Diffusion Masked Pretraining for Dynamic Point Cloud
Diffusion Masked Pretraining (DiMP) is a new self-supervised method for dynamic point clouds that uses diffusion modeling to improve motion learning. It addresses positional leakage and distributional collapse by treating inter-frame displacement as a noise prediction task. This allows the model to learn the full distribution of plausible motions rather than just one fixed estimate, leading to better performance.