Diffusion Masked Pretraining for Dynamic Point Cloud
Dynamic point cloud pretraining is still dominated by masked reconstruction objectives. However, these objectives inherit two key limitations. Existing methods inject ground-truth tube centers as decoder positional embeddings, causing spatio-temporal positional leakage. Moreover, they supervise inter-frame motion with deterministic proxy targets that systematically discard distributional structure by collapsing multimodal trajectory uncertainty into conditional means. To address these limitations, we propose Diffusion Masked Pretraining (DiMP), a unified self-supervised framework for dynamic point clouds. DiMP introduces diffusion modeling into both positional inference and motion learnin...
| Authors | Zhuoyue Zhang, Yiding Sun, Haozhe Cheng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 5 May 2026 |
| Last updated at source | 13 October 2026 |
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