79 results about "Geometric primitive" patented technology

The invention belongs to the field of digital twinning, and provides a PhysGaussian-based digital twinning historic building construction method and system, and the method comprises the steps: carrying out the fitting of geometric primitives through employing a differential optimization algorithm, constructing a scene model of a target historic building through employing a PhysGaussian modeling framework, and carrying out the dynamic simulation prediction of the scene model fusing physical parameters, thereby achieving the dynamic simulation prediction of the target historic building. And rendering the visual effect to generate a digital twin model. According to the method, the geometry, material characteristics and environment interaction data of the ancient building are fused through the Gaussian kernel, twinning is real, the method is suitable for complex structures and fragile materials of the ancient building, preventive protection and repair decision making are supported, and real-time updating of urban-level ancient building groups is supported.

The invention discloses an instance segmentation and structured topology reconstruction method for a complex geometric shape building, and relates to the technical field of three-dimensional model generation, and the method comprises the following steps: S1, building point cloud instance segmentation: employing a SoftGroup network to carry out the semantic analysis of an unmanned plane LiDAR point cloud, outputting an independent part instance containing the semantic tag of each building assembly, and carrying out the segmentation of a building point cloud instance; fine segmentation of each building component is realized through a point cloud instance mask; and S2, geometric primitive extraction: grouping instance point clouds according to semantic tags, and executing differential geometric fitting. Through semantic-geometric coupling modeling, unified expression of a special-shaped structure and a regular component is realized, the bottleneck of segmentation and parameterization of a traditional method in a complex building scene is solved, and the method is more efficient and efficient. And the boundary expression of the conical top or the hemispherical top and the supporting structure is simplified by adopting an elliptic curve section, the calculation complexity is reduced, and meanwhile, the integrity of the building framework is guaranteed.

The invention relates to the technical field of three-dimensional model design of molds, and provides a mold three-dimensional high-precision modeling method and system based on multi-modal data fusion, and the method comprises the steps: collecting an RGB image, a high dynamic range image and a structured light depth image of an entity mold, and carrying out the reconstruction to obtain a globally consistent dense point cloud; denoising and semantic segmentation are carried out on the point cloud, and a cavity function area, a core function area and a runner function area are recognized; performing differential parametric modeling based on an identification result: performing geometric primitive fitting on regular geometric features, performing NURBS curved surface reconstruction on a free-form surface, and performing swept volume or rotator fitting on a flow channel to obtain a parameterized geometry, a parameterized curved surface and a parameterized channel model; and applying boundary continuity constraint to fuse the parameterized geometry, the parameterized curved surface and the parameterized channel model, and constructing a hybrid parameterized three-dimensional model. According to the method, rapid reconstruction of a high-precision, high-semantic and full-parameterized three-dimensional model of an entity mold can be realized.

The invention discloses an apparatus and method for converting compressed geometries into accelerated data structures. An apparatus and method for transforming lossy compressed geometries into a bounding volume hierarchy. For example, one embodiment of a method includes constructing a bounding volume hierarchy (BVH) based on a compressed hierarchy (LOD) structure formed from iteratively merged geometric primitive cluster pairs, where constructing the BVH includes traversing the compressed hierarchy (LOD) structure, to select a subset of the cluster on one or more levels of a compressed hierarchy (LOD) structure based on the current cone of view; decompressing each cluster and constructing a per-cluster BVH on the primitive of each cluster, wherein each per-cluster BVH comprises a per-cluster BVH root node; and fusing the per-cluster BVH root nodes to form a BVH, the BVH being used to efficiently ray trace all decompressed geometric primitives in the scene.

The invention discloses an industrial design-oriented drawing semantic analysis and structured conversion method and system. The method comprises the following steps: acquiring an input image, carrying out different-scale coding on image features through an image coding backbone network, selecting a specific layer to extract a multi-scale feature map, and fusing to generate multi-scale features; inputting the multi-scale features into a geometric primitive detection network and an other element detection network, respectively identifying geometric primitives and non-geometric primitives, and carrying out positioning and classification; analyzing mutual relations between geometric elements and other elements contained in the graph through an element relation graph network, and generating formalized language description based on a matching rule; a Prompt template is constructed, geometric information is supplemented by using a multi-modal large model, the rationality of the supplemented information is verified through a geometric attribute relationship verifier, and complete formalized language description is obtained. According to the method, the industrial drawing image containing the complex constraint relation can be efficiently and accurately converted into machine-readable structured data.

