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Deep Learning for Scene Flow Estimation on Point Clouds: A Survey and Prospective Trends.

Liu, Z., Xiang, N., Chen, H., Zhang, J. and Yang, X., 2023. Deep Learning for Scene Flow Estimation on Point Clouds: A Survey and Prospective Trends. Computer Graphics Forum. (In Press)

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Computer Graphics Forum - 2023 - Li - Deep Learning for Scene Flow Estimation on Point Clouds A Survey and Prospective.pdf - Published Version
Available under License Creative Commons Attribution.


DOI: 10.1111/cgf.14795


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.

Item Type:Article
Uncontrolled Keywords:3D scene flow; literature survey
Group:Faculty of Media & Communication
ID Code:38465
Deposited By: Symplectic RT2
Deposited On:20 Apr 2023 11:15
Last Modified:20 Apr 2023 11:15


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