DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Jinsun | ko |
dc.contributor.author | Joo, Kyungdon | ko |
dc.contributor.author | Hu, Zhe | ko |
dc.contributor.author | Liu, Chi-Kuei | ko |
dc.contributor.author | Kweon, In-So | ko |
dc.date.accessioned | 2020-12-16T07:10:49Z | - |
dc.date.available | 2020-12-16T07:10:49Z | - |
dc.date.created | 2020-12-01 | - |
dc.date.issued | 2020-08 | - |
dc.identifier.citation | European Conference on Computer Vision, ECCV 2020 | - |
dc.identifier.uri | http://hdl.handle.net/10203/278567 | - |
dc.description.abstract | In this paper, we propose a robust and efficient end-to-end non-local spatial propagation network for depth completion. The proposed network takes RGB and sparse depth images as inputs and estimates non-local neighbors and their affinities of each pixel, as well as an initial depth map with pixel-wise confidences. The initial depth prediction is then iteratively refined by its confidence and non-local spatial propagation procedure based on the predicted non-local neighbors and corresponding affinities. Unlike previous algorithms that utilize fixed-local neighbors, the proposed algorithm effectively avoids irrelevant local neighbors and concentrates on relevant non-local neighbors during propagation. In addition, we introduce a learnable affinity normalization to better learn the affinity combinations compared to conventional methods. The proposed algorithm is inherently robust to the mixed-depth problem on depth boundaries, which is one of the major issues for existing depth estimation/completion algorithms. Experimental results on indoor and outdoor datasets demonstrate that the proposed algorithm is superior to conventional algorithms in terms of depth completion accuracy and robustness to the mixed-depth problem. Our implementation is publicly available on the project page. | - |
dc.language | English | - |
dc.publisher | European Conference on Computer Vision | - |
dc.title | Non-Local Spatial Propagation Network for Depth Completion | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | European Conference on Computer Vision, ECCV 2020 | - |
dc.identifier.conferencecountry | EI | - |
dc.identifier.conferencelocation | Virtual | - |
dc.contributor.localauthor | Kweon, In-So | - |
dc.contributor.nonIdAuthor | Park, Jinsun | - |
dc.contributor.nonIdAuthor | Joo, Kyungdon | - |
dc.contributor.nonIdAuthor | Hu, Zhe | - |
dc.contributor.nonIdAuthor | Liu, Chi-Kuei | - |
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