Point-set distances for learning representations of 3d point clouds

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Advancing 3D point cloud understanding through deep transfer learning: A comprehensive survey

SS Sohail, Y Himeur, H Kheddar, A Amira, F Fadli… - Information …, 2024 - Elsevier
The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of
deep learning (DL). However, the latter faces various issues, including the lack of data or …
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Density-aware chamfer distance as a comprehensive metric for point cloud completion

T Wu, L Pan, J Zhang, T Wang, Z Liu, D Lin - ar** system that builds on the differentiable Gaussians to improve the map** …
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Hierarchical sliced wasserstein distance

K Nguyen, T Ren, H Nguyen, L Rout, T Nguyen… - arxiv preprint arxiv …, 2022 - arxiv.org
Sliced Wasserstein (SW) distance has been widely used in different application scenarios
since it can be scaled to a large number of supports without suffering from the curse of …
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