the data can be stored and shared between applications because the resulting data from processing is used to generate new data. In at least one embodiment, the location of the data, and the location of the updated or modified data, may be part of the definition of how the payload in the container is understood.

[0160] In at least one embodiment, one or more AI services 1418 may be used to perform inference services for executing machine learning models associated with the application (e.g., tasks for performing one or more processing tasks of the application). In at least one embodiment, one or more AI services 1418 may utilize AI system 1424 to execute machine learning models (e.g., neural networks such as CNNs) for segmentation, reconstruction, object detection, feature detection, classification, and / or other inference tasks. In at least one embodiment, the application deploying pipeline 1410 may use one or more output models 1316 of the self-training system 1304 and / or other models of the application to perform inference on imaging data. In at least one embodiment, two or more examples of using the application coordination system 1428 (e.g., a scheduler) for inference may be available. In at least one embodiment, a first category may include high-priority / low-latency paths that can implement higher service level protocols, such as for performing inference on urgent requests in emergency situations or for radiologists during diagnostic procedures. In at least one embodiment, a second category may include standard priority paths that can be used for requests that may not be urgent or for situations where analysis can be performed at a later time. In at least one embodiment, the application coordination system 1428 may allocate resources (e.g., services 1320 and / or hardware 1322) based on priority paths for different inference tasks of the AI ​​service 1418.

[0161] In at least one embodiment, shared memory may be installed in one or more AI services 1418 of system 1400. In at least one embodiment, shared memory may operate as a cache (or other storage device type) and may be used to process inference requests from applications. In at least one embodiment, when an inference request is submitted, a set of API instances of deployment system 1306 may receive the request and may select one or more instances (e.g., for best fit, for load balancing, etc.) to process the request. In at least one embodiment, to process the request, the request may be fed into a database, and if not already in the cache, a machine learning model may be located from model registry 1324. A verification step may ensure that an appropriate machine learning model is loaded into the cache (e.g., shared memory), and / or a copy of the model may be saved to the cache. In at least one embodiment, if the application is not already running or there are not enough instances of the application, a scheduler (e.g., the scheduler of pipeline manager 1412) may be used to start the application referenced in the request. In at least one embodiment, if an inference server has not yet been started to execute the model, an inference server may be started. Any number of inference servers may be started for each model. In at least one embodiment, in a pull model that clusters inference servers, the model can be cached whenever load balancing is favorable. In at least one embodiment, the inference servers can be statically loaded into the corresponding distributed servers.

[0162] In at least one embodiment, an inference server running in a container can be used to perform inference. In at least one embodiment, an instance of the inference server can be associated with a model (and optionally multiple versions of the model). In at least one embodiment, if an instance of the inference server does not exist when a request to perform inference on the model is received, a new instance can be loaded. In at least one embodiment, when the inference server is started, a model can be passed to the inference server, allowing the same container to be used to serve different models, as long as the inference server runs as different instances.

[0163] In at least one embodiment, during application execution, an inference request for a given application can be received, and a container (e.g., an instance of a hosted inference server) can be loaded (if not already loaded), and a launcher can be invoked. In at least one embodiment, preprocessing logic within the container can (e.g., using a CPU and / or GPU) load, decode, and / or perform any additional preprocessing on the incoming data. In at least one embodiment, once the data is ready for inference, the container can infer the data as needed. In at least one embodiment, this can include a single inference call for an image (e.g., a hand X-ray) or can request inference for hundreds of images (e.g., a chest CT scan). In at least one embodiment, the application can summarize the results before completion, which may include, but is not limited to, a single confidence score, pixel-level segmentation, voxel-level segmentation, generating visualizations, or generating text to summarize the results. In at least one embodiment, different priorities can be assigned to different models or applications. For example, some models may have a real-time (TAT less than 1 minute) priority, while other models may have a lower priority (e.g., TAT less than 10 minutes). In at least one embodiment, model execution time can be measured from the requesting agency or entity, and may include cooperative network traversal time and inference service execution time.

