Papers › No-Reference Point Cloud Quality Assessment via Weighted Patch Quality Prediction
No-Reference Point Cloud Quality Assessment via Weighted Patch Quality Prediction
Jun Cheng, Honglei Su, Jari Korhonen
With the rapid development of 3D vision applications based on point clouds, point cloud quality assessment(PCQA) is becoming an important research topic. However, the prior PCQA methods ignore the effect of local quality variance across different areas of the point cloud. To take an advantage of the quality distribution imbalance, we propose a no-reference point cloud quality assessment (NR-PCQA) method with local area correlation analysis capability, denoted as COPP-Net. More specifically, we split a point cloud into patches, generate texture and structure features for each patch, and fuse them into patch features to predict patch quality. Then, we gather the features of all the patches of a point cloud for correlation analysis, to obtain the correlation weights. Finally, the predicted qualities and correlation weights for all the patches are used to derive the final quality score. Experimental results show that our method outperforms the state-of-the-art benchmark NR-PCQA methods. The source code for the proposed COPP-Net can be found at https://github.com/philox12358/COPP-Net.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Point Cloud Quality Assessment | WPC | COPP-Net | PLCC | 0.9324 | #1 of 5 | Archive leaderboard | report |
| Point Cloud Quality Assessment | WPC | COPP-Net | RMSE | 8.10 | #1 of 5 | Archive leaderboard | report |
| Point Cloud Quality Assessment | WPC | COPP-Net | SROCC | 0.9251 | #1 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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