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.
Ponder: Point cloud pre-training via neural rendering
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.