The invention provides a virtual model generation method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the steps of determining description information for generating a virtual model in response to a received generation instruction of the virtual model, and generating a grid model based on the description information; determining a distance field set corresponding to the grid model, and performing structure sensing alignment processing on the grid model based on the distance field set to obtain a first model, the first model comprising a plurality of first geometric primitives; determining a first distance field subset corresponding to each first geometric primitive from the distance field set, and trimming the first model based on each first distance field subset to obtain a second model; and determining a plurality of target components based on the second model, constructing a target model based on the plurality of target components, and outputting the target model. According to the method and the device, efficient generation and accurate optimization of the virtual model can be realized, so that the quality and the efficiency of model generation are improved.

The invention relates to the technical field of image recognition, in particular to a wheel rim sundry visual detection system for railway axle counting, and the system comprises a wheel track feature preliminary extraction module which is used for receiving an axle counting point image, traversing the image to calculate the gradient magnitude of each pixel point, screening the pixel points with the gradient magnitude exceeding a preset threshold value, obtaining a discrete feature point set of a wheel track, and obtaining a wheel track feature point set; in the discrete feature point set of the wheel track, dividing the point set into a linear feature point cluster and an arc feature point cluster, and establishing a preliminary wheel track geometric primitive; according to the method, after an axle counting point image is received, edge information screening is completed according to a pixel gradient magnitude, non-significant boundary areas are eliminated through gradient threshold limitation, the accuracy of feature extraction is improved, and after a feature point set is formed, two types of feature point clusters of straight lines and arcs are divided, and geometric structure fitting is performed, so that the accuracy of feature extraction is improved. And the morphological structure of the wheel rail is expressed in a parameter form, so that the region definition stability is improved.

The invention discloses an SAR satellite strip mode task planning method, and aims to maximize the area coverage rate. According to the method, basic data containing time sequence geometric coverage primitives are obtained, and candidate planning schemes containing mixed parameters are initialized. During iterative optimization, an imaging interval is determined according to planning parameters, corresponding geometric primitives are identified, geometric merging calculation is executed to determine a planned coverage area, the area coverage performance is evaluated in combination with load constraint, and the parameters are updated by using an optimization algorithm. And outputting an optimal scheme after iteration. According to the method, through accurate geometric coverage calculation and constraint processing, the problems of inaccurate coverage estimation, incomplete constraint consideration and low planning efficiency are solved, and a feasible planning result with higher coverage rate and better performance can be quickly generated.

The present disclosure provides an object positioning method, device, equipment, medium and program product, and relates to the technical field of image processing. The method converts scene data reflecting the geometric information of the surface of an object into a set of local geometric primitives rich in spatial position, geometric type and characteristic parameters, calculates the geometric affinity between the primitives to represent the possibility that they belong to the same object, and then uses a clustering algorithm to realize instance segmentation, thereby solving the technical problem of low precision and poor robustness of traditional object positioning methods caused by occlusion, background interference and a large number of object types in a complex environment, realizing automatic object instance segmentation and positioning based on geometric clues without a large amount of manual annotation, and achieving the effects of improving the recognition accuracy of objects with complex geometric structures, enhancing the generalization ability of the method in real scenes, and significantly reducing the dependence on pre-training data and manual annotation.

The application provides a robot environment perception method based on multi-sensor fusion, comprising the following steps: performing space-time synchronization processing on original multi-sensor data to generate synchronized perception data; judging whether there is a semantic landmark matching the original or synchronized data in a topological map, wherein the matching condition is that the geometric primitive of the semantic landmark in the topological map and the same geometric primitive existing in the data are matched; when it is confirmed that there is a matching landmark, selecting the matching landmarks to form a set; based on the set, performing pose correction on the original multi-sensor data, fusing different pose constraints in the matching landmarks into the original data, and obtaining corrected data for robot path planning. The application can improve the consistency of multi-sensor data and the accuracy of robot environment perception, and provides reliable correction data for path planning.

