PointCSP achieves better semantic consistency and performance in point cloud self-supervised learning by propagating semantics across batch samples via state-space models and stabilizing transfer with asymmetric distillation.
3dses: an indoor lidar point cloud segmen- tation dataset with real and pseudo-labels from a 3d model
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PointCSP: Cross-Sample Semantic Propagation and Stability Preservation in Self-Supervised Point Cloud Learning
PointCSP achieves better semantic consistency and performance in point cloud self-supervised learning by propagating semantics across batch samples via state-space models and stabilizing transfer with asymmetric distillation.