This paper presents a deep learning-based point cloud processing method named FloorPP-Net for the task of Scan-to-BIM (building information model). FloorPP-Net first converts the input point cloud of a building story into point pillars (PP), then predicts the corners and edges to output the floor plan. Altogether, FloorPP-Net establishes an end-to-end supervised learning framework for the Scan-to-Floor-Plan (Scan2FP) task. In the 1st International Scan-to-BIM Challenge held in conjunction with CVPR 2021, FloorPP-Net was ranked the second runner-up in the floor plan reconstruction track. Future work includes general edge proposals, 2D plan regularization, and 3D BIM reconstruction.
@article{arxiv.2106.10635,
title = {FloorPP-Net: Reconstructing Floor Plans using Point Pillars for Scan-to-BIM},
author = {Yijie Wu and Fan Xue},
journal= {arXiv preprint arXiv:2106.10635},
year = {2021}
}