2021/06/20 by Yijie Wu, Wu, Yijie, Fan Xue +1
Computer Science · Earth and Planetary Sciences · Engineering · Environmental Science · #3D Surveying and Cultural Heritage #Infrastructure Maintenance and Monitoring #Remote Sensing and LiDAR Applications #cs.CV
paper · pdf · doi:10.48550/arxiv.2106.10635
arxiv created 2021/06/20 · arxiv updated 2021/06/22
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.