PointATA is a parameter-efficient transfer learning method that aligns 3D-4D modality gaps via optimal transport before adapting a frozen 3D model with video-specific modules to achieve strong 4D perception results.
No pain, big gain: classify dynamic point cloud sequences with static models by fitting feature-level space-time surfaces,
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Align then Adapt: Rethinking Parameter-Efficient Transfer Learning in 4D Perception
PointATA is a parameter-efficient transfer learning method that aligns 3D-4D modality gaps via optimal transport before adapting a frozen 3D model with video-specific modules to achieve strong 4D perception results.