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.
To- wards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views
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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.