88 results about "3d shapes" patented technology

In various examples, a deep three-dimensional (3D) conditional generative model is implemented that can synthesize high resolution 3D shapes using simple guides—such as coarse voxels, point clouds, etc.—by marrying implicit and explicit 3D representations into a hybrid 3D representation. The present approach may directly optimize for the reconstructed surface, allowing for the synthesis of finer geometric details with fewer artifacts. The systems and methods described herein may use a deformable tetrahedral grid that encodes a discretized signed distance function (SDF) and a differentiable marching tetrahedral layer that converts the implicit SDF representation to an explicit surface mesh representation. This combination allows joint optimization of the surface geometry and topology as well as generation of the hierarchy of subdivisions using reconstruction and adversarial losses defined explicitly on the surface mesh.

The invention relates to the technical field of optical fiber communication device detection, and provides an optical fiber connector end face 3D shape detection method and system, and the method comprises the steps: obtaining a multi-step phase shift interference image sequence of an optical fiber connector end face; performing interface segmentation on the multi-step phase shift interference image sequence by adopting a fiber core boundary detection network based on radial gradient perception to obtain interface position data; based on the interface position data and the multi-step phase shift interference image sequence, performing phase unpacking by adopting a dual-coordinate system fusion phase unpacking algorithm to obtain initial phase data; performing global reconstruction on the initial phase data by adopting a regional confidence coefficient weighted reconstruction algorithm to obtain global phase field data; performing spherical parameter fitting on the global phase field data by adopting an IEC (International Electrotechnical Commission) standard constraint hierarchical particle swarm optimization algorithm to obtain spherical geometric parameters; and generating a 3D shape detection result of the end face of the optical fiber connector according to the spherical geometric parameters. According to the invention, the precision and reliability of 3D shape detection of the end face of the optical fiber connector are improved.

Systems and methods are provided for implementing a multi-stage, ML model training process for autonomous or semi-autonomous driving. The multi-stage ML model training process comprises (1) 2D and 3D supervised losses during a synthetic data ML model training, (2) 2D supervised on real-world data, and (3) 3D self-supervised losses on real-world data. The improved ML training process may not rely on 3D object recognition with real-world 3D labeled data. Once the ML model is trained, in some examples, the trained ML model can implement an inference process to predict the 3D shape, size, and 6D pose of objects within a single image, operate at a category level, and eliminate the need for computer-aided design (CAD) models during inference.

The present application belongs to the technical field of computer vision three-dimensional reconstruction, and specifically relates to a semi-supervised single-view 3D object reconstruction method. A small amount of labeled samples are used to train a neural network, and then the trained neural network is used to generate pseudo labels for unlabeled samples and guide the training of the unlabeled samples. At the same time, the present application proposes that a discriminator scores the quality of the generated pseudo labels, and limits the bias of low-quality pseudo labels on model training. In addition, the present application proposes a prototype shape prior module based on an attention mechanism, which serves as a bridge between images and 3D shapes, reduces the difference between the two modalities, and provides shape prior for the neural network, ensuring that the reconstructed 3D shape is natural. Compared with the current mainstream method in the industry, the present application is more accurate in three-dimensional reconstruction of a single image under the condition that the amount of labeled data is very small, and the generated 3D shape is more realistic and natural.

System, methods, and other embodiments described herein relate to single-shot multi-object three-dimensional (3D) shape reconstruction and categorical six-dimensional (6D) pose and size estimation. In one embodiment, a method includes inferring a heatmap based upon a feature pyramid, where the feature pyramid is generated based upon a red green blue depth (RGB-D) image that includes objects. The method further includes sampling a 3D parameter map at locations corresponding to peaks in the heatmap, where the 3D parameter map is inferred based upon the feature pyramid, and where the locations include latent shape codes, 6D poses, and one-dimensional (1D) scales. The method further includes generating point clouds based upon the latent shape codes, the 6D poses, and the 1D scales.

