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

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dc.contributor.authorLi, Zhiqien_US
dc.contributor.authorXiang, Nanen_US
dc.contributor.authorChen, Honghuaen_US
dc.contributor.authorZhang, Jianjunen_US
dc.contributor.authorYang, Xiaosongen_US
dc.contributor.editorHauser, Helwig and Alliez, Pierreen_US
dc.date.accessioned2023-10-06T11:58:55Z
dc.date.available2023-10-06T11:58:55Z
dc.date.issued2023
dc.description.abstractAiming 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.en_US
dc.description.documenttypestar
dc.description.number6
dc.description.sectionheadersORIGINAL ARTICLES
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14795
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.14795
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14795
dc.publisher© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject3D scene flow
dc.subjectliterature survey
dc.titleDeep Learning for Scene Flow Estimation on Point Clouds: A Survey and Prospective Trendsen_US
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