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Floorplan generation from 3D point clouds: A space partitioning approach

Abstract : We propose a novel approach to automatically reconstruct the floorplan of indoor environments from raw sensor data. In contrast to existing methods that generate floorplans under the form of a planar graph by detecting corner points and connecting them, our framework employs a strategy that decomposes the space into a polygonal partition and selects edges that belong to wall structures by energy minimization. By relying on a efficient space-partitioning data structure instead of a traditional and delicate corner detection task, our framework offers a high robustness to imperfect data. We demonstrate the potential of our algorithm on both RGBD and Lidar points scanned from simple to complex scenes. Experimental results indicate that our method is competitive with respect to existing methods in terms of geometric accuracy and output simplicity.
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Contributor : Florent Lafarge Connect in order to contact the contributor
Submitted on : Wednesday, October 20, 2021 - 10:02:24 AM
Last modification on : Friday, July 8, 2022 - 10:08:12 AM
Long-term archiving on: : Friday, January 21, 2022 - 7:23:12 PM




Hao Fang, Florent Lafarge, Cihui Pan, Hui Huang. Floorplan generation from 3D point clouds: A space partitioning approach. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 175, pp.44-55. ⟨10.1016/j.isprsjprs.2021.02.012⟩. ⟨hal-03387916⟩



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