143 results about "Geometric representation" patented technology

The invention relates to a ship shoreline illegal invasion and occupation identification method and system based on AIS big data, and the method comprises the steps: obtaining the AIS data of a ship, and carrying out the preprocessing; obtaining geographic information data, and constructing geometric representation of geographic information; performing superposition matching on the preprocessed AIS data and the geometric representation of the geographic information to obtain a ship behavior occurrence area, and combining a set threshold to obtain a ship abnormal behavior identification result; and based on the ship abnormal behavior identification result, adopting a multistage clustering algorithm to perform coarse clustering on ship berthing points, then performing fine clustering on each coarse clustering group, and performing statistical analysis in combination with the preprocessed AIS data to obtain a ship shoreline illegal invasion and occupation identification result. Compared with the prior art, the method has the advantages of improving the capability of identifying shoreline resources and illegal wharfs and the like.

A system and method for processing genomic data using dynamic latent manifolds that transforms multi-modal genomic datasets into geometric representations within a curved manifold space. The system receives genomic datasets including DNA sequences, genetic variants, and expression data, then extracts biological features and assesses importance using trained neural networks. Manifold curvature values are computed based on biological significance, and genomic data is embedded as geometric structures where semantic relationships are represented through distance and curvature properties. The system generates compression pressure fields that influence processing decisions and computes optimal geodesic paths through the manifold to minimize cognitive action functionals. Adaptive compression rates are determined for different genomic regions based on geometric properties and biological importance. The manifold structure evolves through use, strengthening frequently accessed pathways while applying thermodynamic decay to unused concepts. The system supports hierarchical organization across biological scales, reversible navigation, and federated learning capabilities that enable privacy-preserving collaboration.

The invention discloses a virtual space multi-person interaction synchronous control method oriented to an end-cloud collaborative architecture. The invention relates to a computer graphics and three-dimensional modeling technology, and discloses a cross-format lightweight and geometric consistency maintenance method based on a three-dimensional model. Through format-independent geometric representation and a self-adaptive lightweight strategy, efficient compression and precision maintenance of three-dimensional model cross-format conversion are realized. The method specifically comprises the following steps: performing format analysis and geometric feature extraction on an input model, and establishing a unified internal representation; adaptively selecting a multi-level LOD lightweight strategy based on the complexity of the model; the accuracy of key information is ensured through geometric feature keeping and topology consistency detection; the geometric consistency is dynamically maintained by combining error monitoring and an iterative correction mechanism; and generating a target format lightweight model and carrying out quality verification. According to the method, adaptive precision control, multi-level consistency maintenance and format irrelevant processing are combined, the model size and conversion errors are effectively reduced, and cross-platform compatibility and geometric fidelity are improved. The method can be widely applied to the fields of industrial design, game development, virtual reality and the like.

The invention provides a data processing method in an electric power construction supervision project, and the method comprises the steps: carrying out the scanning of an engineering construction site through a data collection device, obtaining a three-dimensional point cloud data set containing geometric information, and enabling the three-dimensional point cloud data set to comprise the complete geometric representation of a site space; according to the three-dimensional point cloud data set, a data processing algorithm is adopted to remove noise information, processed point cloud data is obtained, and the processed point cloud data is used for subsequent modeling analysis; aiming at the aligned point cloud model, geometric parameters of key parts are extracted, actually measured geometric feature data are obtained, and the actually measured geometric feature data comprise actual size information of site construction; and generating a quality evaluation report according to the deviation distribution data, and sorting the abnormal regions according to deviation degrees to obtain a quality adjustment priority sequence.

The invention relates to a multi-modal sensor online calibration method based on dynamic environment self-adaption, which comprises the following steps: constructing a memory enhanced scene understanding network architecture, embedding a topology memory unit in a Segment Anything Model basic framework, and constructing a double-layer knowledge solidification mechanism; performing construction and feature extraction of a differential geometric field, and proposing a continuous geometric representation scheme based on a neural radiation field; a decoupling type cross-modal adversarial optimization mechanism is introduced, and a calibration task is decomposed into a geometric constraint and semantic association dual-channel optimization problem; a Bayesian-genetic hybrid optimization framework is provided for an environment-adaptive hybrid optimization engine, and global exploration and local fine tuning of a parameter space are realized. According to the invention, the automatic driving function with high efficiency, accuracy and high adaptability is realized.