A first layer depth map generation unit (131) (distance acquisition unit) of an encoding apparatus (40a) (information processing apparatus) acquires a first distance (d1) between a projection point (Q) and a first point (P1) based on a 3D shape of a surface of a subject (90), a straight line extending from the projection point (Q) to the subject (90) contacts the subject (90) at the first point. A second layer depth map generation unit (132) (distance acquisition unit) acquires a second distance (d2) between the projection point (Q) and a second point (P2) based on the 3D shape of the surface of the subject (90), a straight line extending from the first point (P1) and into an interior of the subject (90) comes out of the subject (90) at the second point.

An organic interface converting data into tangible 3D shapes using programmable materials. It includes an interaction point with multiple surfaces and a projection module, all coordinated by a computing device for data transmission and reception. Data streams carry computer-readable code interpreted by the projection module to reorient surfaces, thereby materializing the data in three-dimensional space. The interface allows tactile input via human touch generating electrical currents that alter surface properties like position and smoothness. It also responds to audible frequencies and can receive data from secondary computing devices. Using a computing device, users can create 3D objects through data transmission to these responsive surfaces. The interface supports user interactions through touch, voice commands, and digital inputs from devices such as smartphones or tablets. Users have the flexibility to adjust the interface's resolution and response rate to tailor their interaction experience.

An object interference checking technique using point sets which uses CAD models of objects and obstacles and converts the CAD models to 3D points. The 3D point locations are updated based on object motion. The 3D points are then converted to 3D grid space indices defining space occupied by any point on any object or obstacle. The 3D grid space indices are then converted to 1D indices and the 1D indices are stored as a set per object and per position. Swept volumes for an object are created by computing a union of the 1D index sets across multiple motion steps. Interference checking between objects is performed by computing an intersection of the 1D index sets for a given motion step or position. The 1D indices are converted back to 3D coordinates to define the 3D shapes of the swept volumes and interferences.

This disclosure aims to make it possible to correct the displacement of the central axis of a tubular body caused by deformation of the tubular body. [Solution] This disclosure provides an analysis device that acquires measurement data of the surface shape of a target to be measured, calculates the longitudinal central axis of a tubular body in the target to be measured using the measurement data, and corrects the deviation of the central axis of the tubular body using information other than the tubular body obtained from the measurement data.

Techniques for generating a stylized drawing of three-dimensional (3D) shapes using neural networks are disclosed. A processing device generates a set of vector curve paths from a viewpoint of a 3D shape; extracts, using a first neural network of a plurality of neural networks of a machine learning model, surface geometry features of the 3D shape based on geometric properties of surface points of the 3D shape; determines, using a second neural network of the plurality of neural networks of the machine learning model, a set of at least one predicted stroke attribute based on the surface geometry features and a predetermined drawing style; generates, based on the at least one predicted stroke attribute, a set of vector stroke paths corresponding to the set of vector curve paths; and outputs a two-dimensional (2D) stylized stroke drawing of the 3D shape based at least on the set of vector stroke paths.

Embodiments are described for a graphic segmentation device comprising a memory and at least one processor coupled to the memory. The at least one processor is configured to receive point cloud data that represents a plurality of 3D objects and select one or more 3D volumes that segment a 3D object out of the plurality of 3D objects. Each of the one or more 3D volumes includes coordinates forming a 3D shape. The at least one processor is further configured to transmit a volume report to a graphic segmentation server, wherein the volume report includes the one or more 3D volume.

We provide the system. [Solution] A depth measurement means for acquiring three-dimensional shape data, A chat interface that receives user requests in natural language, A generation means for automatically generating a 3D model file based on the aforementioned 3D shape data and the aforementioned natural language request, A search method that searches for and presents existing products similar to the generated 3D model, A system that includes this.

Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures using generative design processes, where the three dimensional (3D) models of the physical structures are produced in accordance with a design criterion that limits a minimum thickness of the generatively designed 3D models, include: obtaining a design space for an object to be manufactured and one or more design criteria including a thickness constraint; iteratively modifying a generatively designed 3D shape of the modeled object in the design space in accordance with the one or more design criteria, including measuring a current thickness for the 3D shape using an overall relationship of a volume of the 3D shape with respect to a surface area of the 3D shape; and providing the generatively designed model for use in manufacturing the physical structure using one or more computer-controlled manufacturing systems.

