Paving the Way for Point Cloud Video Representation Learning Using A PDE Model
2026-06-01 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors look at how points in 3D space change over time in point cloud videos, which is tricky because the points aren't ordered like pixels in images. To solve this, they treat the problem using math equations called Partial Differential Equations (PDEs), inspired by how fluids are analyzed. They add a learning method that compares changes over time and space to help guide the solution. Their method, MotionPDE, can be added to existing models with little extra work and shows promise for understanding point cloud videos better.
point cloud videospatial-temporal correlationPartial Differential Equationcontrastive learningself-supervised learningflow-based methodstemporal embeddingsspatial embeddingsplug-and-play module
Authors
Zhuoxu Huang, Zhenkun Fan, Jungong Han, Josef Kittler
Abstract
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 (PDE). While PDEs have long been effective in the physical domain, their application to novel sequential data like point cloud video remains underexplored. Inspired by fluid analysis, we construct a simplified PDE, and the process of solving PDE is guided and refined by a contrastive learning structure between the temporal embeddings and the spatial embeddings. With this extra supervision, our method, named MotionPDE, serves as an effective, plug-and-play enhancement module for existing backbone models, adding minimal computational overhead and parameters. Capitalizing on the contrastive learning process, we delve deeper into the self-supervised capabilities of MotionPDE, yielding promising results that underscore its utility and adaptability in point cloud video data interpretation. The code repo with trained checkpoints will be available at https://github.com/zhh6425/motionpde.git for facilitating future research.