[0164] In at least one embodiment, the transfer of requests between service 1320 and the inference application can be hidden behind a software development kit (SDK) and robust transfer can be provided via queues. In at least one embodiment, requests are placed in queues via an API for individual application / tenant ID combinations, and the SDK pulls requests from the queues and provides them to the application. In at least one embodiment, the name of the queue can be provided in the environment where the SDK picks up the queue. In at least one embodiment, asynchronous communication via queues may be useful because it allows any instance of the application to pick up work when it becomes available. Results can be sent back via queues to ensure no data loss. In at least one embodiment, queues can also provide the ability to partition work, as the highest priority work can go into a queue connected to a majority of instances of the application, while the lowest priority work can go into a queue connected to a single instance that processes tasks in the order they are received. In at least one embodiment, the application can run on a GPU-accelerated instance generated in cloud 1426, and the inference service can perform inference on the GPU.

[0165] In at least one embodiment, visualization service 1420 can be used to generate visualizations for viewing the output of application and / or deployment pipeline 1410. In at least one embodiment, visualization service 1420 can utilize GPU / graphics 1422 to generate visualizations. In at least one embodiment, visualization service 1420 can implement rendering effects such as ray tracing to generate higher quality visualizations. In at least one embodiment, visualizations can include, but are not limited to, 2D image rendering, 3D volume rendering, 3D volume reconstruction, 2D tomographic slicing, virtual reality display, augmented reality display, etc. In at least one embodiment, a virtualized environment can be used to generate virtual interactive displays or environments (e.g., virtual environments) for system users (e.g., doctors, nurses, radiologists, etc.) to interact with. In at least one embodiment, visualization service 1420 can include an internal visualizer, cinematic and / or other rendering or image processing capabilities or functions (e.g., ray tracing, rasterization, internal optics, etc.).

[0166] In at least one embodiment, hardware 1322 may include GPU / graphics 1422, AI system 1424, cloud 1426, and / or any other hardware for performing training system 1304 and / or deployment system 1306. In at least one embodiment, GPU / graphics 1422 (e.g., NVIDIA's TESLA and / or QUADRO GPUs) may include any number of GPUs that can be used to perform processing tasks for any feature or function of computing service 1416, AI service 1418, visualization service 1420, other services, and / or software 1318. For example, for AI service 1418, GPU / graphics 1422 may be used to perform preprocessing on imaging data (or other data types used by machine learning models), postprocessing on the output of machine learning models, and / or inference (e.g., to execute machine learning models). In at least one embodiment, cloud 1426, AI system 1424, and / or other components of system 1400 may use GPU / graphics 1422. In at least one embodiment, cloud 1426 may include a GPU-optimized platform for deep learning tasks. In at least one embodiment, AI system 1424 may use a GPU, and one or more AI systems 1424 may be used to perform cloud 1426 (or at least part of a task for deep learning or inference). Similarly, although hardware 1322 is shown as a discrete component, this is not intended to be limiting, and any component of hardware 1322 may be combined with or utilized by any other component of hardware 1322.

[0167] In at least one embodiment, AI system 1424 may include a specially built computing system (e.g., a supercomputer or HPC) configured for inference, deep learning, machine learning, and / or other artificial intelligence tasks. In at least one embodiment, in addition to CPU, RAM, memory, and / or other components, features, or functions, AI system 1424 (e.g., NVIDIA's DGX) may also include software (e.g., a software stack) that can be used to perform GPU-optimized tasks using multiple GPUs / graphics 1422. In at least one embodiment, one or more AI systems 1424 may be implemented in a cloud 1426 (e.g., in a data center) to perform some or all of the AI-based processing tasks of system 1400.

[0168] In at least one embodiment, cloud 1426 may include GPU-accelerated infrastructure (e.g., NVIDIA's NGC) that can provide a GPU-optimized platform for performing processing tasks of system 1400. In at least one embodiment, cloud 1426 may include AI system 1424 for performing one or more AI-based tasks of system 1400 (e.g., as a hardware abstraction and scaling platform). In at least one embodiment, cloud 1426 may be integrated with application coordination system 1428 utilizing multiple GPUs to achieve seamless scaling and load balancing between and within applications and services 1320. In at least one embodiment, as described herein, cloud 1426 may be responsible for performing at least some of the services 1320 of system 1400, including one or more computing services 1416, one or more AI services 1418, and / or one or more visualization services 1420. In at least one embodiment, cloud 1426 may perform large and small batch inference (e.g., perform NVIDIA's TENSORRT), provide accelerated parallel computing APIs and platform 1430 (e.g., NVIDIA's CUDA), perform application coordination system 1428 (e.g., KUBERNETES), provide graphics rendering APIs and platform (e.g., for ray tracing, 2D graphics, 3D graphics and / or other rendering techniques to produce higher quality cinematic effects), and / or provide other functionalities for system 1400.

