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HGL: Hierarchical Geometry Learning for Test-time Adaptation in 3D Point Cloud Segmentation

3D point cloud segmentation has received significant interest for its growing applications. However, the generalization ability of models suffers in dynamic scenarios due to the distribution shift between test and training data.

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Cite this as

Tianpei Zou, Sanqing Qu, Zhijun Li, Alois Knoll, Lianghua He, Guang Chen, Changjun Jiang (2024). Dataset: HGL: Hierarchical Geometry Learning for Test-time Adaptation in 3D Point Cloud Segmentation. https://doi.org/10.57702/irlzamx3

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Additional Info

Field	Value
Created	December 16, 2024
Last update	December 16, 2024
Defined In	https://doi.org/10.48550/arXiv.2407.12387
Author	Tianpei Zou
More Authors	Sanqing Qu Zhijun Li Alois Knoll Lianghua He Guang Chen Changjun Jiang
Homepage	https://github.com/tpzou/HGL