Machine learning techniques for automating tasks based on boundary representations of 3d cad objects

Abstract

In various embodiments, an inference application performs tasks associated with 3D CAD objects that are represented using B-reps. A UV-net representation of a 3D CAD object that is represented using a B-rep includes a set of 2D UV-grids and a graph. In operation, the inference application maps the set of 2D UV-grids to a set of node feature vectors via a trained neural network. Based on the node feature vectors and the graph, the inference application computes a final result via a trained graph neural network. Advantageously, the UV-net representation of the 3D CAD object enabled the trained neural network and the trained graph neural network to efficiently process the 3D CAD object.

What is claimed is: 
     
         1 . A computer-implemented method for performing tasks associated with three-dimensional (3D) computer-aided design (CAD) objects that are represented using boundary-representations (B-reps), the method comprising:
 mapping a plurality of two-dimensional (2D) UV-grids included in a first UV-net representation of a first 3D CAD object to a set of node feature vectors via a first trained neural network; and   computing a final result via a trained graph neural network based on the set of node feature vectors and a first graph included in the first UV-net representation.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein computing the final result comprises:
 mapping a plurality of one-dimensional (1D) UV-grids included in the first UV-net representation to a set of edge feature vectors via a second trained neural network; and   causing the trained graph neural network to execute a message passing algorithm that propagates the set of node feature vectors and the set of edge feature vectors over the first graph to generate a set of node embeddings.   
     
     
         3 . The computer-implemented method of  claim 2 , wherein the second trained neural network comprises a convolution neural network having a set of weights that are shared across the plurality of 1D UV-grids. 
     
     
         4 . The computer-implemented method of  claim 2 , wherein a first 1D UV-grid included in the plurality of 1D UV-grids specifies at least one of a 3D point position in a geometry domain, a 3D curve tangent, or a 3D face normal for each 1D grid point included in a plurality of 1D grid points in a parameter domain. 
     
     
         5 . The computer-implemented method of  claim 1 , wherein a first 2D UV-grid included in the plurality of 2D UV-grids specifies at least one of a 3D point position in a geometry domain, a 3D normal, or a visibility flag for each 2D grid point included in a plurality of 2D grid points in a parameter domain. 
     
     
         6 . The computer-implemented method of  claim 1 , wherein computing the final result comprises causing a plurality of graph layers included in the trained graph neural network to recursively compute a plurality of sets of hidden node feature vectors and a plurality of sets of hidden edge feature vectors based on the set of node feature vectors and a set of edge feature vectors to generate a set of node embeddings. 
     
     
         7 . The computer-implemented method of  claim 1 , wherein computing the final result comprises:
 mapping the first graph, the set of node feature vectors, and a set of edge feature vectors to a set of node embeddings and a shape embedding via the trained graph neural network;   concatenating the shape embedding to each node embedding included in the set of node embeddings to generate a set of embedding concatenations; and   mapping the set of embedding concatenations to a set of predicted classifications via a trained non-linear classifier, wherein each predicted classification included in the set of predicted classifications is associated with different face of a B-rep of the first 3D CAD object.   
     
     
         8 . The computer-implemented method of  claim 1 , wherein computing the final result comprises:
 mapping the first graph, the set of node feature vectors, and a set of edge feature vectors to a shape embedding via the trained graph neural network; and   mapping the shape embedding to a predicted classification for the 3D CAD object via a trained non-linear classifier.   
     
     
         9 . The computer-implemented method of  claim 1 , further comprising computing the first UV-net representation based on a B-rep of the first 3D CAD object. 
     
     
         10 . The computer-implemented method of  claim 1 , wherein the final result specifies at least one of a set of node embeddings or a shape embedding, and further comprising transmitting the final result to a CAD tool that determines one or more similarities in shape between the first 3D CAD object and a second 3D CAD object based at least in part on the final result. 
     
     
         11 . One or more non-transitory computer readable media including instructions that, when executed by one or more processors, cause the one or more processors to perform tasks associated with three-dimensional (3D) computer-aided design (CAD) objects that are represented using boundary-representations (B-reps) by performing the steps of:
 mapping a plurality of two-dimensional (2D) UV-grids included in a first UV-net representation of a first 3D CAD object to a set of node feature vectors via a first trained neural network; and   computing a final result via a trained graph neural network based on the set of node feature vectors and a first graph included in the first UV-net representation.   
     
     
         12 . The one or more non-transitory computer readable media of  claim 11 , wherein computing the final result comprises causing the trained graph neural network to execute a message passing algorithm that propagates the set of node feature vectors over the first graph to generate a set of node embeddings. 
     
     
         13 . The one or more non-transitory computer readable media of  claim 11 , wherein the first trained neural network comprises a convolution neural network having a set of weights that are shared across the 2D UV-grids. 
     
     
         14 . The one or more non-transitory computer readable media of  claim 11 , wherein a first 2D UV-grid included in the plurality of 2D UV-grids specifies at least one of a 3D point position in a geometry domain, a 3D normal, or a visibility flag for each 2D grid point included in a plurality of 2D grid points in a parameter domain. 
     
     
         15 . The one or more non-transitory computer readable media of  claim 11 , wherein computing the final result comprises causing a plurality of graph layers included in the trained graph neural network to recursively compute a plurality of sets of hidden node feature vectors and a plurality of sets of hidden edge feature vectors based on the set of node feature vectors and a set of edge feature vectors to generate a set of node embeddings. 
     
     
         16 . The one or more non-transitory computer readable media of  claim 15 , wherein computing the final result further comprises causing the trained graph neural network to compute a shape embedding based on the plurality of sets of hidden node feature vectors. 
     
     
         17 . The one or more non-transitory computer readable media of  claim 11 , wherein computing the final result comprises:
 mapping the first graph, the set of node feature vectors, and a set of edge feature vectors to a set of node embeddings and a shape embedding via the trained graph neural network;   concatenating the shape embedding to each node embedding included in the set of node embeddings to generate a set of embedding concatenations; and   mapping the set of embedding concatenations to a set of predicted classifications via a trained non-linear classifier, wherein each predicted classification included in the set of predicted classifications is associated with different face of a B-rep of the first 3D CAD object.   
     
     
         18 . The one or more non-transitory computer readable media of  claim 11 , wherein computing the final result comprises:
 mapping the first graph, the set of node feature vectors, and a set of edge feature vectors to a shape embedding via the trained graph neural network; and   mapping the shape embedding to a predicted classification for the 3D CAD object via a trained non-linear classifier.   
     
     
         19 . The one or more non-transitory computer readable media of  claim 11 , further comprising computing the first UV-net representation based on a B-rep of the first 3D CAD object. 
     
     
         20 . A system comprising:
 one or more memories storing instructions; and   one or more processors coupled to the one or more memories that, when executing the instructions, perform the steps of:
 mapping a plurality of two-dimensional UV-grids included in a UV-net representation of a three-dimensional computer-aided design object to a set of node feature vectors via a trained neural network; and 
 computing a final result via a trained graph neural network based on the set of node feature vectors and a graph included in the UV-net representation.