Automatic region-growing system for the segmentation of large point clouds: Fid-Bau Portal

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

Poux, F. / Mattes, C. / Selman, Z. / Kobbelt, L.

Abstract 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/.

Highlights • Briefly, this paper makes the following three main contributions. • A new planar-based region growing segmentation system for massive 3D point clouds • An unsupervised heuristic to automatically set optimal parameters. • A substantial open-source labelled point cloud dataset incl. instances and classes.