Papers › Deep Hierarchical Representation of Point Cloud Videos via Spatio-Temporal Decomposition
Deep Hierarchical Representation of Point Cloud Videos via Spatio-Temporal Decomposition
Fan, Hehe; Yu, Xin; Yang, Yi; Kankanhalli, Mohan
In point cloud videos, point coordinates are irregular and unordered but point timestamps exhibit regularities and order. Grid-based networks for conventional video processing cannot be directly used to model raw point cloud videos. Therefore, in this work, we propose a point-based network that directly handles raw point cloud videos. First, to preserve the spatio-temporal local structure of point cloud videos, we design a point tube covering a local range along spatial and temporal dimensions. By progressively subsampling frames and points and enlarging the spatial radius as the point features are fed into higher-level layers, the point tube can capture video structure in a spatio-temporally hierarchical manner. Second, to reduce the impact of the spatial irregularity on temporal modeling, we decompose space and time when extracting point tube representations. Specifically, a spatial operation is employed to capture the local structure of each spatial region in a tube and a temporal operation is used to model the dynamics of the spatial regions along the tube.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Action Recognition | NTU RGB+D | PSTNet++ | Cross Subject Accuracy | 91.4 | #2 of 5 | Archive leaderboard | report |
| 3D Action Recognition | NTU RGB+D | PSTNet++ | Cross View Accuracy | 96.7 | #2 of 5 | Archive leaderboard | report |
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