# No-Reference Point Cloud Quality Assessment via Weighted Patch Quality Prediction

arXiv: https://arxiv.org/abs/2305.07829 · HTML: https://mlsanity.com/paper/2305.07829/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 13 May 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Inferring Point Cloud Quality via Graph Similarity](https://mlsanity.com/paper/2006.00497/) (2020)
- [Point Cloud Quality Assessment: Dataset Construction and Learning-based No-Reference Metric](https://mlsanity.com/paper/2012.11895/) (2020)
- [No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models](https://mlsanity.com/paper/2107.02041/) (2021)
- [Perceptual Quality Assessment of Colored 3D Point Clouds](https://mlsanity.com/paper/2111.05474/) (2021)
- [No-Reference Point Cloud Quality Assessment via Domain Adaptation](https://mlsanity.com/paper/2112.02851/) (2021)
- [Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compression](https://mlsanity.com/paper/2011.12688/) (2020)
- [Subjective Quality Database and Objective Study of Compressed Point Clouds With 6DoF Head-Mounted Display](https://mlsanity.com/paper/2008.02501/) (2020)
- [A generalized Hausdorff distance based quality metric for point cloud geometry](https://mlsanity.com/paper/2003.13669/) (2020)
- [k-Nearest Neighbors on Road Networks: A Journey in Experimentation and In-Memory Implementation](https://mlsanity.com/paper/1601.01549/) (2016)

## Built on by (most cited first, 3 of 3)

- [PAME: Self-Supervised Masked Autoencoder for No-Reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2403.10061/) (2024)
- [Low-Complexity Patch-based No-Reference Point Cloud Quality Metric exploiting Weighted Structure and Texture Features](https://mlsanity.com/paper/2503.15001/) (2025)
- [PointQ-Bench: Benchmarking Diagnostic and Interpretable Point Cloud Quality Assessment](https://mlsanity.com/paper/2605.28241/) (2026)

## Abstract (arXiv)

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.