Depth-map completion for large indoor scene reconstruction

Abstract

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.