The invention discloses a laser cutting path optimization method of laser cutting equipment, and relates to the technical field of cutting path optimization. According to the laser cutting path optimization method of the laser cutting equipment, geometric primitive data of a to-be-cut workpiece is obtained, contour analysis processing is carried out, and a contour geometric data set of the to-be-cut workpiece is extracted; cutting sequence planning processing is conducted on the contour geometric data set of the to-be-cut workpiece based on a genetic algorithm, and an initial cutting path of the to-be-cut workpiece is generated; multi-target collaborative optimization processing is conducted on the initial cutting path of the to-be-cut workpiece, and the optimized cutting path of the to-be-cut workpiece is obtained.According to the method, the optimized cutting path of the to-be-cut workpiece is verified, the optimized verification cutting path of the to-be-cut workpiece is generated, and therefore the machining efficiency is remarkably improved while the machining precision is guaranteed; therefore, the reliability of the cutting process is comprehensively improved.

Techniques applicable to a ray tracing hardware accelerator for traversing a hierarchical acceleration structure with reduced round-trip communications with a processor are disclosed. The reduction of round-trip communications with a processor during traversal is achieved by having a visibility mask that defines visibility states for regions within a geometric primitive available to be accessed in the ray tracing hardware accelerator when a ray intersection is detected for the geometric primitive.

The invention relates to a method, a system, equipment and a medium for constructing a three-dimensional model based on geometric primitive hierarchy, and the method comprises the following steps: generating basic geometric primitives based on user input; determining a mapping rule type and an input parameter based on the generated basic geometric primitive; automatically executing a mapping algorithm to generate an initial three-dimensional grid model based on the determined mapping rule type and the input parameters; and adjusting and optimizing the initial three-dimensional grid model according to user requirements to obtain a final three-dimensional grid model. According to the method, basic geometric primitives are subjected to formalized description through a mathematical expression, a hierarchical structure capable of being driven by parameters is constructed, and systematic organization and precise control of a three-dimensional model are achieved. Thus. The method can be widely applied to the technical field of computer graphics and three-dimensional modeling.

Approaches presented herein provide for the clustering of geometric primitives, such as triangular faces, of a mesh representation that is advantageous for downstream processing. In at least one embodiment, clustering can be performed based in part on the spatial locations of the geometric primitives, such as to minimize the surface area of bounding boxes around clustered primitives or minimize the overlap between bounding boxes. Other factors such as connectivity within, or across, clusters can also be considered. A cost function can be used that includes weighted combination of cost terms, where those cost terms can be selected based in part upon a type of downstream processing to be performed, or requirements / optimizations of hardware to perform the processing.

The present disclosure relates to a multi-view image generation apparatus and method, and a graphics processing unit. Waves are constructed according to primitive information and vertex information of geometric primitives to generate a plurality of waves for parallel processing, and the waves are scheduled by a wave management unit for execution, which can improve execution efficiency. Since the primitive information of the geometric primitives is stored in a first cache unit, data sharing between waves can be implemented. During the parallel processing of the waves, vertex information in a second cache unit is updated in real time according to vertex address information outputted by the waves, so that a raster processing unit can read the latest vertex information from the second cache unit. Finally, through an image generation unit, a multi-view image is generated according to image colors obtained by executions of the waves and views corresponding to pixel points.

The invention discloses a wheeled inspection robot and a camera motion understanding method and system thereof. Displacement, video stream data and pose data are acquired in real time through a data acquisition module; the camera motion classification module performs motion classification and visual detection on the video frames, outputs geometric primitive and semantic primitive tags and an equipment state detection result, and maps the geometric primitive and semantic primitive tags and the equipment state detection result to a reference frame; the SfM-VLM fusion inference module extracts geometric parameters of camera motion through an SfM algorithm, fuses visual features and then inputs the fused visual features into a VLM model to generate natural language semantic description; the structured annotation module generates double-track annotations of a tag layer and a subtitle layer based on geometric primitives, semantic primitive tags and natural language semantic description; the multi-modal decision module fuses geometric primitive and semantic primitive tags, equipment state detection results and data acquired by the data acquisition module to generate an inspection decision; the problems of motion decoupling, target tracking, data annotation and multi-modal decision making in a dynamic environment are solved, and the routing inspection reliability and the intelligent level are improved.

The approaches presented here involve clustering geometric primitives, such as triangular areas, within a mesh representation, which is advantageous for downstream processing. In at least one embodiment, clustering can be partially based on the spatial positions of the geometric primitives, for example, to minimize the surface area of ​​bounding boxes around clustered primitives or to minimize the overlap between bounding boxes. Other factors, such as connectivity within or between clusters, can also be considered. A cost function can be used that comprises a weighted combination of cost terms, where these cost terms can be selected partly based on the type of downstream processing to be performed or on hardware requirements / optimizations for performing the processing.