A three-dimensional (3D) scanner is in communication with a display includes one or more optical sensors. The scanner scans, using the one or more optical sensors, an object having a surface. The scanning generates data corresponding to a 3D shape of at least a portion of the surface of the object. The scanner generates a 3D reconstruction of the shape of the surface of the object. The scanner provides a preview of the 3D reconstruction of the at least portion of the shape of the surface of the object. The scanner provides, to the display, for rendering with the preview of the 3D reconstruction of the at least portion of the shape of the surface of the object, an indication of at least one of a quantity or a quality of the data corresponding to the 3D shape of the at least portion of the surface of the object.

The invention relates to the technical field of automatic driving and intelligent traffic, in particular to a high-precision map dynamic updating method and system based on neural radiation field incremental learning, and the method comprises a dynamic object updating mechanism which specifically comprises the steps: carrying out the individualized modeling of each detected dynamic object, establishing an exclusive small NeRF model for the dynamic object, recording the 3D shape, color and motion trail of the dynamic object, and forming an independent file of the dynamic object; the model continuously tracks the moving state of an object, and when the position of the object changes, the possible position of the next frame is predicted according to the speed; when the shape of the object changes, details of door opening and umbrella lifting actions are recorded; the method has the beneficial effects that incremental data acquired by a large number of common vehicles (not professional surveying and mapping vehicles) are fully utilized, and the data acquisition cost is greatly reduced. Through incremental learning, full model retraining is avoided, and computing resource consumption and updating time are remarkably reduced.

Methods and systems are disclosed for performing operations for estimating a 3D scene representation from one or multiple 2D images. The operations include: receiving one or multiple two-dimensional (2D) images representing a real-world environment; and generating, by a machine learning model, a three-dimensional (3D) scene representation of the 2D image, which explicitly (separately) defines the a 3D shape and appearance of the background as well as a 3D position, 3D shape and appearance of each object of the scene depicted in the set of images, where the machine learning model has been trained in an unsupervised approach from a dataset of images and their camera poses (e.g. without any manually labelled annotations, such as depth maps, segmentation masks, object poses).

A data structure for 3D object management data used for displaying images of digitized 3D objects is disclosed.The data structure includes a registrant identification code to identify the registrant of the 3D object, an object type number specifying the type of the 3D object, an object serial number assigned to the 3D object whose type has been specified in sequential order of registration, a storage number to specify a storage means storing 3D data to specify a 3D shape of the 3D object to be registered, geodetic position data to specify a current position of the 3D object to be registered, orientation data to indicate the direction of the 3D object, and updated date and time of the 3D data. The 3D data is acquired from a blockchain, in which the 3D object management data is recorded, using an inputted geodetic position to generate and display relevant 3D images.

The present application relates to a dynamic modeling method and apparatus for a coronary artery, and a device, a medium and a program product. The method comprises: constructing a vascular tree model of a target coronary artery (S101); on the basis of the vascular tree model, calculating the skin weight of the target coronary artery (S102); and tracking the centerline coordinates of the target coronary artery phase by phase, obtaining a plurality of key frames of the vascular tree model in view of the skin weight, and combining the plurality of key frames to obtain a dynamic sequence, so as to use the dynamic sequence to construct a dynamic model of the target coronary artery (S103). Therefore, the problems in the relevant art, such as it being impossible to obtain the time-varying 3D shape of a blood vessel, the accuracy and efficiency of CFD calculation performed at different moments and for mesh models having different topologies being low, and it being impossible to construct the intermediate shape of the blood vessel and corresponding numerical meshes when images are missing or are of poor quality, are solved.