[0169] Figure 15A A data flow diagram of a process 1500 for training, retraining, or updating a machine learning model according to at least one embodiment is shown. In at least one embodiment, a non-limiting example can be used. Figure 14The system 1400 executes the process 1500. In at least one embodiment, the process 1500 may utilize services and / or hardware as described herein. In at least one embodiment, the refined model 1512 generated by the process 1500 may be executed by a deployment system for one or more containerized applications in the deployment pipeline 1410.

[0170] In at least one embodiment, model training 1514 may include retraining or updating the initial model 1504 (e.g., a pre-trained model) using new training data (e.g., new input data, such as customer dataset 1506, and / or new ground reality data associated with the input data). In at least one embodiment, to retrain or update the initial model 1504, the output or loss layer of the initial model 1504 may be reset, deleted, and / or replaced with an updated or new output or loss layer. In at least one embodiment, the initial model 1504 may have previously fine-tuned parameters (e.g., weights and / or biases) retained from previous training, so training or retraining 1514 may not require as much time or processing as training the model from scratch. In at least one embodiment, during model training 1514, when generating predictions on the new customer dataset 1506 by resetting or replacing the output or loss layer of the initial model 1504, the parameters of the new dataset may be updated and readjusted based on the loss calculation associated with the accuracy of the output or loss layer.

[0171] In at least one embodiment, one or more pre-trained models 1406 may be stored in a data store or registry. In at least one embodiment, one or more pre-trained models 1406 may have been trained, at least partially, at one or more facilities other than the facility performing process 1500. In at least one embodiment, to protect the privacy and rights of patients, subjects, or customers at different facilities, one or more pre-trained models 1406 may have been trained locally using locally generated customer or patient data. In at least one embodiment, one or more pre-trained models 1406 may be trained using cloud and / or other hardware, but confidential, privacy-protected patient data may not be transferred to, used by, or accessed by any component of the cloud (or other non-local hardware). In at least one embodiment, if one or more pre-trained models 1406 are trained using patient data from more than one facility, one or more pre-trained models 1406 may have been trained individually for each facility before training on patient or customer data from another facility. In at least one embodiment, such as when customer or patient data has been published for privacy reasons (e.g., by abandonment, for experimental purposes, etc.), or where customer or patient data is included in a public dataset, customer or patient data from any number of facilities can be used to train one or more pre-trained models 1406 locally and / or externally, such as in a data center or other cloud computing infrastructure.

[0172] In at least one embodiment, when selecting an application for use in the deployment pipeline, the user may also select a machine learning model for a specific application. In at least one embodiment, the user may not have a model available, so the user may select one or more pre-trained models 1406 to use with the application. In at least one embodiment, one or more pre-trained models 1406 may not be optimized to generate accurate results on the user facility's customer dataset 1506 (e.g., based on patient diversity, demographics, type of medical imaging equipment used, etc.). In at least one embodiment, one or more pre-trained models 1406 may be updated, retrained, and / or fine-tuned for use at various facilities before being deployed into the deployment pipeline for use with one or more applications.

[0173] In at least one embodiment, a user may select a pre-trained model to update, retrain, and / or fine-tune, and the pre-trained model 1406 may be referred to as the initial model 1504 of the training system in process 1500. In at least one embodiment, a client dataset 1506 (e.g., imaging data, genomic data, sequencing data, or other data types generated by equipment at the facility) may be used to perform model training (which may include, but is not limited to, transfer learning) on ​​the initial model 1504 to generate a refined model 1512. In at least one embodiment, ground-based data corresponding to the client dataset 1506 may be generated by the training system 1304. In at least one embodiment, ground-based data may be generated at the facility, at least in part, by clinicians, scientists, physicians, or practitioners.

[0174] In at least one embodiment, AI-assisted annotation 1310 may be used in some examples to generate ground reality data. In at least one embodiment, AI-assisted annotation 1310 (e.g., implemented using an AI-assisted annotation SDK) may leverage machine learning models (e.g., neural networks) to generate suggested or predicted ground reality data for a customer dataset. In at least one embodiment, a user may use the annotation tool within a user interface (graphical user interface (GUI)) on a computing device.

[0175] In at least one embodiment, user 1510 can interact with the GUI via computing device 1508 to edit or fine-tune annotations or automatic annotations. In at least one embodiment, polygon editing features can be used to move the vertices of a polygon to more precise or fine-tuned positions.

