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Floorplan generation from 3D point clouds: A space partitioning approach
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DOI：10.1016/j.isprsjprs.2021.02.012.png)
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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.
Keyword:
Indoor scene
Point cloud
Floorplan reconstruction
Primitive detection
Integer programming
Markov random field
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