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DepthComp: Real-time Depth Image Completion Based on Prior Semantic Scene Segmentation

Atapour-Abarghouei, A.; Breckon, T.P.

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Abstract

We address plausible hole filling in depth images in a computationally lightweight methodology that leverages recent advances in semantic scene segmentation. Firstly, we perform such segmentation over a co-registered color image, commonly available from stereo depth sources, and non-parametrically fill missing depth values based on a multipass basis within each semantically labeled scene object. Within this formulation, we identify a bounded set of explicit completion cases in a grammar inspired context that can be performed effectively and efficiently to provide highly plausible localized depth continuity via a case-specific non-parametric completion approach. Results demonstrate that this approach has complexity and efficiency comparable to conventional interpolation techniques but with accuracy analogous to contemporary depth filling approaches. Furthermore, we show it to be capable of fine depth relief completion beyond that of both contemporary approaches in the field and computationally comparable interpolation strategies.

Citation

Atapour-Abarghouei, A., & Breckon, T. (2017). DepthComp: Real-time Depth Image Completion Based on Prior Semantic Scene Segmentation. In Proc. British Machine Vision Conference (208.1-208.13). https://doi.org/10.5244/C.31.58

Conference Name 28th British Machine Vision Conference (BMVC) 2017
Conference Location London, UK
Start Date Sep 4, 2017
End Date Sep 7, 2017
Acceptance Date Jul 1, 2017
Online Publication Date Sep 4, 2017
Publication Date 2017
Deposit Date Jul 20, 2017
Publicly Available Date Mar 28, 2024
Pages 208.1-208.13
Book Title Proc. British Machine Vision Conference
DOI https://doi.org/10.5244/C.31.58
Keywords depth filling, RGB-D, surface relief, hole filling, surface completion, 3D texture, depth completion, depth map, disparity hole filling
Public URL https://durham-repository.worktribe.com/output/1145974
Publisher URL https://breckon.org/toby/publications/papers/abarghouei17depthcomp.pdf

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Copyright Statement
© 2017. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.





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