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Scene-level Point Cloud Colorization with Semantics-and-geometry-aware Networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Gao, Rongrong | - |
dc.contributor.author | Xiang, Tian-Zhu | - |
dc.contributor.author | Lei, Chenyang | - |
dc.contributor.author | Park, Jaesik | - |
dc.contributor.author | Chen, Qifeng | - |
dc.date.accessioned | 2024-05-08T07:28:35Z | - |
dc.date.available | 2024-05-08T07:28:35Z | - |
dc.date.created | 2024-05-08 | - |
dc.date.created | 2024-05-08 | - |
dc.date.created | 2024-05-08 | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Proceedings - IEEE International Conference on Robotics and Automation, Vol.2023-May, pp.2818-2824 | - |
dc.identifier.issn | 1050-4729 | - |
dc.identifier.uri | https://hdl.handle.net/10371/201220 | - |
dc.description.abstract | In robotic applications, we often obtain tons of 3D point cloud data without color information, and it is difficult to visualize point clouds in a meaningful and colorful way. Can we colorize 3D point clouds for better visualization? Existing deep learning-based colorization methods usually only take simple 3D objects as input, and their performance for complex scenes with multiple objects is limited. To this end, this paper proposes a novel semantics-and-geometry-aware colorization network, termed SGNet, for vivid scene-level point cloud colorization. Specifically, we propose a novel pipeline that explores geometric and semantic cues from point clouds containing only coordinates for color prediction. We also design two novel losses, including a colorfulness metric loss and a pairwise consistency loss, to constrain model training for genuine colorization. To the best of our knowledge, our work is the first to generate realistic colors for point clouds of large-scale indoor scenes. Extensive experiments on the widely used ScanNet benchmarks demonstrate that the proposed method achieves state-of-the-art performance on point cloud colorization. | - |
dc.language | 영어 | - |
dc.publisher | IEEE | - |
dc.title | Scene-level Point Cloud Colorization with Semantics-and-geometry-aware Networks | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICRA48891.2023.10161469 | - |
dc.citation.journaltitle | Proceedings - IEEE International Conference on Robotics and Automation | - |
dc.identifier.wosid | 001036713002031 | - |
dc.identifier.scopusid | 2-s2.0-85168672959 | - |
dc.citation.endpage | 2824 | - |
dc.citation.startpage | 2818 | - |
dc.citation.volume | 2023-May | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Park, Jaesik | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
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