A self-supervised point cloud model that encodes spatial structure into SSM latent states and adapts state-update scale to input length achieves new SOTA on ScanObjectNN and ModelNet40.
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StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning
A self-supervised point cloud model that encodes spatial structure into SSM latent states and adapts state-update scale to input length achieves new SOTA on ScanObjectNN and ModelNet40.