Abstract
Despite recent advances in reconstructing an organic model with the neural signed distance function (SDF), the high-fidelity reconstruction of a CAD model directly from low-quality unoriented point clouds remains a significant challenge. In this paper, we address this challenge based on the prior observation that the surface of a CAD model is generally composed of piecewise surface patches, each approximately developable even around the feature line. Our approach, named NeurCADRecon, is self-supervised, and its loss includes a developability term to encourage the Gaussian curvature toward 0 while ensuring fidelity to the input points. Noticing that the Gaussian curvature is non-zero at tip points, we introduce a double-trough curve to tolerate the existence of these tip points. Furthermore, we develop a dynamic sampling strategy to deal with situations where the given points are incomplete or too sparse. Since our resulting neural SDFs can clearly manifest sharp feature points/lines, one can easily extract the feature-aligned triangle mesh from the SDF and then decompose it into smooth surface patches, greatly reducing the difficulty of recovering the parametric CAD design. A comprehensive comparison with existing state-of-the-art methods shows the significant advantage of our approach in reconstructing faithful CAD shapes.
Abstract (translated)
尽管在最近，使用带神经签名距离函数（SDF）重构有机模型取得了进展，但直接从低质量无方向点云中重构高级别CAD模型仍然是一个重要的挑战。在本文中，我们根据先前的观察，即CAD模型的表面通常由局部表面补丁组成，每个补丁都可以在特征线附近开发，来解决这个问题。我们的方法称为NeurCADRecon，是一种自监督的方法，其损失包括一个开发性项，以鼓励高斯曲线向0发展，同时确保对输入点的忠实性。注意到在尖点处高斯曲线不为零，我们引入了一个双孔曲线来容忍这些尖点存在。此外，我们还开发了一种动态采样策略来处理输入点不完整或过于稀疏的情况。由于我们得到的神经SDF可以明显地表现出尖点/线，因此可以很容易地从SDF中提取特征对齐的三角形网格，然后将其分解成平滑的表面补丁，从而大大减少了从参数化CAD设计中恢复的难度。与现有最先进的方法进行全面的比较表明，我们方法在重构忠实CAD形状方面具有显著优势。
URL
https://arxiv.org/abs/2404.13420