[0176] In at least one embodiment, once the customer dataset 1506 has associated ground-based data, the ground-based data (e.g., from AI-assisted annotations, manual labeling, etc.) can be used to generate a refined model 1512 during model training. In at least one embodiment, the customer dataset 1506 can be applied to the initial model 1504 an arbitrary number of times, and the ground-based data can be used to update the parameters of the initial model 1504 until an acceptable level of accuracy is achieved for the refined model 1512. In at least one embodiment, once the refined model 1512 is generated, it can be deployed in one or more deployment pipelines at the facility to perform one or more processing tasks related to medical imaging data.

[0177] In at least one embodiment, the refined model 1512 can be uploaded to a pre-trained model registry for selection by another facility. In at least one embodiment, this process can be performed at any number of facilities, allowing the refined model 1512 to be further refined an arbitrary number of times on a new dataset to generate a more general model.

[0178] Figure 15B This is an example illustration of a client-server architecture 1532 for enhancing an annotation tool using a pre-trained annotation model 1542, according to at least one embodiment. In at least one embodiment, an AI-assisted annotation tool 1536 may be instantiated based on the client-server architecture 1532. In at least one embodiment, the AI-assisted annotation tool 1536 in an imaging application can assist radiologists, for example, in identifying organs and abnormalities. In at least one embodiment, the imaging application may include software tools, as a non-limiting example, that help user 1510 identify several extreme points on a specific organ of interest in a raw image 1534 (e.g., in a 3D MRI or CT scan) and receive automatic annotation results for all 2D slices of that specific organ. In at least one embodiment, the results may be stored in a data store as training data 1538 and used as (e.g., but not limited to) ground-based data for training. In at least one embodiment, when computing device 1508 sends extreme points for AI-assisted annotation, for example, a deep learning model may receive this data as input and return inference results for segmenting organs or abnormalities. In at least one embodiment, a pre-instantiated annotation tool (e.g., Figure 15B The AI-assisted annotation tool 1536 can be enhanced by making API calls (e.g., API call 1544) to a server (such as annotation assistant server 1540), which may include a set of pre-trained models 1542 stored, for example, in an annotation model registry. In at least one embodiment, the annotation model registry may store pre-trained models 1542 (e.g., machine learning models, such as deep learning models) that have been pre-trained to perform AI-assisted annotation 1310 on specific organs or anomalies. In at least one embodiment, these models can be further updated using a training pipeline. In at least one embodiment, the pre-installed annotation tool can be improved over time as new labeled data is added.

[0179] Various embodiments may be described by the following terms:

[0180] 1. At least one processor, said processor comprising:

[0181] One or more circuits are used for:

[0182] Calculate the covariance matrix of the objects represented in a 3D scene;

[0183] The signature of the object is calculated using the covariance matrix;

[0184] Determine similar objects based on the signature of the object; and

[0185] A single representation is maintained in memory for rendering both the similar object and the object.

[0186] 2. The at least one processor as claimed in claim 1, wherein the one or more circuits are further configured to:

[0187] Determine the rigid rotation of the object.

[0188] 3. The at least one processor as claimed in claim 1, wherein the covariance matrix is ​​calculated based on one or more three-dimensional 3D vectors of the object.

[0189] 4. The at least one processor as claimed in claim 1, wherein the one or more circuits are further configured to:

[0190] The signature is stored in the data representation;

[0191] Determine the distance between the similar object and the object; and

[0192] Based on the distance, it is determined that the similar object and the object are similar objects.

[0193] 5. The at least one processor as claimed in claim 1, wherein the distance is determined by at least one of the square vector normal form or the cosine similarity function.

[0194] 6. The at least one processor of claim 1, wherein at least a portion of the covariance matrix is ​​based on at least one of the following: object vertex position, object vertex normal, color, or vertex texture coordinates.

[0195] 7. The at least one processor as claimed in claim 1, wherein the one or more circuits are further configured to:

[0196] Based on multiple scenarios including multiple objects, train one or more neural networks to identify one or more features representing the objects;

[0197] Select one or more of the aforementioned characteristics; and

[0198] This allows the use of one or more features to determine the covariance matrix.

