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Motion-Saliency Complementary Masked Modeling for Point Cloud Video Understanding

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Point cloud video representation learning is crucial for 3D dynamic scene understanding. In this paper, we propose MoSaiC, a novel Motion-Saliency Complementary masked modeling framework for self-supervised point cloud video representation learning. MoSaiC couples three components: Curriculum Motion-Saliency Masking (CMSM), which guides the masking process toward motion-salient tokens under a curriculum schedule; Normal-Flow Motion (NFM) modeling, which supervises the local rigid rotation of each token in the Lie algebra so(3) as an explicit geometric motion target; and Cross-view Token Consis

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Evidence & attribution

First collected: 2026-09-21T07:22:03.933Z. This is not the publication date.