Journal
ACM TRANSACTIONS ON GRAPHICS
Volume 41, Issue 4, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3528223.3530078
Keywords
CAD reconstruction; point cloud; B-Rep chain complex; transformer network; global optimization
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This study views the reconstruction of CAD models as the detection of geometric primitives and their correspondence, and proposes a novel neural network framework for more complete and regularized reconstructions. By solving a global optimization and applying geometric refinements, it achieves more accurate and complete CAD B-Rep models.
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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