Deep Learning for Scene Flow Estimation on Point Clouds: A Survey and Prospective Trends
Deep Learning for Scene Flow Estimation on Point Clouds: A Survey and Prospective Trends
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DOI：
10.1111/cgf.14795
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发表时间：
2023-04
影响因子：
2.5
通讯作者：
Zhiqi Li-;Nan Xiang;Honghua Chen;Jian-Jun Zhang;Xiaosong Yang
中科院分区：
文献类型：
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作者：
Zhiqi Li-;Nan Xiang;Honghua Chen;Jian-Jun Zhang;Xiaosong Yang
为了获取动态场景的结构信息和三维运动信息，场景流估计一直是计算机视觉和计算机图形学的研究热点。这也是自动驾驶等各种应用的基础任务。与之前利用图像表示的方法相比，最近的许多研究都建立在深度分析的基础上，并专注于点云表示来进行3D流量估计。本文全面回顾了基于点云的场景流估计的前沿文献。同时，它深入研究了学习范式的细节，并对使用深度学习进行场景流估计的最新方法进行了深入的比较。此外，本文还研究了各种更高级别的场景理解任务，包括对象跟踪，运动分割等，并概述了场景流估计的可预见的研究趋势。
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.