Deep learning for scene flow estimation on point clouds: a survey and prospective trends
Aiming at obtaining structural information and 3D motion of dynamic scenes, scene flow estimation has been an interest of research in computer vision and computer graphics for a long time. It is also a fundamental task for various applications such as autonomous driving. Compared to previous methods...
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sg-ntu-dr.10356-1722112023-12-01T15:36:03Z Deep learning for scene flow estimation on point clouds: a survey and prospective trends Li, Zhiqi Xiang, Nan Chen, Honghua Zhang, Jianjun Yang, Xiaosong School of Computer Science and Engineering Engineering::Computer science and engineering 3D Scene Flow Literature Survey Aiming at obtaining structural information and 3D motion of dynamic scenes, scene flow estimation has been an interest of research in computer vision and computer graphics for a long time. It is also a fundamental task for various applications such as autonomous driving. Compared to previous methods that utilize image representations, many recent researches build upon the power of deep analysis and focus on point clouds representation to conduct 3D flow estimation. This paper comprehensively reviews the pioneering literature in scene flow estimation based on point clouds. Meanwhile, it delves into detail in learning paradigms and presents insightful comparisons between the state-of-the-art methods using deep learning for scene flow estimation. Furthermore, this paper investigates various higher-level scene understanding tasks, including object tracking, motion segmentation, etc. and concludes with an overview of foreseeable research trends for scene flow estimation. Published version 2023-11-29T05:43:29Z 2023-11-29T05:43:29Z 2023 Journal Article Li, Z., Xiang, N., Chen, H., Zhang, J. & Yang, X. (2023). Deep learning for scene flow estimation on point clouds: a survey and prospective trends. Computer Graphics Forum, 42(6), e14795-. https://dx.doi.org/10.1111/cgf.14795 0167-7055 https://hdl.handle.net/10356/172211 10.1111/cgf.14795 2-s2.0-85152089081 6 42 e14795 en Computer Graphics Forum © 2023 The Authors. Computer Graphics Forum published by Eurographics - The European Association for Computer Graphics and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. application/pdf |
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Engineering::Computer science and engineering 3D Scene Flow Literature Survey Li, Zhiqi Xiang, Nan Chen, Honghua Zhang, Jianjun Yang, Xiaosong Deep learning for scene flow estimation on point clouds: a survey and prospective trends |
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Aiming at obtaining structural information and 3D motion of dynamic scenes, scene flow estimation has been an interest of research in computer vision and computer graphics for a long time. It is also a fundamental task for various applications such as autonomous driving. Compared to previous methods that utilize image representations, many recent researches build upon the power of deep analysis and focus on point clouds representation to conduct 3D flow estimation. This paper comprehensively reviews the pioneering literature in scene flow estimation based on point clouds. Meanwhile, it delves into detail in learning paradigms and presents insightful comparisons between the state-of-the-art methods using deep learning for scene flow estimation. Furthermore, this paper investigates various higher-level scene understanding tasks, including object tracking, motion segmentation, etc. and concludes with an overview of foreseeable research trends for scene flow estimation. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Li, Zhiqi Xiang, Nan Chen, Honghua Zhang, Jianjun Yang, Xiaosong |
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Article |
author |
Li, Zhiqi Xiang, Nan Chen, Honghua Zhang, Jianjun Yang, Xiaosong |
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Li, Zhiqi |
title |
Deep learning for scene flow estimation on point clouds: a survey and prospective trends |
title_short |
Deep learning for scene flow estimation on point clouds: a survey and prospective trends |
title_full |
Deep learning for scene flow estimation on point clouds: a survey and prospective trends |
title_fullStr |
Deep learning for scene flow estimation on point clouds: a survey and prospective trends |
title_full_unstemmed |
Deep learning for scene flow estimation on point clouds: a survey and prospective trends |
title_sort |
deep learning for scene flow estimation on point clouds: a survey and prospective trends |
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2023 |
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https://hdl.handle.net/10356/172211 |
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1784855605686566912 |