Deep Learning for Scene Flow Estimation on Point Clouds: A Survey and Prospective Trends
Authors: Li, Z., Xiang, N., Chen, H., Zhang, J. and Yang, X.
Journal: Computer Graphics Forum
Volume: 42
Issue: 6
eISSN: 1467-8659
ISSN: 0167-7055
DOI: 10.1111/cgf.14795
Abstract: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.
https://eprints.bournemouth.ac.uk/38465/
Source: Scopus
Deep Learning for Scene Flow Estimation on Point Clouds: A Survey and Prospective Trends
Authors: Li, Z., Xiang, N., Chen, H., Zhang, J. and Yang, X.
Journal: COMPUTER GRAPHICS FORUM
Volume: 42
Issue: 6
eISSN: 1467-8659
ISSN: 0167-7055
DOI: 10.1111/cgf.14795
https://eprints.bournemouth.ac.uk/38465/
Source: Web of Science (Lite)
Deep Learning for Scene Flow Estimation on Point Clouds: A Survey and Prospective Trends
Authors: Liu, Z., Xiang, N., Chen, H., Zhang, J. and Yang, X.
Journal: Computer Graphics Forum
ISSN: 0167-7055
Abstract: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.
https://eprints.bournemouth.ac.uk/38465/
Source: BURO EPrints