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ModelNet40

Point cloud registration is a crucial problem in computer vision and robotics. Existing methods either rely on matching local geometric features, which are sensitive to the pose differences, or leverage global shapes, which leads to inconsistency when facing distribution variances such as partial overlapping.

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Cite this as

Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, Jianxiong Xiao (2024). Dataset: ModelNet40. https://doi.org/10.57702/uexf99xg

DOI retrieved: November 25, 2024

Additional Info

Field Value
Created November 25, 2024
Last update December 2, 2024
Defined In https://doi.org/10.48550/arXiv.2104.02963
Citation
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Author Zhirong Wu
More Authors
Shuran Song
Aditya Khosla
Fisher Yu
Linguang Zhang
Xiaoou Tang
Jianxiong Xiao
Homepage https://www.cs.unc.edu/~eht/Research/ModelNet40/