SFD-Net: Sharp Feature Detection Network Based on Local Geometric Features

Reliable detection of sharp features in point clouds—loci where the surface-normal field is discontinuous—remains challenging under increased local density variation and noise. SFD-Net couples a compact multi-scale Local Geometric Descriptor (LGD) with an enhanced PointNet++ backbone. LGD aggregates second-moment statistics of surface normal differences at three nested neighborhood scales, yielding rigid-motionand scale-invariant cues that remain stable under joint noise and spacing variability. On the ABC benchmark, SFD-Net achieves state-of-the-art F1-score and competitive False Positive Rate (FPR) across four noise conditions under a consistent evaluation setup. LGD further improves competitive detectors in a plug-and-play manner without architectural changes, and transfer to Stanford Large-Scale 3D Indoor Spaces (S3DIS) confirms generalization to real scans. Downstream applications potentially benefit from more reliable feature localization. Our source code and pretrained models are publicly available at https: //github.com/inyoungoh-cde/SFD-Net.