ComplexGen: CAD Reconstruction by B-Rep Chain Complex Generation

• URL: http://arxiv.org/abs/2205.14573v1
• Date: Sun, 29 May 2022 05:30:33 GMT
• Title: ComplexGen: CAD Reconstruction by B-Rep Chain Complex Generation
• Authors: Haoxiang Guo and Shilin Liu and Hao Pan and Yang Liu and Xin Tong and Baining Guo
• Abstract summary: We view the reconstruction of CAD models in the boundary representation (B-Rep) as the detection of geometric primitives of different orders. We show that by modeling such comprehensive structures more complete and regularized reconstructions can be achieved.
• Score: 28.445041795260906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We view the reconstruction of CAD models in the boundary representation (B-Rep) as the detection of geometric primitives of different orders, i.e. vertices, edges and surface patches, and the correspondence of primitives, which are holistically modeled as a chain complex, and show that by modeling such comprehensive structures more complete and regularized reconstructions can be achieved. We solve the complex generation problem in two steps. First, we propose a novel neural framework that consists of a sparse CNN encoder for input point cloud processing and a tri-path transformer decoder for generating geometric primitives and their mutual relationships with estimated probabilities. Second, given the probabilistic structure predicted by the neural network, we recover a definite B-Rep chain complex by solving a global optimization maximizing the likelihood under structural validness constraints and applying geometric refinements. Extensive tests on large scale CAD datasets demonstrate that the modeling of B-Rep chain complex structure enables more accurate detection for learning and more constrained reconstruction for optimization, leading to structurally more faithful and complete CAD B-Rep models than previous results.

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