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Depth-map Completion for Large Indoor Scene Reconstruction
Hongmin Liu; Xincheng Tang; Shuhan Shen
Source PublicationPattern Recognition
SubtypeRegular Paper

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.

KeywordDepth completion MVS 3D reconstruction Point cloud
Document Type期刊论文
Corresponding AuthorShuhan Shen
Recommended Citation
GB/T 7714
Hongmin Liu,Xincheng Tang,Shuhan Shen. Depth-map Completion for Large Indoor Scene Reconstruction[J]. Pattern Recognition,2019(X):X.
APA Hongmin Liu,Xincheng Tang,&Shuhan Shen.(2019).Depth-map Completion for Large Indoor Scene Reconstruction.Pattern Recognition(X),X.
MLA Hongmin Liu,et al."Depth-map Completion for Large Indoor Scene Reconstruction".Pattern Recognition .X(2019):X.
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