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

Lookup NU author(s): Dr Amir Atapour AbarghoueiORCiD



This is the final published version of a conference proceedings (inc. abstract) that has been published in its final definitive form by BMVA, 2017.

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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.

Publication metadata

Author(s): Atapour-Abarghouei A, Breckon TP

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: British Machine Vision Conference (BMVC 2017)

Year of Conference: 2017

Online publication date: 04/09/2017

Acceptance date: 10/07/2017

Date deposited: 06/02/2021

Publisher: BMVA


DOI: 10.5244/C.31.58