Variants of the method can include determining a design, generating a geometric representation of the design, generating a topological representation of the design, determining a context value set, predicting a physical field using a trained physics model, optionally verifying the physical field, optionally generating a corrected physical field, and optionally training the physics model based on the corrected physical field.

The invention discloses a reconstruction method and device of a three-dimensional face model and electronic equipment. The method comprises the steps that an initial parameter set used for reconstructing a three-dimensional face model is determined according to a target data set, an initial three-dimensional face model is constructed according to the initial parameter set, and the three-dimensional face model is geometric representation of a three-dimensional face corresponding to a target object; determining a projection relationship between the initial three-dimensional face model and the face images at a plurality of preset angles; adjusting the initial parameter set according to the projection relation to obtain a target parameter set, and updating the initial three-dimensional face model according to the target parameter set to obtain a target three-dimensional face model; and optimizing the target three-dimensional face model according to a preset constraint condition to obtain a three-dimensional face model. According to the method and the device, the technical problems of low reconstruction efficiency and poor precision of the three-dimensional face model due to the fact that a three-dimensional face model reconstruction method in related technologies needs to depend on complex equipment and needs a large amount of face image data are solved.

The invention relates to the technical field of computer vision, in particular to a complex scene object recognition system based on multi-scale feature fusion, which comprises a feature extraction module, a Riemannian geometric feature pyramid module, a candidate region generation module and an instance recognition and positioning module, and is characterized in that the feature extraction module extracts multi-scale spatial features; the Riemannian geometric feature pyramid module maps spatial features to a Riemannian manifold space, calculates curvature distribution of feature manifolds, dynamically adjusts feature extraction levels and fusion strategies, and realizes geometric consistent fusion of different scale features through geodesic line feature fusion; the candidate region generation module generates an object candidate region based on the fusion features; and the instance identification and positioning module completes final object classification and positioning, introduces a differential geometry theory to construct a Riemannian geometric feature pyramid, realizes geometric representation and adaptive fusion of features, solves the problems of scale change, partial occlusion, background interference and the like in a complex scene, and effectively improves the accuracy and robustness of object identification.

A training method, apparatus and system for a feature extraction network of a 3D mesh model are provided. The method includes dividing a training 3D mesh model into a plurality of patches which do not overlap with each other, dividing the plurality of patches into first-type patches and second-type patches, and using mask embedding as a feature encoding of each second-type patch; inputting geometric representation information and positional representation information of each first-type patch into a feature extraction network; determining predicted geometric representation information of each face based on a feature encoding of each first-type patch output from the feature extraction network, the mask embedding, and positional representation information of each second-type patch, and adjusting parameters of the feature extraction network based on differences between the predicted geometric representation information and geometric representation information of the each face.

The invention discloses a point cloud registration method based on unsupervised multi-scale feature fusion and geometric representation. Two innovative modules, namely a multi-scale neighborhood feature fusion module and a triangle-based representation learning module, are provided. The multi-scale neighborhood feature fusion module is used for extracting local neighborhood information from point clouds of different scales, balancing fine details and a global structure on multiple scales by combining multi-scale neighborhood construction, an attention mechanism and a feature fusion strategy, and overcoming the challenge of single-scale feature extraction in processing local shape similarity. And the triangle-based representation learning module deeply fuses the multi-scale neighborhood features and geometric constraints based on triangular geometric descriptors to generate feature representation with higher discrimination, so that the accuracy and stability of registration are remarkably improved. Finally, the multi-scale neighborhood feature fusion module and the triangle-based representation learning module enable the model provided by the invention to show excellent registration performance and have excellent applicability.

Resource-efficient systems, methods, and software to estimate the geometry of indoor space(s) (e.g., a location) and render a model of the indoor space(s) to a user in real time natively on mobile smartphones is described. Lightweight neural networks are used to estimate depths for each frame in received video data of the location, and a surfel representation is used to fuse location depth information into a geometric representation. Detecting when a user has entered a new room based on the geometric representation in real time is also described, and the resulting ability to create full floor scans where each room is fused together and labeled during a scan.

