9 results about "Geometric primitive" patented technology

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 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.

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 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.

This invention discloses a spatiotemporal data indexing query method, system, medium, and device based on GPU ray tracing, belonging to the field of spatial data indexing technology. The method includes responding to a point query request, obtaining the query coordinates, query time, and target object identifier from the point query request, emitting a vertical ray along a preset direction starting from the query coordinates and starting height, performing GPU ray traversal of the nearest intersection point on the vertical ray using a pre-established spatiotemporal index structure, and returning the intersecting geometric primitive with the smallest ray parameter, whose object identifier matches the target object identifier, and is a valid data record, as the query result of the point query request. This invention achieves hardware acceleration of spatiotemporal indexing query by uniformly mapping two-dimensional spatial coordinates and time dimension to a three-dimensional coordinate system and utilizing a bounding volume hierarchy and its corresponding geometric acceleration structure.

The present disclosure relates to an image reconstruction method, an electronic device and a storage medium, the image reconstruction method comprising: performing a first coordinate system conversion from a world coordinate system to a camera coordinate system on original primitive parameters of three-dimensional geometric primitives in a scene captured by an input image to obtain first primitive parameters of the three-dimensional geometric primitives, the primitive parameters of the three-dimensional geometric primitives being used to describe spatial attributes of the corresponding three-dimensional geometric primitives in the scene; applying directional distortion to the first primitive parameters of the three-dimensional geometric primitives to obtain second primitive parameters of the three-dimensional geometric primitives; performing a second coordinate system conversion from the camera coordinate system to the world coordinate system on the second primitive parameters of the three-dimensional geometric primitives to obtain target primitive parameters of the three-dimensional geometric primitives; and performing image rendering based on the target primitive parameters of the three-dimensional geometric primitives to obtain a target image after reconstruction. Through directional distortion, the target image is consistent with the input image in distortion parameters.

The application provides a hybrid rendering method and a graphics processing unit. The method is applied to a graphics processing unit and includes: obtaining a rendering instruction, the rendering instruction being used to instruct to perform a first rasterization task and a first ray tracing task on a geometric primitive in a viewport, wherein the geometric primitive is used as part of an input of the first ray tracing task and an input of the first rasterization task, and an output of the first rasterization task is used as another part of the input of the first ray tracing task; determining M second rasterization tasks respectively according to M sub-views of the viewport, and determining M second ray tracing tasks respectively according to the M sub-views, wherein an output of an i-th second rasterization task is used as part of an input of an i-th second ray tracing task; and performing the M second rasterization tasks and the M second ray tracing tasks, wherein at least one second rasterization task is performed in parallel with at least one second ray tracing task.