[0199] 8. The at least one processor as claimed in claim 1, wherein the processor is included in at least one of the following:

[0200] A system used to perform simulation operations;

[0201] A system used to perform simulations to test or validate autonomous machine applications;

[0202] Systems used to perform digital twin operations;

[0203] A system for performing optical transmission simulation;

[0204] A system used for rendering graphics output;

[0205] A system used to perform deep learning operations;

[0206] Systems implemented using edge devices;

[0207] Systems used to generate or present virtual reality (VR) content;

[0208] A system for generating or presenting augmented reality (AR) content;

[0209] A system for generating or presenting mixed reality (MR) content;

[0210] A system containing one or more virtual machines (VMs);

[0211] A system used to perform operations for conversational AI applications;

[0212] A system used to perform operations for generative AI applications;

[0213] A system for performing operations using a language model;

[0214] A system for performing one or more generative content operations using a large language model (LLM);

[0215] A system for performing one or more content generation operations using a Visual Language Model (VLM);

[0216] A system for performing one or more content generation operations using a multimodal language model;

[0217] A system that is at least partially implemented in a data center;

[0218] A system for performing hardware tests using simulation;

[0219] Systems for generating synthetic data;

[0220] A collaborative content creation platform for 3D assets; or

[0221] A system that utilizes cloud computing resources at least in part.

[0222] 9. A computer-implemented method, the computer-implemented method comprising:

[0223] The signature of the object is calculated based on the input characteristics corresponding to the appearance of the object represented in the 3D scene.

[0224] Compare the signature with multiple additional signatures of a set of additional objects represented in the three-dimensional 3D scene;

[0225] Based on at least one similarity metric, determine that one of the additional signatures of the corresponding additional object is similar to the signature; and

[0226] Replace at least one of the object or the corresponding additional object with a reference object.

[0227] 10. The computer implementation method of claim 9, wherein the signature is a three-dimensional 3D vector.

[0228] 11. The computer implementation method of claim 9, wherein the signature is based on at least one of the following: one or more object vertex positions, one or more object vertex normals, colors, or one or more vertex texture coordinates.

[0229] 12. The computer implementation method as described in claim 9, further comprising:

[0230] The signature and the plurality of additional signatures are stored in a spatial partitioned data structure; and

[0231] Calculate the distance between the signature and each of the plurality of additional signatures.

[0232] 13. The computer implementation method of claim 9, wherein the object is represented by a mesh or point cloud.

[0233] 14. The computer implementation method of claim 9, wherein the signature is calculated by singular value decomposition.

[0234] 15. The computer implementation method as described in claim 9, further comprising:

[0235] Determine the rotation of the object; and

[0236] The rotation is stored for use in rendering the object within the three-dimensional 3D scene.

[0237] 16. The computer implementation method as described in claim 9, further comprising:

[0238] Determine the centroid of the object; and

[0239] The centroid is stored for use in rendering the object within the three-dimensional 3D scene.

[0240] 17. A system comprising:

[0241] One or more processing units are configured to determine that two or more objects are within a threshold similarity based on corresponding signatures corresponding to two or more objects in a 3D scene, and to store the two or more objects as a shared reference representation.

[0242] 18. The system of claim 17, wherein the corresponding signature is based on at least one of the following: one or more object vertex positions, one or more object vertex normals, at least one color, or one or more vertex texture coordinates.

[0243] 19. The system of claim 17, wherein the threshold similarity is based on the distance between corresponding vectors associated with the corresponding signature.

[0244] 20. The system of claim 17, wherein the system is one of the following:

[0245] A system used to perform simulation operations;

[0246] A system used to perform simulations to test or validate autonomous machine applications;

[0247] Systems used to perform digital twin operations;

[0248] A system for performing optical transmission simulation;

[0249] A system used for rendering graphics output;

[0250] A system used to perform deep learning operations;

[0251] Systems implemented using edge devices;

[0252] Systems used to generate or present virtual reality (VR) content;

[0253] A system for generating or presenting augmented reality (AR) content;

[0254] A system for generating or presenting mixed reality (MR) content;

[0255] A system containing one or more virtual machines (VMs);

[0256] A system used to perform operations for conversational AI applications;

[0257] A system used to perform operations for generative AI applications;

[0258] A system for performing operations using a language model;

[0259] A system for performing one or more generative content operations using a large language model (LLM);

[0260] A system for performing one or more content generation operations using a Visual Language Model (VLM);

[0261] A system for performing one or more content generation operations using a multimodal language model;

[0262] A system that is at least partially implemented in a data center;

[0263] A system for performing hardware tests using simulation;

[0264] Systems for generating synthetic data;

[0265] A collaborative content creation platform for 3D assets; or

[0266] A system that utilizes cloud computing resources at least in part.

