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

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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 46% reduction in noise of reconstructed planar surfaces.

4013 Geomatic Engineering, 46 Information and Computing Sciences, 40 Engineering
Journal Title
Proceedings of the 2022 European Conference on Computing in Construction
Conference Name
2022 European Conference on Computing in Construction
Journal ISSN
Volume Title
University of Turin
EPSRC (EP/V056441/1)