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.

综合建模几何原语的链复形，可精确重建 CAD B-Rep 模型，提出一个新的神经网络框架来推测几何原语之间的关系，并通过全局最大化似然性和几何优化方法恢复一个确定的 B-Rep 链复形，实验表明，该模型比以前的结果更加准确和完整