The invention provides a three-dimensional semantic scene completion method and system based on semantic-physical joint representation. The method comprises the following steps: extracting image features according to multi-view image input, and mapping the image features to a three-dimensional voxel space; voxel features containing local semantic information and three-view features containing global semantic information are extracted through a double-branch encoder, key anchor points with identifiable semantic features are screened out according to semantic consistency of the voxel features and the three-view features, and efficient initialization of Gaussian representation in a key area is guided; and then introducing a semantic spherical harmonic function, projecting Gaussian semantic representation to an orthogonal spherical harmonic space, enhancing the modeling capability of local context geometric structure information, and promoting the semantic-geometric representation consistency of the context by using key voxel distribution alignment, thereby realizing three-dimensional semantic scene completion with both precision and efficiency.

There is provided a system and method for determining a three-dimensional model of an object using a projector and a light sensor arranged in a stereo configuration. The method including: receiving pixel intensity values captured by the light sensor for a plurality of images of a scene containing the object, wherein the images of the scene each capture different projection patterns emitted by the projector onto the scene; determining, using the pixel intensity values from a set of the plurality of images, correspondence between a projector plane and a camera plane to form a three-dimensional model of the object, the correspondence determined using a trained artificial neural network model that uses a combination of volume rendering and geometric representation; and outputting the three-dimensional model of the object.

The present disclosure relates to decoding facial gestures from human speech and other sounds for avatar rendering in AR / VR applications. Generating a character includes capturing, for each of a plurality of frames, sensor data including an image of an object and audio. Image data and audio data are captured from the object. First geometry data representing the object is generated using the image data. Second geometry data representing the object is determined using the first geometry data and characteristics of the object from the audio data, wherein the second geometry data is different from the first geometry data. A 3D geometric representation of the object for the object character is generated using the second geometric data.

The invention provides a visualization method of finite element result data in a dam three-dimensional model, and relates to the technical field of finite element analysis, and the method comprises the steps: constructing a finite element spatial discrete model based on geometric information in the finite element result data; based on a sampling position in the dam three-dimensional model, determining a target space unit or a target space region corresponding to the sampling position in the finite element spatial discrete model, and obtaining first dam body displacement data corresponding to the target space unit or the target space region from the finite element result data; converting the first dam body displacement data into a first visual parameter according to a preset mapping relationship, and associating the first visual parameter to a geometric representation of the dam three-dimensional model to form a visual attribute for rendering; and carrying out rendering display on the dam three-dimensional model based on the visualization attribute so as to output a displacement visualization result of the surface of the dam three-dimensional model. According to the method, the intuition, interactivity and updating efficiency of visual display can be improved.

A system and method for implementing experiential manifold cognition that extends persistent cognitive machines beyond discrete thought caching to continuous geometric representation of experience. The system maintains an experiential manifold comprising a differentiable manifold with Riemannian metric tensor encoding semantic relationships, compression pressure field governing memory consolidation, and potential field encoding goals and attention. Input data is projected onto the manifold through adaptive geometric diffusion preserving semantic structure. The system executes geometric transformations including metric evolution, geodesic computation, and curvature estimation. During non-interactive periods, autonomous evolution occurs through trajectory recombination and selective pruning. A user interface enables visualization and direct manipulation of manifold geometry, translating navigation into geodesic traversal and edits into metric modifications. The system maintains persistence across sessions and enables controlled federation between multiple manifolds through consent-bounded synchronization. Applications include persistent narrative worlds, collaborative cognitive spaces, and experiential intelligence systems that learn through geometric evolution.

The invention provides a structured grid-based unstructured grid division method and electronic equipment, and the method comprises the steps: determining a computational domain range of a combustion detonation simulation task, carrying out the surface grid division of a geometric model, obtaining a geometric representation surface grid, generating a structured grid according to the geometric representation surface grid and the computational domain range, and carrying out the calculation of a combustion detonation simulation task. The method comprises the following steps: constructing a structured grid, performing simulation calculation on the structured grid, and determining a grid encryption parameter according to a simulation calculation result of the structured grid, so that an unstructured grid is generated according to the grid encryption parameter, a calculation domain range and a geometric representation surface grid, and unstructured grid division based on the structured grid is realized. The method can replace the trial-and-error iterative optimization or complex dynamic adaptive process of the unstructured grid, improves the generation efficiency of the unstructured grid, guarantees the generation quality of the unstructured grid, balances the calculation efficiency while meeting the high-precision requirement, and is suitable for a large-scale parallel environment.

