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

Zhiqi Li, Nan Xiang, Honghua Chen, Jianjun Zhang, Xiaosong Yang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Article numbere14795
JournalComputer Graphics Forum
Volume42
Issue number6
DOIs
Publication statusPublished - Sept 2023

Keywords

  • 3D scene flow
  • literature survey

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