CGHP: Component-Guided Hierarchical Progressive Point Cloud Unsupervised Segmentation Framework

With the rapid development of airborne LiDAR and photogrammetric techniques, massive amounts of high-resolution 3D point cloud data have become increasingly available. However, extracting meaningful semantic information from such unstructured and noisy point clouds remains a challenging task, particularly in the absence of manually annotated labels. We present CGHP, a novel component-guided hierarchical progressive framework that addresses this challenge through a two-stage learning approach. Our method first decomposes point clouds into components using geometric and appearance consistency, constructing comprehensive geometric-appearance descriptors that capture shape, scale, and gravity-aligned distribution information to guide initial feature learning. These component-level features then undergo progressive growth through an adjacency-constrained clustering algorithm that gradually merges components into object-level semantic clusters. Extensive experiments on publicly available point cloud datasets S3DIS and ScanNet++ datasets demonstrate the effectiveness of the proposed method. On the S3DIS dataset, our method achieves state-of-the-art performance, with 48.69% mIoU and 79.68% OA, without using any annotations, closely approaching the results of fully supervised PointNet++ (50.1% mIoU, 77.5% OA). On the more challenging ScanNet++ benchmark, our approach also demonstrates competitive performance in terms of both mAcc and mIoU.