The invention discloses an electromagnetic field simulation adaptive grid generation method taking electromagnetic field distribution characteristics as core guidance and a system thereof. Combining the geometric model data of each radio frequency device with the connection relation information to form a geometric representation containing topological connection information; setting a current sampling point on a non-contact surface of the surface of the metal radio frequency device and other radio frequency devices based on the geometric representation containing the topological connection information, and obtaining a current value of the current sampling point; s parameters of two rounds and current values of current sampling points are obtained through the first two rounds of mesh generation; if the convergence condition is met, the grid generated in the first round is used for full-wave-band simulation; and if the convergence condition is not met, carrying out a local encryption process until the convergence condition is met, and carrying out full-wave band simulation by using the grid generated in the previous round. According to the method, the limitation that traditional grid generation is only adaptive to geometric shapes and neglects electromagnetic characteristics is broken through, and collaborative optimization of grid quality and calculation efficiency in a high-frequency electromagnetic field simulation scene is realized.

A system and method for implementing persistent cognitive computation through geometric representation of thought in a dynamic latent manifold. The system encodes inputs into a curved space characterized by time-evolving metric tensors, compression pressure fields derived from Ricci curvature, and goal potential fields that shape attention flow. Cognition occurs through geodesic traversal of this manifold, with attention following paths that minimize cognitive action while balancing semantic density and goal relevance. A Cognitive Dynamics Engine maintains manifold geometry, computing optimal trajectories and managing thought bundle operations including consolidation, expansion, and higher-order abstraction. During idle periods, autonomous dreaming processes reorganize the manifold through perturbation, recombination, and topological surgery. This architecture enables persistent memory through geometric encoding, where frequently accessed concepts develop high-curvature regions and cognitive shortcuts emerge from usage patterns, transforming artificial intelligence from stateless computation to structured motion through shaped memory space.

The present invention discloses a deformable object shape control method based on visual language model and historical data learning, which combines the guidance at the microscopic pixel level in the visual target and uses the information in the language target for task planning and logical reasoning at the macroscopic semantic level. Finally, the visual language model is used to fuse the target information of the two modalities, enabling the visual target and the language target to promote and cooperate with each other, ensuring the precise planning of the deformable object shape control task. An online parameter estimation is carried out using a black-box optimization algorithm to achieve real-time tracking and updating of the object deformation state. The parametric polygon model, as a geometric representation, compresses the high-dimensional visual observation data into a low-dimensional parameter space, significantly reducing the dimension of the deformable object state observation and only retaining the key state information related to the task.

Generating a persona includes capturing, for each of a plurality of frames, sensor data that includes image and audio of a subject. Image and audio data are captured of the subject. First geometric data representing the subject is generated using the image data. Second geometric data representing the subject is determined using the first geometric data and a characteristics of the subject from the audio data, wherein the second geometric data is different than the first geometric data. A 3D geometric representation of the subject for a subject persona is generated using the second geometric data.

A system and method for implementing persistent cognitive computation through geometric representation augmented with holonomy-based experiential memory. The system encodes inputs into a curved latent manifold and maintains bounded sets of holonomy descriptors at each location, enabling two-component cognitive states comprising position and experiential context. Cognition occurs through holonomy-sensitive traversal where paths depend jointly on geometric structure and accumulated path-dependent constraints. Holonomy generators are created during traversal from prediction errors and constraint encounters, composed into consolidated descriptors, and undergo lifecycle management including reinforcement, decay, and irreversible export to residual constraint regions. This architecture escapes location-only representations by distinguishing cognitive states that occupy identical semantic positions but arise through different experiential histories. The system supports counterfactual reasoning through holonomy switching at fixed locations and preserves semantic memory as compressed transport deformation rather than stored trajectories, enabling scalable experiential learning where repeated patterns strengthen constraints while capacity remains bounded.

