Semantic Segmentation for 3D Point Clouds with Curvature-Aware Sampling and Inverse-Density Weighting

Abstract

Point cloud classification and part segmentation require effective downsampling methods that reduce computational cost while preserving object boundaries, thin structures, and other informative geometric regions. However, raw point clouds are often large, irregular, and non-uniformly distributed, making efficient downsampling essential. This paper proposes the Curvature-Aware Sampling Network (CASNet), a feature-preserving framework that combines curvature-aware sampling, inverse-density weighting, attention pooling, and residual blocks. CASNet estimates local curvature from neighborhood covariance and converts curvature scores into sampling probabilities, increasing the likelihood of retaining geometrically informative points. Inverse-density weighting further reduces redundant selection in dense regions and improves the representation of sparse but structurally important areas. Although motivated by point-wise semantic prediction, the proposed sampling and aggregation modules are task-independent. Accordingly, ModelNet40 classification evaluates global geometric discrimination, ShapeNet Part provides direct point-wise segmentation evaluation, and Semantic3D and S3DIS examine sampling behavior in complex scenes. Under the controlled hierarchical abstraction backbone, the CAS + IDW sampling configuration achieves 92.26% instance accuracy on ModelNet40 and 85.41% instance-average mIoU on ShapeNet Part. In the architecture-level evaluation, the complete CASNet obtains 94.30% overall accuracy and 91.70% mean class accuracy on ModelNet40, together with 85.29% class-average mIoU and 87.61% instance-average mIoU on ShapeNet Part. With only 64 retained points, CAS + IDW achieves 53.86% classification accuracy, compared with 53.65% for curvature-informed FPS, 53.37% for SAMBLE, 51.92% for FPS, and 32.64% for random sampling. In the controlled sampling experiment, CAS + IDW requires 90 s per training epoch and 49 ms per sample for inference, compared with 103 s and 59 ms for the FPS-based counterpart, respectively. These results demonstrate that CASNet maintains competitive predictive performance while providing favorable computational efficiency.