The present application provides a graphics rendering method and system based on GLSL shader, which relates to the technical field of three-dimensional model rendering. The rendering method includes: loading a 3D model, extracting data of control points of the 3D model in a VBO of a GPU, generating a first geometric primitive, extracting vertex data in the first geometric primitive, generating an index array of the vertex data, creating an EBO, processing the vertex data in sequence using a vertex shader based on the index array stored in the EBO, obtaining the position and interpolation data of each vertex, clipping the vertex data, regenerating a second geometric primitive, converting the second geometric primitive into a fragment, using a fragment shader to calculate the color data of each fragment, and a GPU rendering the color data in parallel to obtain a final output image. While converting a 3D model to a 2D image, the present application reduces resource consumption and memory occupancy, thereby improving rendering efficiency.

The invention discloses a three-dimensional point cloud data segmentation model training and application method and device and a medium, and relates to the technical field of three-dimensional point cloud data segmentation, and the method comprises the steps: constructing an implicit fitting module, for each type of geometric primitive type, obtaining a first data set corresponding to the geometric primitive type, training an implicit fitting module by using the first data set to obtain a post-training implicit fitting module corresponding to the geometric primitive type, and forming an initial three-dimensional point cloud data segmentation model by using the segmentation module, the clustering module and the post-training implicit fitting module corresponding to each type of geometric primitive type, and training the initial three-dimensional point cloud data segmentation model to obtain a three-dimensional point cloud data segmentation model, and completing three-dimensional point cloud data segmentation by using the three-dimensional point cloud data segmentation model. According to the three-dimensional point cloud data segmentation method and the three-dimensional point cloud data segmentation system, the primitive geometric loss information is introduced by adopting the trained post implicit fitting module with the same structure and different parameters, so that the accuracy and robustness of the three-dimensional point cloud data segmentation model can be improved.

The apparatus comprises a receiver 201 that receives a first depth map for a first video frame of the plurality of video frames. A divider 203 divides the first depth map into two-dimensional geometric primitives / polygons larger than two pixels. A map generator 207 generates a vertex position map indicating vertex positions of the geometric primitives in the first depth map. A depth map generator 205 generates a second depth map including the depth values ​​of the first depth map at the vertex positions, and a bitstream generator 209 generates a video bitstream to include the second depth map and the vertex position map for the first video frame. The division depends on the depth values ​​of the depth map. A complementary apparatus renders an image using a mesh generated from the bitstream by determining original vertex positions based on the received vertex position map and projecting them into a three-dimensional space.

This invention relates to a depth completion method for non-cooperative spatial targets based on geometric primitive constraints, belonging to the field of 3D structure reconstruction technology for spatial targets. The method includes: establishing local depth constraint equations based on the target's geometric primitives; constructing a multi-task depth completion neural network model and incorporating the local depth constraint equations into the network loss function; establishing a sample dataset and training the multi-task depth completion neural network model; and embedding the trained model into the information processing unit of a spaceborne platform for target depth and pose prediction. This method addresses the problems of low accuracy and interpretability in the fine structure recovery of existing neural network-based target depth completion models, offering advantages such as high accuracy, fast speed, and strong interpretability of prediction results, thus achieving accurate recovery of the 3D fine structure of spatial targets under structural constraints.

This specification discloses a method, device, storage medium and electronic device for pre-training a large model, by pre-determining a number of preset geometric primitives and the first description texts corresponding to each geometric primitive, and determining the second description text of the constructed geometric structure according to the relationship between each geometric primitive and the corresponding first description text. Then, the property data of the geometric structure is determined, thereby determining the description text of the geometric structure, and the large model is pre-trained with the description text as a training sample. By determining the description text of a single geometric primitive and the relationship between the geometric primitives, the second description text of the constructed geometric structure is determined, and the geometric structure is efficiently converted into text that can be understood by the large model, which reduces the complexity of converting the geometric structure into text, provides clearer structured data for the large model, and reduces the difficulty of training the large model that can perform generative design.

