# Full reference point cloud quality assessment using support vector regression

arXiv: https://arxiv.org/abs/2406.10520 · HTML: https://mlsanity.com/paper/2406.10520/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 58 references that are in the map.
- First posted on arXiv 15 Jun 2024.

## Builds on (most cited first, 14 of 14)

- [Inferring Point Cloud Quality via Graph Similarity](https://mlsanity.com/paper/2006.00497/) (2020)
- [Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression](https://mlsanity.com/paper/1903.08548/) (2019)
- [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)
- [Improved Deep Point Cloud Geometry Compression](https://mlsanity.com/paper/2006.09043/) (2020)
- [Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compression](https://mlsanity.com/paper/2011.12688/) (2020)
- [A No-reference Quality Assessment Metric for Point Cloud Based on Captured Video Sequences](https://mlsanity.com/paper/2206.05054/) (2022)
- [GPA-Net:No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Network](https://mlsanity.com/paper/2210.16478/) (2022)
- [BASICS: Broad quality Assessment of Static point clouds In Compression Scenarios](https://mlsanity.com/paper/2302.04796/) (2023)
- [Reduced-Reference Quality Assessment of Point Clouds via Content-Oriented Saliency Projection](https://mlsanity.com/paper/2301.07681/) (2023)
- [PCQA-GRAPHPOINT: Efficients Deep-Based Graph Metric For Point Cloud Quality Assessment](https://mlsanity.com/paper/2211.02459/) (2022)
- [PointPCA+: Extending PointPCA objective quality assessment metric](https://mlsanity.com/paper/2311.13880/) (2023)
- [Fast graph-based denoising for point cloud color information](https://mlsanity.com/paper/2401.09721/) (2024)

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

- [Point Cloud Compression and Objective Quality Assessment: A Survey](https://mlsanity.com/paper/2506.22902/) (2025)
- [Objective Quality Assessment of Point Clouds Using Multi-scale Implicit Structural Similarity](https://mlsanity.com/paper/2601.01200/) (2026)
- [Complexity-Calibrated Reconstruction Discrepancy for No-Reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2610.13219/) (2026)

## Abstract (arXiv)

Point clouds are a general format for representing realistic 3D objects in diverse 3D applications. Since point clouds have large data sizes, developing efficient point cloud compression methods is crucial. However, excessive compression leads to various distortions, which deteriorates the point cloud quality perceived by end users. Thus, establishing reliable point cloud quality assessment (PCQA) methods is essential as a benchmark to develop efficient compression methods. This paper presents an accurate full-reference point cloud quality assessment (FR-PCQA) method called full-reference quality assessment using support vector regression (FRSVR) for various types of degradations such as compression distortion, Gaussian noise, and down-sampling. The proposed method demonstrates accurate PCQA by integrating five FR-based metrics covering various types of errors (e.g., considering geometric distortion, color distortion, and point count) using support vector regression (SVR). Moreover, the proposed method achieves a superior trade-off between accuracy and calculation speed because it includes only the calculation of these five simple metrics and SVR, which can perform fast prediction. Experimental results with three types of open datasets show that the proposed method is more accurate than conventional FR-PCQA methods. In addition, the proposed method is faster than state-of-the-art methods that utilize complicated features such as curvature and multi-scale features. Thus, the proposed method provides excellent performance in terms of the accuracy of PCQA and processing speed. Our method is available from https://github.com/STAC-USC/FRSVR-PCQA.