[0267] Other variations are within the spirit of this disclosure. Therefore, although the disclosed technology is readily adaptable to various modifications and alternative constructions, certain embodiments thereof are illustrated in the accompanying drawings and have been described in detail above. However, it should be understood that the disclosure is not intended to be limited to one or more specific forms disclosed, but rather, it is intended to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of this disclosure as defined in the appended claims.

[0268] Unless otherwise stated or obviously contradicted by the context, the terms “a,” “an,” and “the,” and similar references, used in the context of describing the disclosed embodiments (particularly in the context of the appended claims), should be interpreted as encompassing both singular and plural forms, rather than as definitions of the terms. Unless otherwise stated, the terms “comprising,” “having,” “including,” and “containing” should be interpreted as open-ended terms (meaning “including, but not limited to”). The term “connection” (referring to a physical connection where not modified) should be interpreted as partially or wholly contained, attached to, or joined together, even with some intervention. Unless otherwise indicated herein, references to numerical ranges herein are intended only as a way of abbreviating each individual value falling within that range, and each individual value is incorporated into the specification as if it were separately described herein. Unless otherwise indicated or contradicted by the context, the use of the terms “set” (e.g., “item set”) or “subset” should be interpreted as a non-empty set comprising one or more members. Furthermore, unless otherwise indicated or contradicted by the context, the term "subset" of a corresponding set does not necessarily refer to an appropriate subset of the corresponding set, but rather the subset and the corresponding set can be equal.

[0269] Unless otherwise explicitly stated or clearly contradicted by the context, connective phrases such as “at least one of A, B, and C” or “at least one of A, B, and C” are understood in the context to generally refer to items, terms, etc., which can be A or B or C, or any non-empty subset of the set A, B, and C. For example, in an illustrative example of a set with three members, the connective phrases “at least one of A, B, and C” and “at least one of A, B, and C” refer to any of the following sets: {A}, {B}, {C}, {A, B}, {A, C}, {B, C}, {A, B, C}. Therefore, such connective language is generally not intended to imply that some embodiments require the presence of at least one of A, at least one of B, and at least one of C. Additionally, unless otherwise stated or contradicted by the context, the term “multiple” indicates a plural state (e.g., “multiple items” means multiple items). The number of items in a multiple item is at least two, but may be more if explicitly indicated or indicated by the context. Furthermore, unless otherwise stated or clearly understood from the context, the phrase “based on” means “at least partially based on” rather than “based on only”.

[0270] Unless otherwise indicated herein or clearly contradicted by the context, the operations of the processes described herein may be performed in any suitable order. In at least one embodiment, processes such as those described herein (or variations thereof and / or combinations thereof) are executed under the control of one or more computer systems configured with executable instructions and are implemented as code (e.g., executable instructions, one or more computer programs, or one or more application programs) that is executed jointly on one or more processors via hardware or a combination thereof. In at least one embodiment, the code is stored on a computer-readable storage medium, for example, in the form of a computer program comprising a plurality of instructions executable by one or more processors. In at least one embodiment, the computer-readable storage medium is a non-transitory computer-readable storage medium that excludes transient signals (e.g., propagating transient electrical or electromagnetic transmissions) but includes non-transitory data storage circuitry (e.g., buffers, caches, and queues). In at least one embodiment, code (e.g., executable code or source code) is stored on one or more non-transitory computer-readable storage media (or other memory for storing executable instructions) on which executable instructions are stored, which, when executed by one or more processors of a computer system (i.e., as a result of execution), cause the computer system to perform the operations described herein. In at least one embodiment, the set of non-transitory computer-readable storage media comprises multiple non-transitory computer-readable storage media, and one or more of the individual non-transitory storage media lack all the code, but the multiple non-transitory computer-readable storage media collectively store all the code. In at least one embodiment, the executable instructions are executed such that different instructions are executed by different processors; for example, the non-transitory computer-readable storage media store the instructions, and the main central processing unit (“CPU”) executes some instructions while the graphics processing unit (“GPU”) executes other instructions. In at least one embodiment, different components of the computer system have separate processors, and the different processors execute different subsets of the instructions.

[0271] Therefore, in at least one embodiment, the computer system is configured to implement one or more services that perform the operations of the processes described herein, either individually or collectively, and such a computer system is configured with suitable hardware and / or software to enable the implementation of the operations. Furthermore, the computer system implementing at least one embodiment of this disclosure is a single device, and in another embodiment it is a distributed computer system comprising multiple devices operating in different ways, such that the distributed computer system performs the operations described herein, and that a single device does not perform all the operations.