This invention discloses an implicit reconstruction method for point cloud surfaces based on a piecewise learning strategy. The invention employs a piecewise surface representation and trains an implicit reconstruction network for point cloud surfaces targeting the local symbolic distance field of a 3D shape. The method includes the following steps: using discrete point cloud data as input, a farthest-point sampling strategy is used to generate initial patches; the offset of each sampling point within each patch relative to the patch center is calculated as the relative position of the sampling point. The latent features of the patch are obtained in the neural network encoder, and the symbolic distance values ​​of the relative positions of each sampling point are obtained in the neural network decoder. The relative positions of sampling points located in overlapping areas of different patches are weighted and summed to obtain their corresponding symbolic distance values. A mesh model of the 3D shape is obtained using the Marching Cube algorithm. This invention can reconstruct the overall shape of an object while preserving the fine details of the original shape, and it is robust to point cloud normals and noise.

A computer-implemented method of optimising a shape of an aquatic vessel part. The method comprises obtaining (202) design data representing an initial 3D shape of an aquatic vessel part, and obtainin

Generate 3D shape data that provides a suitable viewing experience. [Solution] The information processing device is an information processing device having a generation means for generating three-dimensional shape data of an object based on a distance image, and includes a identification means for identifying an edge region in which edges relating to the object are distributed in the distance image, a determination means for determining whether or not there are irregularities that do not meet the tolerance conditions in the ridge lines corresponding to the edge region in the three-dimensional shape data generated by the generation means, and a correction means for executing a process related to correcting the shape of the ridge lines when the determination means determines that there are irregularities that do not meet the tolerance conditions.

An organic interface converting data into tangible 3D shapes using programmable materials. It includes an interaction point with multiple surfaces and a projection module, all coordinated by a computing device for data transmission and reception. Data streams carry computer-readable code interpreted by the projection module to reorient surfaces, thereby materializing the data in three-dimensional space. The interface allows tactile input via human touch generating electrical currents that alter surface properties like position and smoothness. It also responds to audible frequencies and can receive data from secondary computing devices. Using a computing device, users can create 3D objects through data transmission to these responsive surfaces. The interface supports user interactions through touch, voice commands, and digital inputs from devices such as smartphones or tablets. Users have the flexibility to adjust the interface's resolution and response rate to tailor their interaction experience.

To provide a three dimensional shape teacher data generation method for achieving a generation AI technology related to a 3DCAD for highly accurately understanding a complicated 3DCAD and generating a three dimensional model corresponding to a precise instruction.SOLUTION: The method for generating teacher data for learning the three dimensional shape of a recognition object includes a step in which a 3DCAD data-acquiring part 2 acquires CAD (computer aided design) data of the three dimensional shape, and a step in which a linguistic expression-converting part 3 converts the CAD data of the three dimensional shape acquired by the 3DCAD data-acquiring part 2 into a STEPAP242 to generate teacher data of the three dimensional shape. As a result, it is possible to realize a generation and AI technique related to a 3DCAD capable of understanding even a complicated 3DCAD with high accuracy and generating a three dimensional model according to a precise instruction.SELECTED DRAWING: Figure 1

Provided are computing systems, methods, and platforms that infer an object shape from an image using a neural radiance field (NeRF) model. A NeRF model can infer a 3D shape from a 2D image by performing a plurality of iterations to generate a plurality of sample 2D images of a 3D scene. For each iteration, an object code can be sampled from a posterior distribution of learned priors on NeRF models associated with the 3D scene, the object code can be processed with a hypernetwork to generate a set of NeRF weights from the object code, and a NeRF model with the set of NeRF weights predicted by the hypernetwork can generate a sample 2D image of the 3D scene. The sample 2D images generated during the iterations can be provided as an output.

The application provides a 3D shape reconstruction system based on active light and shadow, which does not depend on the texture of an object. The system controls a small point light source at the end of a mechanical arm, moves on the surface of a target object and projects clear shadows or highlights. Another fixed camera captures the dynamic changes of the light and shadow features, and through a learning model, the surface normal and depth information of the object which is invisible to both the light source and the camera are inverted. Passive observation is changed to active detection, and the dynamic light and shadow are projected by the mechanical arm to analyze the three-dimensional geometry of the textureless or highly reflective object. Without relying on the texture of the object itself, the reconstruction problem of structured light and multi-view stereo vision on textureless objects is solved. The active movement of the mechanical arm driving the point light source increases the dimension of light and shadow information from 2D to 3D, provides more abundant constraint conditions and significantly improves the inversion accuracy of the normal and depth. The double-branch deep learning model based on the attention mechanism, combined with bilateral filtering and guided filtering post-processing, improves the reconstruction accuracy.

