# PointPCA+: Extending PointPCA objective quality assessment metric

arXiv: https://arxiv.org/abs/2311.13880 · HTML: https://mlsanity.com/paper/2311.13880/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 23 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [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)
- [Perceptual Quality Assessment of Colored 3D Point Clouds](https://mlsanity.com/paper/2111.05474/) (2021)
- [MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2209.00244/) (2022)
- [BASICS: Broad quality Assessment of Static point clouds In Compression Scenarios](https://mlsanity.com/paper/2302.04796/) (2023)
- [PointPCA: Point Cloud Objective Quality Assessment Using PCA-Based Descriptors](https://mlsanity.com/paper/2111.12663/) (2021)
- [Progressive Knowledge Transfer Based on Human Visual Perception Mechanism for Perceptual Quality Assessment of Point Clouds](https://mlsanity.com/paper/2211.16646/) (2022)

## Built on by (most cited first, 4 of 4)

- [LMM-PCQA: Assisting Point Cloud Quality Assessment with LMM](https://mlsanity.com/paper/2404.18203/) (2024)
- [Full reference point cloud quality assessment using support vector regression](https://mlsanity.com/paper/2406.10520/) (2024)
- [Full-reference Point Cloud Quality Assessment Using Spectral Graph Wavelets](https://mlsanity.com/paper/2406.09762/) (2024)
- [Point Cloud Compression and Objective Quality Assessment: A Survey](https://mlsanity.com/paper/2506.22902/) (2025)

## Abstract (arXiv)

A computationally-simplified and descriptor-richer Point Cloud Quality Assessment (PCQA) metric, namely PointPCA+, is proposed in this paper, which is an extension of PointPCA. PointPCA proposed a set of perceptually-relevant descriptors based on PCA decomposition that were applied to both the geometry and texture data of point clouds for full reference PCQA. PointPCA+ employs PCA only on the geometry data while enriching existing geometry and texture descriptors, that are computed more efficiently. Similarly to PointPCA, a total quality score is obtained through a learning-based fusion of individual predictions from geometry and texture descriptors that capture local shape and appearance properties, respectively. Before feature fusion, a feature selection module is introduced to choose the most effective features from a proposed super-set. Experimental results show that PointPCA+ achieves high predictive performance against subjective ground truth scores obtained from publicly available datasets. The code is available at urlhttps://github.com/cwi-dis/pointpcasuite/.