The invention discloses a drawing update identification method and system, and the method comprises the following steps: S1, analyzing drawing files of new and old versions, and extracting multi-dimensional information including geometric primitives, layers and text annotations, S2, comparing the extracted multi-dimensional information of new and old versions, and identifying difference items, S3, based on a preset rule base, analyzing the identified difference items, and carrying out the identification of the new and old versions of the new and old versions of the new and old versions of the new and old versions of the new and old versions. The method comprises the following steps of S1, comparing and analyzing a drawing version, S2, automatically judging the change type and the influence level of the drawing version, S4, automatically generating a visual change report on the basis of a comparison and analysis result, highlighting a change area in a graphical mode by the report, and attaching a structured change list.According to the method, a traditional drawing version management mode is thoroughly innovated through an automatic, multi-dimensional and intelligent technical means; accurate, efficient and intelligent change identification is realized, project risks are effectively reduced, and cooperation efficiency and management level are improved.

Methods, devices, and data streams are provided for generating, transmitting, and decoding scene descriptions of an augmented reality scene. According to the present principles, a scene description graph links nodes and includes information indicating how a proximity trigger has to be evaluated to trigger an action on a virtual object. Conditions regarding proximity are based on the set of geometric primitives plus boundary values that change related 3D regions.

The application discloses a kind of instance segmentation and structured topological reconstruction methods of complex geometric building, it is related to three-dimensional model generation technical field, comprising the following steps: S1, building point cloud instance segmentation: using SoftGroup network to the semantic analysis of unmanned aerial vehicle LiDAR point cloud, output contains the independent component instance of each building component semantic label, the fine segmentation of each building component is realized by point cloud instance mask;S2, geometric primitive extraction: according to semantic label, group instance point cloud is carried out, and differential geometric fitting is executed, the application is realized by semantic-geometric coupling modeling The unified expression of irregular structure and regular component, solve the segmentation and parameterization bottleneck in complex building scene in traditional method, also adopt the boundary expression of elliptic curve segment simplification conical top or hemispherical top and support structure, reduce the complexity of calculation, while guarantee the integrity of building skeleton.

The application discloses a wheeled inspection robot and a camera motion understanding method and system thereof, wherein displacement, video stream data and pose data are acquired in real time through a data acquisition module; a camera motion classification module classifies motion and detects vision for a video frame, outputs geometric primitives and semantic primitive labels and device state detection results, and maps to a reference frame; an SfM-VLM fusion inference module extracts geometric parameters of camera motion through an SfM algorithm, inputs a VLM model after fusing visual features to generate natural language semantic description; a structured annotation module generates double-track annotations of a label layer and a subtitle layer based on geometric primitives, semantic primitive labels and natural language semantic description; and a multi-modal decision module fuses geometric primitives and semantic primitive labels, device state detection results and data collected by the data acquisition module to generate an inspection decision; the problems of motion decoupling, target tracking, data annotation and multi-modal decision in a dynamic environment are solved, and the inspection reliability and intelligent level are improved.

This invention relates to the field of computer vision and image processing, and proposes a method and system for automatic 3D modeling of 2D engineering drawings. The method includes the following steps: inputting 2D engineering drawings; parsing the context, generating a feature to-do list, and establishing a cross-view spatial mapping coordinate system; extracting the component types from the feature to-do list; obtaining parameterized topological constraint rules for the component types from a preset knowledge base; adopting goal-oriented active probing to search for geometric contours matching the features in the three views, obtaining the geometric primitives and their spatial poses corresponding to each feature; reading the dimension annotations, attaching the dimension values ​​as logical variables to the corresponding geometric primitives, and generating an enhanced structured feature list; constructing an instruction compiler, inputting the enhanced structured feature list, mapping each feature to a feature class calling function in the engineering knowledge base, and synthesizing a Python source code sequence to generate a parameterized 3D model.

The approaches presented here involve clustering geometric primitives, such as triangular areas, within a mesh representation, which is advantageous for downstream processing. In at least one embodiment, clustering can be partially based on the spatial positions of the geometric primitives, for example, to minimize the surface area of ​​the bounding boxes around clustered primitives or to minimize the overlap between bounding boxes. Other factors, such as connectivity within or between clusters, can also be considered. A cost function can be used that comprises a weighted combination of cost terms, where these cost terms can be selected, in part, based on the type of downstream processing to be performed or on hardware requirements / optimizations for performing the processing.

The application relates to the technical field of architectural model design, and discloses a curtain wall simulation design method and system based on BIM, which comprises the following steps: obtaining architectural point cloud data containing complex curved surface features; combining various geometric features of the point cloud data to improve a traditional region growing algorithm to segment the point cloud data; fitting geometric primitives to the segmented point cloud data and constructing an architectural BIM model to complete curtain wall simulation design. The application aims to improve the accuracy of region growing algorithm in edge segmentation of different architectural planar point cloud data.