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Conference Paper: P 2 Net: Patch-Match and Plane-Regularization for Unsupervised Indoor Depth Estimation
Title | P <sup>2</sup> Net: Patch-Match and Plane-Regularization for Unsupervised Indoor Depth Estimation |
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Authors | |
Keywords | Multiview photometric consistency Patch-based representation Piece-wise planar loss Unsupervised depth estimation |
Issue Date | 2020 |
Citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, v. 12369 LNCS, p. 206-222 How to Cite? |
Abstract | This paper tackles the unsupervised depth estimation task in indoor environments. The task is extremely challenging because of the vast areas of non-texture regions in these scenes. These areas could overwhelm the optimization process in the commonly used unsupervised depth estimation framework proposed for outdoor environments. However, even when those regions are masked out, the performance is still unsatisfactory. In this paper, we argue that the poor performance suffers from the non-discriminative point-based matching. To this end, we propose P2Net. We first extract points with large local gradients and adopt patches centered at each point as its representation. Multiview consistency loss is then defined over patches. This operation significantly improves the robustness of the network training. Furthermore, because those textureless regions in indoor scenes (e.g., wall, floor, roof, etc.) usually correspond to planar regions, we propose to leverage superpixels as a plane prior. We enforce the predicted depth to be well fitted by a plane within each superpixel. Extensive experiments on NYUv2 and ScanNet show that our P2Net outperforms existing approaches by a large margin. |
Persistent Identifier | http://hdl.handle.net/10722/345126 |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
DC Field | Value | Language |
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dc.contributor.author | Yu, Zehao | - |
dc.contributor.author | Jin, Lei | - |
dc.contributor.author | Gao, Shenghua | - |
dc.date.accessioned | 2024-08-15T09:25:25Z | - |
dc.date.available | 2024-08-15T09:25:25Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, v. 12369 LNCS, p. 206-222 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/345126 | - |
dc.description.abstract | This paper tackles the unsupervised depth estimation task in indoor environments. The task is extremely challenging because of the vast areas of non-texture regions in these scenes. These areas could overwhelm the optimization process in the commonly used unsupervised depth estimation framework proposed for outdoor environments. However, even when those regions are masked out, the performance is still unsatisfactory. In this paper, we argue that the poor performance suffers from the non-discriminative point-based matching. To this end, we propose P2Net. We first extract points with large local gradients and adopt patches centered at each point as its representation. Multiview consistency loss is then defined over patches. This operation significantly improves the robustness of the network training. Furthermore, because those textureless regions in indoor scenes (e.g., wall, floor, roof, etc.) usually correspond to planar regions, we propose to leverage superpixels as a plane prior. We enforce the predicted depth to be well fitted by a plane within each superpixel. Extensive experiments on NYUv2 and ScanNet show that our P2Net outperforms existing approaches by a large margin. | - |
dc.language | eng | - |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.subject | Multiview photometric consistency | - |
dc.subject | Patch-based representation | - |
dc.subject | Piece-wise planar loss | - |
dc.subject | Unsupervised depth estimation | - |
dc.title | P <sup>2</sup> Net: Patch-Match and Plane-Regularization for Unsupervised Indoor Depth Estimation | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-030-58586-0_13 | - |
dc.identifier.scopus | eid_2-s2.0-85097653732 | - |
dc.identifier.volume | 12369 LNCS | - |
dc.identifier.spage | 206 | - |
dc.identifier.epage | 222 | - |
dc.identifier.eissn | 1611-3349 | - |