PointDC: Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-Modal Distillation and Super-Voxel Clustering

Zisheng Chen, Hongbin Xu, Weitao Chen, Zhipeng Zhou, Haihong Xiao, Baigui Sun, Xuansong Xie, Wenxiong kang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 14290-14299

Semantic segmentation of point clouds usually requires exhausting efforts of human annotations, hence it attracts wide attention to a challenging topic of learning from unlabeled or weaker form of annotations. In this paper, we take the first attempt for fully unsupervised semantic segmentation of point clouds, which aims to delineate semantically meaningful objects without any form of annotations. Previous works of unsupervised pipeline on 2D images fails in this task of point clouds, due to: 1) Clustering Ambiguity caused by limited magnitude of data and imbalanced class distribution; 2) Irregularity Ambiguity caused by the irregular sparsity of point cloud. Therefore, we propose a novel framework, PointDC, which is comprised of two steps that handles the aforementioned problems respectively: Cross-Modal Distillation (CVD) and Super-Voxel Clustering (SVC). In the first stage of CVD, multi-view visual features are back-projected to the 3D space and aggregated to a unified point feature to distill the training of the point representation. In the second stage of SVC, the point features are aggregated to super-voxels and then fed to the iterative clustering process for excavating semantic classes. PointDC yields a significant improvement over the prior state-of-the-art unsupervised methods, on both the ScanNet v2 (+18.4 mIOU) and S3DIS (+11.5 mIOU) semantic segmentation benchmarks.