The present invention provides a program for a device and method that imports information about multiple components constituting an assembly and the relationships between each component into the device. [Solution] The device 100 includes: an acquisition unit 201 that acquires a file created in OpenUSD format which includes 3D shape data of each of a plurality of parts constituting an assembly and coupling information which indicates the coupling relationships between the plurality of parts; a storage unit 202 which stores definition information 210 which includes coupling relationships between parts constituting each of a plurality of mechanisms and is associated with additional information corresponding to each of the plurality of mechanisms; a determination unit 203 which determines whether or not definition information 210 which indicates the same coupling relationship as the coupling information of the file exists in the definition information of the plurality of mechanisms; and a generation unit 204 which, if definition information which indicates the same coupling relationship as the coupling information exists, generates a dataset 220 that can be used in the control program development environment based on the plurality of parts, the coupling relationships between the plurality of parts and the additional information.

This disclosure provides a method and system for online intent recognition of unmanned swarms based on a self-attention model. First, depth images collected by the onboard equipment of the unmanned system are acquired, and the 3D bounding box of each target at the current moment is determined as the current detection target. Prior predictions are obtained based on the historical trajectories of the tracked unmanned swarm targets. The similarity between the current detection target and the prior predictions is measured from the target position, 3D shape, point cloud quantity, and spatial location to achieve cross-frame data association. A Transformer model based on a multi-head self-attention mechanism is used to process the associated historical trajectory sequence, jointly outputting the probability distribution of multiple intents of the swarm targets, and the short-term predicted position and bounding box size for each intent. This invention can achieve joint intent-trajectory prediction when facing concurrent multi-target swarms; and through an effective matching mechanism, it reduces the impact of interference on cross-frame data association, effectively reducing the false positive and false negative rates.

The disclosed system uses a body shape capturing device for acquiring a patient's body shape and a 3D shape generating device for additively manufacturing a patient receiving device that is at least partially adapted to the patient's body shape or at least partially deviates from the patient's body shape bringing the patient's body into a desired shape so that the outer shape of the patient's body during surgery is identical to the outer shape of the body during shape capturing. The patient receiving device comprises at least one tracker element that is detectable by a detection system. The detection system captures data indicating the at least one tracker element's position and / or orientation during surgery enabling, particularly for surgical operations on or in soft tissues with high flexibly and with no specific natural or artificial landmarks, the surgeon to orientate / navigate in live images from the surgical site.

An implant can be configured to transition between a first stable state and a second stable state. The implant can include a first surface and a second surface coupled to the first surface. The first surface can include a first structure configured to form a stable planar shape during the first stable state of the implant and to form a stable three-dimensional (“3D”) shape during the second stable state of the implant. The second surface can include a second structure configured to form a stable planar shape during the first stable state of the implant and to form a stable 3D shape during the second stable state of the implant.

This invention discloses a stripe projection 3D imaging system based on a Micro-LED array. Its 3D information acquisition module consists of an integrated packaged Micro-LED driver board, a Micro-LED array, a micro-projection module, and a CCD camera. The 3D reconstruction module utilizes a computer. The micro-projection module is equipped with a plano-convex lens and a mechanical focusing damping structure, and is separately arranged in the front and rear optical paths of the Micro-LED array. The system's workflow is as follows: the driver board reads pre-stored stripe pattern data and timing-controls the Micro-LED array to output coded structured light stripes; the focusing structure adjusts the object distance in the optical path, focusing and scaling the stripes before projecting them onto the surface of the object being measured; the CCD camera simultaneously acquires a sequence of deformed images modulated by the object's shape and transmits it to the computer; the computer combines a multi-step phase-shifting algorithm and Gray code rules to complete phase calculation and unfolding, and, combined with a preset phase-height mapping relationship, reconstructs the 3D shape and point cloud data of the object being measured. This invention enables low-cost, miniaturized, and high-performance 3D shape measurement.