OmniMVS: End-to-end learning for omnidirectional stereo matching
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Won, Changhee | - |
dc.contributor.author | Ryu, Jongbin | - |
dc.contributor.author | Lim, Jongwoo | - |
dc.date.accessioned | 2022-07-09T00:39:13Z | - |
dc.date.available | 2022-07-09T00:39:13Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2019-11 | - |
dc.identifier.issn | 1550-5499 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/146814 | - |
dc.description.abstract | In this paper, we propose a novel end-to-end deep neural network model for omnidirectional depth estimation from a wide-baseline multi-view stereo setup. The images captured with ultra wide field-of-view (FOV) cameras on an omnidirectional rig are processed by the feature extraction module, and then the deep feature maps are warped onto the concentric spheres swept through all candidate depths using the calibrated camera parameters. The 3D encoder-decoder block takes the aligned feature volume to produce the omnidirectional depth estimate with regularization on uncertain regions utilizing the global context information. In addition, we present large-scale synthetic datasets for training and testing omnidirectional multi-view stereo algorithms. Our datasets consist of 11K ground-truth depth maps and 45K fisheye images in four orthogonal directions with various objects and environments. Experimental results show that the proposed method generates excellent results in both synthetic and real-world environments, and it outperforms the prior art and the omnidirectional versions of the state-of-the-art conventional stereo algorithms. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | OmniMVS: End-to-end learning for omnidirectional stereo matching | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lim, Jongwoo | - |
dc.identifier.doi | 10.1109/ICCV.2019.00908 | - |
dc.identifier.scopusid | 2-s2.0-85081931236 | - |
dc.identifier.wosid | 000548549204011 | - |
dc.identifier.bibliographicCitation | Proceedings of the IEEE International Conference on Computer Vision, v.2019-October, pp.8986 - 8995 | - |
dc.relation.isPartOf | Proceedings of the IEEE International Conference on Computer Vision | - |
dc.citation.title | Proceedings of the IEEE International Conference on Computer Vision | - |
dc.citation.volume | 2019-October | - |
dc.citation.startPage | 8986 | - |
dc.citation.endPage | 8995 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | Cameras | - |
dc.subject.keywordPlus | Computer vision | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Deep neural networks | - |
dc.subject.keywordPlus | Large dataset | - |
dc.subject.keywordPlus | Calibrated cameras | - |
dc.subject.keywordPlus | Concentric spheres | - |
dc.subject.keywordPlus | Neural network model | - |
dc.subject.keywordPlus | Orthogonal directions | - |
dc.subject.keywordPlus | Real world environments | - |
dc.subject.keywordPlus | Stereo algorithms | - |
dc.subject.keywordPlus | Synthetic datasets | - |
dc.subject.keywordPlus | Training and testing | - |
dc.subject.keywordPlus | Stereo image processing | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9010863 | - |
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