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

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Název: CGHP: Component-Guided Hierarchical Progressive Point Cloud Unsupervised Segmentation Framework.
Autoři: Shi, Shuo1,2,3 (AUTHOR), Zhao, Haifeng1,2 (AUTHOR) zhaohaifeng@whu.edu.cn, Gong, Wei1,2,3 (AUTHOR), Bi, Sifu1 (AUTHOR)
Zdroj: Remote Sensing. Nov2025, Vol. 17 Issue 21, p3589. 22p.
Témata: *POINT cloud, *HIERARCHICAL clustering (Cluster analysis), *DESCRIPTOR systems, *IMAGE segmentation, *DEEP learning, *LIDAR
Abstrakt: Highlights: What are the main findings? We propose CGHP, a component-guided hierarchical progressive framework that achieves unsupervised 3D point cloud segmentation without any manual annotations, 2D data, or pre-trained models. The framework integrates component-level descriptors with an adjacency-constrained progressive clustering strategy, enabling effective transition from component- to object-level semantics and achieving competitive results on benchmark datasets. What is the implication of the main finding? The results demonstrate that structured priors and hierarchical learning can substantially enhance unsupervised 3D semantic segmentation, effectively narrowing the gap with supervised methods. The framework provides a scalable solution for processing massive airborne and terrestrial point clouds, enabling broader applications in smart city development and scene understanding. 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. [ABSTRACT FROM AUTHOR]
Databáze: Academic Search Index
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Abstrakt:Highlights: What are the main findings? We propose CGHP, a component-guided hierarchical progressive framework that achieves unsupervised 3D point cloud segmentation without any manual annotations, 2D data, or pre-trained models. The framework integrates component-level descriptors with an adjacency-constrained progressive clustering strategy, enabling effective transition from component- to object-level semantics and achieving competitive results on benchmark datasets. What is the implication of the main finding? The results demonstrate that structured priors and hierarchical learning can substantially enhance unsupervised 3D semantic segmentation, effectively narrowing the gap with supervised methods. The framework provides a scalable solution for processing massive airborne and terrestrial point clouds, enabling broader applications in smart city development and scene understanding. 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. [ABSTRACT FROM AUTHOR]
ISSN:20724292
DOI:10.3390/rs17213589