# Joint Geometry and Color Projection-based Point Cloud Quality Metric

arXiv: https://arxiv.org/abs/2108.02481 · HTML: https://mlsanity.com/paper/2108.02481/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2021.

## Built on by (most cited first, 8 of 8)

- [TCDM: Transformational Complexity Based Distortion Metric for Perceptual Point Cloud Quality Assessment](https://mlsanity.com/paper/2210.04671/) (2022)
- [Reduced Reference Quality Assessment for Point Cloud Compression](https://mlsanity.com/paper/2301.01009/) (2023)
- [RBFIM: Perceptual Quality Assessment for Compressed Point Clouds Using Radial Basis Function Interpolation](https://mlsanity.com/paper/2503.14154/) (2025)
- [Empir3D : A Framework for Multi-Dimensional Point Cloud Assessment](https://mlsanity.com/paper/2306.03660/) (2023)
- [From Images to Point Clouds: An Efficient Solution for Cross-media Blind Quality Assessment without Annotated Training](https://mlsanity.com/paper/2501.13387/) (2025)
- [Point Cloud Compression and Objective Quality Assessment: A Survey](https://mlsanity.com/paper/2506.22902/) (2025)
- [Perceptual Quality Assessment of Trisoup-Lifting Encoded 3D Point Clouds](https://mlsanity.com/paper/2410.06689/) (2024)
- [Objective Quality Assessment of Point Clouds Using Multi-scale Implicit Structural Similarity](https://mlsanity.com/paper/2601.01200/) (2026)

## Abstract (arXiv)

Point cloud coding solutions have been recently standardized to address the needs of multiple application scenarios. The design and assessment of point cloud coding methods require reliable objective quality metrics to evaluate the level of degradation introduced by compression or any other type of processing. Several point cloud objective quality metrics has been recently proposed to reliable estimate human perceived quality, including the so-called projection-based metrics. In this context, this paper proposes a joint geometry and color projection-based point cloud objective quality metric which solves the critical weakness of this type of quality metrics, i.e., the misalignment between the reference and degraded projected images. Moreover, the proposed point cloud quality metric exploits the best performing 2D quality metrics in the literature to assess the quality of the projected images. The experimental results show that the proposed projection-based quality metric offers the best subjective-objective correlation performance in comparison with other metrics in the literature. The Pearson correlation gains regarding D1-PSNR and D2-PSNR metrics are 17% and 14.2 when data with all coding degradations is considered.