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Depth-map Completion for Large Indoor Scene Reconstruction
Nov 23, 2021Author:
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Liu, Hongmin; Tang, Xincheng; Shen, Shuhan


Traditional Multi View Stereo (MVS) algorithms are often difficult to deal with large-scale indoor scene reconstruction, due to the photo-consistency measurement errors in weak textured regions, which are commonly exist in indoor scenes. To solve this limitation, in this paper we proposed a point cloud completion strategy that combines learning-based depth-map completion and geometry-based consistency filtering to fill large-area missing in depth-maps. The proposed method takes nonuniform and noisy MVS depth-map as input, and completes each depth-map individually. In the completion process, we first complete depth-maps using learning based method, and then filter each depth-map using depth consistency validation with its neighboring depth-maps. This depth-map completion and geometric filtering steps are performed iteratively until the number of depth points is converged. Experiments on large-scale indoor scenes and benchmark MVS datasets demonstrate the effectiveness of the proposed methods.


Pattern Recognition

Research Area

Computer Science, Artificial Intelligence, Engineering, Electrical & Electronic

DOI: 10.1016/j.patcog.2019.107112