The invention provides a self-supervised monocular depth estimation method based on parameterized geometric representation, which breaks through the traditional pixel-by-pixel independent prediction normal form, models a three-dimensional scene into an explicit plane set, and uniquely characterizes each plane by a normal vector and an offset. A geometric driving regularization mechanism is established according to depth relevance of pixels in the same plane, a depth discontinuous sensing module is introduced to identify a jump region, and then a structured plane generation module is designed to recover correct scene plane representation by using a space-time geometry clue and a consistency clue. And finally, deducing the depth by using an accurate plane normal and plane offset based on the geometric mapping relationship between the two plane parameters and the depth, thereby improving the continuity and accuracy of the depth map. According to the scheme of the invention, the depth estimation precision in indoor and outdoor scenes is improved, and strong generalization ability is shown in an unseen cross-domain scene.

A computer-implemented method trains an artificial intelligence model based on neural networks to predict physical features of a brake caliper from a geometric representation, such as a mesh. The method provides for receiving geometric representations of brake calipers, each comprising local regions such as vertices and faces, and local information relating to the geometry. The artificial intelligence model is trained using input data comprising the received local information and output data relating to physical features of the brake caliper, including fields of stresses due to the pressure of brake fluid and the friction force, and the integral of the elastic deformation energy. The system comprises an input interface to access training data and a processor module configured to train the artificial intelligence model. The trained artificial intelligence model predicts physical features of a brake caliper from the geometric representation, providing output data such as stress fields and deformation energy.

This invention relates to the field of computer graphics processing and 3D modeling technology, specifically to an automated intelligent generation system for 3D modeling of shoe exteriors. The system includes: data acquisition and streaming, where an image acquisition terminal generates and sends four-channel image data using a high-precision sensor array; feature reconstruction and representation, where a topological semantic reconstruction server extracts semantic features and constructs an initial geometric representation using an implicit function based on pixel alignment and a decoder to predict the symbolic distance function; topology optimization and segmentation, where topological streamline optimization and component semantic segmentation are performed based on the curvature field to generate a structured 3D model with independent component structures; and interaction and local updates, where an interactive terminal responds to user editing operations on independent component structures, sending local topology update commands, and the server performs parametric recalculation of the local mesh based on the commands. This invention solves the problem of the separation between geometric structure and semantic information, achieving real-time and refined model updates.

Variants of the method can include determining a design, generating a geometric representation of the design, generating a topological representation of the design, determining a context value set, predicting a physical field using a trained physics model, optionally verifying the physical field, optionally generating a corrected physical field, and optionally training the physics model based on the corrected physical field.

The application discloses a generative AI industrial design topology closed-loop optimization method and system, and relates to the technical field of computer-aided design, comprising: obtaining an initial three-dimensional geometric representation of an industrial product and design constraints, and constructing a geometric parameter offset field; in iterative deformation, calculating discrete curvature, deformation energy, time sequence mutation and topology anomaly index, triggering and positioning a topology sensitive area; in the area, performing edge collapse, edge split, local re-meshing, Loop subdivision, constraint Laplace smoothing or SDF local reconstruction, and verifying and repairing the results by Euler characteristic number, genus, boundary ring, connected component and topology anomaly; calling a physically consistent proxy model to predict performance and evaluate confidence, triggering finite element sampling when confidence or safety margin is insufficient, error risk and topology sensitive area overlap, updating the proxy model by using finite element true value increment, and finally completing layered optimization and complete finite element verification.

A method and apparatus of a device for obtaining a geometric representation of an object including a thin structure having a first surface and a second surface; generating one or more 2D blocking faces as a simplified representation of one of the first surface or the second surface; generating one or more 3D blocks based on an extrusion of the one or more 2D blocking faces; and determining a 3D mesh of the object based on the one or more 3D blocks.

The invention provides a dynamic online handwritten flow chart recognition method and device, and the method comprises the steps: extracting the geometric representation of strokes from online stroke data, and obtaining a stroke feature sequence; performing streaming reasoning on the stroke feature sequence through target coding operation by adopting an attention mask mechanism to obtain coding features; the target coding operation comprises the steps of segmenting a stroke feature sequence into non-overlapping blocks, setting stroke features in the blocks to be mutually visible, setting a current block to only access a preorder block in a historical window, and shielding future block information; and identifying symbol instances in the online handwritten flow chart in real time according to the coding characteristics, and outputting category information and position information of each symbol instance. According to the method provided by the invention, the real-time dynamic stream symbol segmentation and identification of the online handwritten flow chart are realized, and the real-time performance and accuracy of the identification of the online handwritten flow chart are improved.