Multi scale spatial clustering improves 3d point cloud segmentation accuracy
MSSP: Multi-Scale Spatially-Constrained Partition for Unsupervised Semantic Segmentation of 3D Point Clouds
Computer Vision and Pattern RecognitionArtificial Intelligence
Summary
Understanding what parts of a 3D space represent can be tricky, especially when you don't want to label every bit manually. The authors propose a new way to group small regions of 3D data at different scales and ensure that only physically connected areas get combined. This method helps capture complex structures more accurately without needing any human-labeled examples. Their tests show it performs better than previous unsupervised methods, especially in indoor scenes.
What this means in practice
- •For robotics engineers: Improve robots’ ability to map and understand indoor environments without needing costly labeled training data by using multi-scale spatially constrained point cloud segmentation.
- •For autonomous vehicle developers: Enhance perception systems for indoor autonomous robots to better distinguish objects and spaces using unsupervised multi-scale segmentation of 3D sensor data.
Authors
Zhenghao Zhang, Xinjie Wang, Wei Wang, Jun Zhang, Hanyun Wang
Abstract
3D point cloud semantic segmentation is essential for real-world spatial understanding, yet the prohibitive cost of human annotations motivates unsupervised approaches that require no labels. Existing superpoint-based methods typically rely on spectral analysis at a fixed granularity, failing to capture the hierarchical semantic structures inherent in complex indoor scenes. To bridge this gap, we present a Multi-Scale Spatially-Constrained Partition (MSSP) framework that combines multi-scale spectral analysis with spatially-constrained clustering. Multi-scale spectral analysis constructs enriched superpoint descriptors across multiple clustering granularities; however, the resulting high-dimensional feature space calls for a structural prior to translate into cleaner segmentation. Spatially-constrained clustering supplies this prior by restricting superpoint merging to physically adjacent regions, imposing the spatial coherence needed for multi-scale features to be effective. Extensive experiments on S3DIS and ScanNet show that MSSP achieves the best mIoU among unsupervised methods on the main benchmarks, with particularly significant gains on S3DIS. Notably, our ablation reveals a regularize-then-enrich interaction: multi-scale features alone do not improve final segmentation, yet become highly effective when coupled with spatial regularization, underscoring that spatial coherence is aprerequisite for multi-scale representations in superpoint clustering.