# Improving PSNR-based Quality Metrics Performance For Point Cloud Geometry

arXiv: https://arxiv.org/abs/2006.03714 · HTML: https://mlsanity.com/paper/2006.03714/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [MPED: Quantifying Point Cloud Distortion based on Multiscale Potential Energy Discrepancy](https://mlsanity.com/paper/2103.02850/) (2021)
- [PointCompress3D: A Point Cloud Compression Framework for Roadside LiDARs in Intelligent Transportation Systems](https://mlsanity.com/paper/2405.01750/) (2024)
- [Point Cloud Compression and Objective Quality Assessment: A Survey](https://mlsanity.com/paper/2506.22902/) (2025)
- [A deep perceptual metric for 3D point clouds](https://mlsanity.com/paper/2102.12839/) (2021)
- [Perceptual Quality Assessment of Trisoup-Lifting Encoded 3D Point Clouds](https://mlsanity.com/paper/2410.06689/) (2024)

## Abstract (arXiv)

An increased interest in immersive applications has drawn attention to emerging 3D imaging representation formats, notably light fields and point clouds (PCs). Nowadays, PCs are one of the most popular 3D media formats, due to recent developments in PC acquisition, namely with new depth sensors and signal processing algorithms. To obtain high fidelity 3D representations of visual scenes a huge amount of PC data is typically acquired, which demands efficient compression solutions. As in 2D media formats, the final perceived PC quality plays an important role in the overall user experience and, thus, objective metrics capable to measure the PC quality in a reliable way are essential. In this context, this paper proposes and evaluates a set of objective quality metrics for the geometry component of PC data, which plays a very important role in the final perceived quality. Based on the popular PSNR PC geometry quality metric, the novel improved PSNR-based metrics are proposed by exploiting the intrinsic PC characteristics and the rendering process that must occur before visualization. The experimental results show the superiority of the best-proposed metrics over the state-of-the-art, obtaining an improvement of up to 32% in the Pearson correlation coefficient.