Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark

2026-08-07

AI Summary

This work addresses the challenges of limited surface coverage, uneven point density, and severe class imbalance between planar and non-planar components in single LiDAR scans of architectural scenes. To this end, the authors propose an incidence-angle-aware geometric normalization sampling strategy that treats sampling as an active component of 3D segmentation. Under a fixed point budget, points are mapped into a normalized manifold space for voxel selection while preserving their original Euclidean coordinates for downstream learning. The method requires only point coordinates and normals and does not necessitate modifications to the network backbone. Evaluated on the SIP benchmark, it significantly improves average segmentation performance—particularly for non-planar structures such as ladders—and reduces sensitivity to sampling resolution.

Abstract

3D scene understanding is increasingly important in construction, yet most methods are developed on curated datasets that do not fully reflect real site sensing conditions. In many workflows, individual LiDAR scans provide rapid local updates rather than complete scene representations, producing limited surface coverage, acquisition-driven density variation, and severe imbalance between dominant planar surfaces and sparse construction elements. Because large point clouds must be downsampled, sampling resolution and point allocation directly affect the balance between geometric detail and spatial context. This study evaluates these effects under a fixed per-fragment point budget and introduces an incidence-aware sampling strategy for individual LiDAR scans. The method maps points to a geometry-normalized manifold space for voxel-based selection while preserving original Euclidean coordinates for downstream learning. It requires only point coordinates and normals and no backbone modification. Using the Site in Pieces (SIP) benchmark, experiments with Point Transformer and PointNeXt show improved resolution-averaged segmentation performance, especially for non-planar elements and ladders, while reducing sensitivity to sampling resolution. The results show that acquisition-aware sampling can provide a more stable geometric representation and should be treated as an active component of individual-scan 3D segmentation rather than generic preprocessing.

Problem

• 3D segmentation
• LiDAR scans
• point cloud sampling
• class imbalance
• construction sites

Innovation

• incidence-aware sampling
• individual LiDAR scans
• 3D segmentation
• geometry-normalized manifold
• SIP benchmark