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DepthComp : real-time depth image completion based on prior semantic scene segmentation.

Atapour-Abarghouei, A. and Breckon, T.P. (2017) 'DepthComp : real-time depth image completion based on prior semantic scene segmentation.', 28th British Machine Vision Conference (BMVC) 2017 London, UK, 4-7 September 2017.


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.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2017. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.
Date accepted:01 July 2017
Date deposited:21 July 2017
Date of first online publication:04 September 2017
Date first made open access:No date available

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