NeurCADRecon: Neural Representation for Reconstructing CAD Surfaces by Enforcing Zero Gaussian Curvature
<|reference_start|>NeurCADRecon: Neural Representation for Reconstructing CAD Surfaces by Enforcing Zero Gaussian Curvature: 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.<|reference_end|>
@article{dong2024neurcadrecon:,
    title={NeurCADRecon: Neural Representation for Reconstructing CAD Surfaces by
  Enforcing Zero Gaussian Curvature},
    author={Qiujie Dong, Rui Xu, Pengfei Wang, Shuangmin Chen, Shiqing Xin,
  Xiaohong Jia, Wenping Wang, Changhe Tu},
    journal={arXiv preprint arXiv:2404.13420},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13420},
    primaryClass={cs.CV}
}