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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 modelingsuch comprehensive structures more complete and regularized reconstructions canbe achieved. We solve the complex generation problem in two steps. First, wepropose a novel neural framework that consists of a sparse CNN encoder forinput point cloud processing and a tri-path transformer decoder for generatinggeometric primitives and their mutual relationships with estimatedprobabilities. Second, given the probabilistic structure predicted by theneural network, we recover a definite B-Rep chain complex by solving a globaloptimization maximizing the likelihood under structural validness constraintsand applying geometric refinements. Extensive tests on large scale CAD datasetsdemonstrate that the modeling of B-Rep chain complex structure enables moreaccurate detection for learning and more constrained reconstruction foroptimization, leading to structurally more faithful and complete CAD B-Repmodels than previous results.

ComplexGen: CAD Reconstruction by B-Rep Chain Complex Generation