[0272] The use of any and all examples or exemplary language (e.g., “such as”) provided herein is intended only to better illustrate embodiments of this disclosure and does not constitute a limitation on the scope of the disclosure unless otherwise required. No language in the specification should be construed as indicating that any unclaimed element is essential to the practice of the disclosure.

[0273] All references cited in this article, including publications, patent applications and patents, are incorporated herein by reference as if each reference were individually and specifically indicated to be incorporated herein by reference and the entire contents of which are described herein.

[0274] The terms “coupled” and “connected”, and their derivatives, may be used in the specification and claims. It should be understood that these terms may not be intended to be synonyms with each other. Rather, in certain examples, “connected” or “coupled” may be used to indicate that two or more elements are in direct or indirect physical or electrical contact with each other. “Coupled” may also mean that two or more elements are not in direct contact with each other, but still cooperate or interact with each other.

[0275] Unless otherwise expressly stated, it will be understood that throughout this specification, terms such as “processing,” “computing,” “determining,” etc., refer to the actions and / or processes of a computer or computing system or similar electronic computing device that process and / or convert data represented as physical quantities (e.g., electrons) in the registers and / or memory of the computing system into other data represented as physical quantities in the memory, registers, or other such information storage, transmission, or display devices of the computing system.

[0276] In a similar manner, the term "processor" can refer to any device or part of memory that processes electronic data from registers and / or memory and converts that electronic data into other electronic data that can be stored in registers and / or memory. As a non-limiting example, a "processor" can be a CPU or a GPU. A "computing platform" can include one or more processors. As used herein, a "software" process can include, for example, software and / or hardware entities that perform work over time, such as tasks, threads, and intelligent agents. Similarly, each process can refer to multiple processes that execute instructions sequentially or intermittently, sequentially, or in parallel. The terms "system" and "method" are used interchangeably herein, provided that a system can embody one or more methods, and a method can be considered a system.

[0277] This document refers to the process of acquiring, obtaining, receiving, or inputting analog or digital data into a subsystem, computer system, or computer-implemented machine. Analog and digital data can be acquired, obtained, received, or input in various ways, such as by receiving data as a parameter to a function call or a call to an application programming interface (API). In some implementations, the process of acquiring, obtaining, receiving, or inputting analog or digital data can be accomplished by transmitting data via a serial or parallel interface. In another implementation, the process of acquiring, obtaining, receiving, or inputting analog or digital data can be accomplished by transmitting data from a providing entity to an acquiring entity via a computer network. Reference can also be made to providing, outputting, transmitting, sending, or presenting analog or digital data. In various examples, the process of providing, outputting, transmitting, sending, or presenting analog or digital data can be implemented by transmitting data as an input or output parameter to a function call, an API, or an inter-process communication mechanism.

[0278] While the discussion above illustrates example implementations of the described technologies, other architectures can be used to implement the described functionality and are intended to fall within the scope of this disclosure. Furthermore, although specific assignments of responsibilities have been defined above for discussion purposes, various functions and responsibilities can be assigned and divided in different ways depending on the circumstances.

[0279] Furthermore, although the subject matter has been described in language specific to structural features and / or methodological actions, it should be understood that the subject matter claimed in the appended claims is not necessarily limited to the specific features or actions described. Rather, specific features and actions are disclosed as exemplary forms for implementing the claims.

Claims

1. At least one processor, said processor comprising: One or more circuits are used for: Calculate the covariance matrix of the objects represented in a 3D scene; The signature of the object is calculated using the covariance matrix; Similar objects are determined based on the signature of the object; as well as A single representation is maintained in memory for rendering both the similar object and the object.

2. The at least one processor as claimed in claim 1, wherein, The one or more circuits are also used for: Determine the rigid rotation of the object.

3. The at least one processor as claimed in claim 1, wherein, The covariance matrix is ​​calculated based on one or more three-dimensional 3D vectors of the object.

4. The at least one processor as claimed in claim 1, wherein, The one or more circuits are also used for: The signature is stored in the data representation; Determine the distance between the similar object and the object; and Based on the distance, it is determined that the similar object and the object are similar objects.

5. The at least one processor as claimed in claim 1, wherein, The distance is determined by at least one of the squared vector normal form or the cosine similarity function.

