NeurCADRecon: Neural Representation for Reconstructing CAD Surfaces by Enforcing Zero Gaussian Curvature

TL;DR

NeurCADRecon is a self-supervised neural method that reconstructs high-fidelity CAD surfaces from low-quality point clouds by enforcing zero Gaussian curvature, effectively capturing sharp features and developable patches.

Contribution

The paper introduces a novel neural SDF approach with a developability loss and dynamic sampling for accurate CAD surface reconstruction from sparse point clouds.

Findings

Outperforms existing methods in CAD shape fidelity

Effectively captures sharp features and developable patches

Handles incomplete and sparse point cloud data

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.