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

The patent describes techniques that make it efficient to process 3D CAD objects using neural networks. These techniques convert the 3D CAD object into a stream of data that can be easily processed using neural networks. By using this approach, the traditional method of manually extracting data from the 3D CAD object using B-reps is improved. The techniques also capture the geometric features of each B-rep and store them as a graph and a regular grid, which makes it easy to use neural networks to process the object. Using this method, a consistent set of data is extracted from each B-rep, which leads to better results when using machine learning models. Overall, these techniques provide an improved way to process 3D CAD objects using neural networks.

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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