6. The at least one processor as claimed in claim 1, wherein, The covariance matrix is ​​based at least in part on at least one of the following: object vertex position, object vertex normal, color, or vertex texture coordinates.

7. The at least one processor as claimed in claim 1, wherein, The one or more circuits are also used for: Based on multiple scenarios including multiple objects, train one or more neural networks to identify one or more features representing the objects; Select one or more of the aforementioned characteristics; and This allows the use of one or more features to determine the covariance matrix.

8. The at least one processor as claimed in claim 1, wherein, The processor is included in at least one of the following: A system used to perform simulation operations; A system used to perform simulations to test or validate autonomous machine applications; Systems used to perform digital twin operations; A system for performing optical transmission simulation; A system used for rendering graphics output; A system used to perform deep learning operations; Systems implemented using edge devices; Systems used to generate or present virtual reality (VR) content; A system for generating or presenting augmented reality (AR) content; A system for generating or presenting mixed reality (MR) content; A system containing one or more virtual machines (VMs); A system used to perform operations for conversational AI applications; A system used to perform operations for generative AI applications; A system for performing operations using a language model; A system for performing one or more generative content operations using a large language model (LLM); A system for performing one or more content generation operations using a Visual Language Model (VLM); A system for performing one or more content generation operations using a multimodal language model; A system that is at least partially implemented in a data center; A system for performing hardware tests using simulation; Systems for generating synthetic data; A collaborative content creation platform for 3D assets; or A system that utilizes cloud computing resources at least in part.

9. A computer-implemented method, the computer-implemented method comprising: The signature of the object is calculated based on the input characteristics corresponding to the appearance of the object represented in the 3D scene. Compare the signature with multiple additional signatures of a set of additional objects represented in the three-dimensional 3D scene; Based on at least one similarity metric, determine that one of the additional signatures of the corresponding additional object is similar to the signature; as well as Replace at least one of the object or the corresponding additional object with a reference object.

10. The computer implementation method as described in claim 9, wherein, The signature is a three-dimensional 3D vector.

11. The computer implementation method as described in claim 9, wherein, The signature is based on at least one of the following: one or more object vertex positions, one or more object vertex normals, colors, or one or more vertex texture coordinates.

12. The computer implementation method as described in claim 9, further comprising: The signature and the plurality of additional signatures are stored in a spatial partitioning data structure; as well as Calculate the distance between the signature and each of the plurality of additional signatures.

13. The computer implementation method as described in claim 9, wherein, The object is represented by a mesh or point cloud.

14. The computer implementation method as described in claim 9, wherein, The signature is calculated using singular value decomposition.

15. The computer implementation method as described in claim 9, further comprising: Determine the rotation of the object; as well as The rotation is stored for use in rendering the object within the three-dimensional 3D scene.

16. The computer implementation method as described in claim 9, further comprising: Determine the centroid of the object; as well as The centroid is stored for use in rendering the object within the three-dimensional 3D scene.

17. A system comprising: One or more processing units are configured to determine that the two or more objects are within a threshold similarity based on corresponding signatures corresponding to two or more objects in a three-dimensional 3D scene, and to store the two or more objects as a shared reference representation.

18. The system of claim 17, wherein, The corresponding signature is based on at least one of the following: one or more object vertex positions, one or more object vertex normals, at least one color, or one or more vertex texture coordinates.

19. The system of claim 17, wherein, The threshold similarity is based on the distance between the corresponding vectors associated with the respective signatures.

20. The system of claim 17, wherein, The system is one of the following: A system used to perform simulation operations; A system used to perform simulations to test or validate autonomous machine applications; Systems used to perform digital twin operations; A system for performing optical transmission simulation; A system used for rendering graphics output; A system used to perform deep learning operations; Systems implemented using edge devices; Systems used to generate or present virtual reality (VR) content; A system for generating or presenting augmented reality (AR) content; A system for generating or presenting mixed reality (MR) content; A system containing one or more virtual machines (VMs); A system used to perform operations for conversational AI applications; A system used to perform operations for generative AI applications; A system for performing operations using a language model; A system for performing one or more generative content operations using a large language model (LLM); a system for performing one or more content generation operations using a visual language model (VLM); a system for performing one or more content generation operations using a multimodal language model; A system that is at least partially implemented in a data center; A system for performing hardware tests using simulation; Systems for generating synthetic data; A collaborative content creation platform for 3D assets; or A system that utilizes cloud computing resources at least in part.

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