Proceedings of the 2022 European Conference on Computing in Construction


Precise and dense AI-based mobile 3D reconstruction of indoor scenes by camera-lidar fusion and odometry

Maciej Trzeciak, Ioannis Brilakis
University of Cambridge, United Kingdom

DOI: 10.35490/EC3.2022.150
Abstract: We propose a mobile 3D reconstruction method for improving the precision and density of point clouds. It is suitable for hand-held scanners comprised of a colour camera and a lidar. We fuse time-synchronized and spatially registered images and lidar sweeps using deep learning techniques into dense scans, which are then used for progressive reconstruction in an odometry-like manner. We build a prototypic scanner and test our method in an indoor case-study. The results show that our pipeline outperforms reconstructions by other devices and methods, yielding relatively denser and detail-preserving point clouds with a 67% reduction in noise of reconstructed planar surfaces.
Keywords: mobile 3D reconstruction, scanning, odometry, camera-lidar fusion, artificial intelligence

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