  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.
