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- Publisher Website: 10.1007/978-3-030-01234-2_39
- Scopus: eid_2-s2.0-85055089718
- WOS: WOS:000594221500039
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Conference Paper: SegStereo: Exploiting semantic information for disparity estimation
Title | SegStereo: Exploiting semantic information for disparity estimation |
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Authors | |
Keywords | Softmax loss regularization Disparity estimation Semantic cues Semantic feature embedding |
Issue Date | 2018 |
Publisher | Springer. |
Citation | 15th European Conference on Computer Vision (ECCV 2018), Munich, Germany, 8-14 September 2018. In Ferrari, V, Hebert, M, Sminchisescu, C, Weiss, Y (Eds.), Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part VII, p. 660-676. Cham: Springer, 2018 How to Cite? |
Abstract | Disparity estimation for binocular stereo images finds a wide range of applications. Traditional algorithms may fail on featureless regions, which could be handled by high-level clues such as semantic segments. In this paper, we suggest that appropriate incorporation of semantic cues can greatly rectify prediction in commonly-used disparity estimation frameworks. Our method conducts semantic feature embedding and regularizes semantic cues as the loss term to improve learning disparity. Our unified model SegStereo employs semantic features from segmentation and introduces semantic softmax loss, which helps improve the prediction accuracy of disparity maps. The semantic cues work well in both unsupervised and supervised manners. SegStereo achieves state-of-the-art results on KITTI Stereo benchmark and produces decent prediction on both CityScapes and FlyingThings3D datasets. |
Persistent Identifier | http://hdl.handle.net/10722/303584 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
ISI Accession Number ID | |
Series/Report no. | Lecture Notes in Computer Science ; 11211 Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11211 |
DC Field | Value | Language |
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dc.contributor.author | Yang, Guorun | - |
dc.contributor.author | Zhao, Hengshuang | - |
dc.contributor.author | Shi, Jianping | - |
dc.contributor.author | Deng, Zhidong | - |
dc.contributor.author | Jia, Jiaya | - |
dc.date.accessioned | 2021-09-15T08:25:37Z | - |
dc.date.available | 2021-09-15T08:25:37Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | 15th European Conference on Computer Vision (ECCV 2018), Munich, Germany, 8-14 September 2018. In Ferrari, V, Hebert, M, Sminchisescu, C, Weiss, Y (Eds.), Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part VII, p. 660-676. Cham: Springer, 2018 | - |
dc.identifier.isbn | 9783030012335 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/303584 | - |
dc.description.abstract | Disparity estimation for binocular stereo images finds a wide range of applications. Traditional algorithms may fail on featureless regions, which could be handled by high-level clues such as semantic segments. In this paper, we suggest that appropriate incorporation of semantic cues can greatly rectify prediction in commonly-used disparity estimation frameworks. Our method conducts semantic feature embedding and regularizes semantic cues as the loss term to improve learning disparity. Our unified model SegStereo employs semantic features from segmentation and introduces semantic softmax loss, which helps improve the prediction accuracy of disparity maps. The semantic cues work well in both unsupervised and supervised manners. SegStereo achieves state-of-the-art results on KITTI Stereo benchmark and produces decent prediction on both CityScapes and FlyingThings3D datasets. | - |
dc.language | eng | - |
dc.publisher | Springer. | - |
dc.relation.ispartof | Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part VII | - |
dc.relation.ispartofseries | Lecture Notes in Computer Science ; 11211 | - |
dc.relation.ispartofseries | Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11211 | - |
dc.subject | Softmax loss regularization | - |
dc.subject | Disparity estimation | - |
dc.subject | Semantic cues | - |
dc.subject | Semantic feature embedding | - |
dc.title | SegStereo: Exploiting semantic information for disparity estimation | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-030-01234-2_39 | - |
dc.identifier.scopus | eid_2-s2.0-85055089718 | - |
dc.identifier.spage | 660 | - |
dc.identifier.epage | 676 | - |
dc.identifier.eissn | 1611-3349 | - |
dc.identifier.isi | WOS:000594221500039 | - |
dc.publisher.place | Cham | - |