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.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims priority benefit of the United States Provisional Patent Application titled, “UV-NET LEARNING FROM BOUNDARY REPRESENTATIONS,” filed on Mar. 31, 2021 and having Ser. No. 63 / 169,070. The subject matter of this related application is hereby incorporated herein by reference.BACKGROUNDField of the Various Embodiments[0002]Embodiments of the present invention relate generally to computer science and computer-aided design software and, more specifically, to machine learning techniques for automating tasks based on boundary representations of 3D CAD objects.Description of the Related Art[0003]In the context of three-dimensional (“3D”) mechanical design, computer-aided design (“CAD”) tools are software applications that streamline the process of generating, analyzing, modifying, optimizing, displaying, and / or documenting designs of one or more 3D CAD objects making up an overarching mechanical design. Many of these types of...

AI Technical Summary

Benefits of technology

[0008]At least one technical advantage of the disclosed techniques relative the prior art is that the disclosed techniques enable 3D CAD objects that are represented using B-reps to be processed efficiently using neural networks. In particular, because topological data and geometric features derived from a given B-rep are stored as a graph and regular grids, respectively, using the disclosed techniques, the topological data and the geometric features can be directly and efficiently processed using neural networks. Further, because each geometric feature is captured from the parameter domain of a corresponding parametric surface or parametric curve using the disclosed techniques, the geometric features are predominantly invariant with respect to how shapes are specified in B-reps. Accordingly, the same 2D UV-grids are typically extracted from each B-rep associated with a given 3D CAD object. The disclosed techniques can therefore increase the likelihood that a machine learning model generates consistent final results for each 3D CAD object irrespective of the B-reps used to represent the 3D CAD object. These technical advantages provide one or more technological advancements over prior art approaches.

Problems solved by technology

One drawback of B-reps is that processing B-reps using neural networks can be quite difficult.

In general, training a conventional neural network to infer a useful final result from unstructured data, such a B-rep data, is impractical.

Consequently, even if training a conventional neural network to recognize meaningful patterns in B-rep data were possible, training that neural network to generate consistent final results for each 3D CAD object, irrespective of the B-rep used to represent the 3D CAD object, would be difficult, if not impossible.

Because B-rep data cannot be processed by conventional neural networks, many CAD tools that represent 3D CAD objects using B-reps are unable to efficiently or accurately perform certain types of tasks associated with 3D CAD objects.

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