<p><strong>Abstract.</strong> This work addresses the automatic reconstruction of objects useful for BIM, like walls, floors and ceilings, from meshed and textured mapped 3D point clouds of indoor scenes. For this reason, we focus on the semantic segmentation of 3D indoor meshes as the initial step for the automatic generation of BIM models. Our investigations are based on the benchmark dataset ScanNet, which aims at the interpretation of 3D indoor scenes. For this purpose it provides 3D meshed representations as collected from low cost range cameras. In our opinion such RGB-D data has a great potential for the automated reconstruction of BIM objects.</p>
Global Adaptive 4-Points Congruent Sets Registration for 3D Indoor Scenes With Robust Estimation