AI RESEARCH
Paving the Way for Point Cloud Video Representation Learning Using A PDE Model
arXiv CS.CV
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ArXi:2606.01604v1 Announce Type: new Investigating spatial-temporal correlations, specifically how spatial points vary over time, is crucial for understanding point cloud videos. Traditional methods, particularly flow-based techniques, struggle with these correlations due to the unordered spatial arrangement of sequential point cloud data. To address this challenge, we propose a novel approach that regularizes spatial-temporal correlation learning by formulating the problem as a solvable Partial Differential Equation.