Automatic region-growing system for the segmentation of large point clouds

Florent Poux, Christian Mattes, Zain Selman, Leif Kobbelt
Automation in Construction

This article describes a complete unsupervised system for the segmentation of massive 3D point clouds. Our system bridges the missing components that permit to go from 99% automation to 100% automation for the construction industry. It scales up to billions of 3D points and targets a generic low-level grouping of planar regions usable by a wide range of applications. Furthermore, we introduce a hierarchical multi-level segment definition to cope with potential variations in high-level object definitions. The approach first leverages planar predominance in scenes through a normal-based region growing. Then, for usability and simplicity, we designed an automatic heuristic to determine without user supervision three RANSAC-inspired parameters. These are the distance threshold for the region growing, the threshold for the minimum number of points needed to form a valid planar region, and the decision criterion for adding points to a region. Our experiments are conducted on 3D scans of complex buildings to test the robustness of the “one-click” method in varying scenarios. Labelled and instantiated point clouds from different sensors and platforms (depth sensor, terrestrial laser scanner, hand-held laser scanner, mobile mapping system), in different environments (indoor, outdoor, buildings) and with different objects of interests (AEC-related, BIM-related, navigation-related) are provided as a new extensive test-bench. The current implementation processes ten million points per minutes on a single thread CPU configuration. Moreover, the resulting segments are tested for the high-level task of semantic segmentation over 14 classes, to achieve an F1-score of 90+ averaged over all datasets while reducing the training phase to a fraction of state of the art point-based deep learning methods. We provide this baseline along with six new open-access datasets with 300+ million hand-labelled and instantiated 3D points at: https://www.graphics.rwth-aachen.de/project/ 45/.

» Show BibTeX

title = {Automatic region-growing system for the segmentation of large point clouds},
journal = {Automation in Construction},
volume = {138},
pages = {104250},
year = {2022},
issn = {0926-5805},
doi = {https://doi.org/10.1016/j.autcon.2022.104250},
url = {https://www.sciencedirect.com/science/article/pii/S0926580522001236},
author = {F. Poux and C. Mattes and Z. Selman and L. Kobbelt},
keywords = {3D point cloud, Segmentation, Region-growing, RANSAC, Unsupervised clustering}

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