# Point Cloud Quality Assessment: Dataset Construction and Learning-based No-Reference Metric

arXiv: https://arxiv.org/abs/2012.11895 · HTML: https://mlsanity.com/paper/2012.11895/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2020.

## Built on by (most cited first, 30 of 53)

- [MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2209.00244/) (2022)
- [MPED: Quantifying Point Cloud Distortion based on Multiscale Potential Energy Discrepancy](https://mlsanity.com/paper/2103.02850/) (2021)
- [Advancing Zero-Shot Digital Human Quality Assessment through Text-Prompted Evaluation](https://mlsanity.com/paper/2307.02808/) (2023)
- [Quality Assessment in the Era of Large Models: A Survey](https://mlsanity.com/paper/2409.00031/) (2024)
- [A No-reference Quality Assessment Metric for Point Cloud Based on Captured Video Sequences](https://mlsanity.com/paper/2206.05054/) (2022)
- [Evaluating Point Cloud from Moving Camera Videos: A No-Reference Metric](https://mlsanity.com/paper/2208.14085/) (2022)
- [GPA-Net:No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Network](https://mlsanity.com/paper/2210.16478/) (2022)
- [GMS-3DQA: Projection-based Grid Mini-patch Sampling for 3D Model Quality Assessment](https://mlsanity.com/paper/2306.05658/) (2023)
- [LMM-PCQA: Assisting Point Cloud Quality Assessment with LMM](https://mlsanity.com/paper/2404.18203/) (2024)
- [Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2403.10066/) (2024)
- [Perceptual Quality Assessment for Digital Human Heads](https://mlsanity.com/paper/2209.09489/) (2022)
- [DDH-QA: A Dynamic Digital Humans Quality Assessment Database](https://mlsanity.com/paper/2212.12734/) (2022)
- [EEP-3DQA: Efficient and Effective Projection-based 3D Model Quality Assessment](https://mlsanity.com/paper/2302.08715/) (2023)
- [A No-Reference Quality Assessment Method for Digital Human Head](https://mlsanity.com/paper/2310.16732/) (2023)
- [3DGCQA: A Quality Assessment Database for 3D AI-Generated Contents](https://mlsanity.com/paper/2409.07236/) (2024)
- [No-Reference Point Cloud Quality Assessment via Graph Convolutional Network](https://mlsanity.com/paper/2411.07728/) (2024)
- [Reduced Reference Quality Assessment for Point Cloud Compression](https://mlsanity.com/paper/2301.01009/) (2023)
- [Perception-Guided Quality Metric of 3D Point Clouds Using Hybrid Strategy](https://mlsanity.com/paper/2407.03885/) (2024)
- [Progressive Knowledge Transfer Based on Human Visual Perception Mechanism for Perceptual Quality Assessment of Point Clouds](https://mlsanity.com/paper/2211.16646/) (2022)
- [GeodesicPSIM: Predicting the Quality of Static Mesh with Texture Map via Geodesic Patch Similarity](https://mlsanity.com/paper/2308.04928/) (2023)
- [PointPCA+: Extending PointPCA objective quality assessment metric](https://mlsanity.com/paper/2311.13880/) (2023)
- [Stochasticity-aware No-Reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2401.08926/) (2024)
- [Headset: Human emotion awareness under partial occlusions multimodal dataset](https://mlsanity.com/paper/2402.09107/) (2024)
- [PAME: Self-Supervised Masked Autoencoder for No-Reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2403.10061/) (2024)
- [MUGSQA: Novel Multi-Uncertainty-Based Gaussian Splatting Quality Assessment Method, Dataset, and Benchmarks](https://mlsanity.com/paper/2511.06830/) (2025)
- [No-Reference Point Cloud Quality Assessment via Weighted Patch Quality Prediction](https://mlsanity.com/paper/2305.07829/) (2023)
- [Screen-based 3D Subjective Experiment Software](https://mlsanity.com/paper/2308.03698/) (2023)
- [Full reference point cloud quality assessment using support vector regression](https://mlsanity.com/paper/2406.10520/) (2024)
- [Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities](https://mlsanity.com/paper/2410.08534/) (2024)
- [DHQA-4D: Perceptual Quality Assessment of Dynamic 4D Digital Human](https://mlsanity.com/paper/2510.03874/) (2025)

## Abstract (arXiv)

Full-reference (FR) point cloud quality assessment (PCQA) has achieved impressive progress in recent years. However, in many cases, obtaining the reference point clouds is difficult, so no-reference (NR) metrics have become a research hotspot. Few researches about NR-PCQA are carried out due to the lack of a large-scale PCQA dataset. In this paper, we first build a large-scale PCQA dataset named LS-PCQA, which includes 104 reference point clouds and more than 22,000 distorted samples. In the dataset, each reference point cloud is augmented with 31 types of impairments (e.g., Gaussian noise, contrast distortion, local missing, and compression loss) at 7 distortion levels. Besides, each distorted point cloud is assigned with a pseudo quality score as its substitute of Mean Opinion Score (MOS). Inspired by the hierarchical perception system and considering the intrinsic attributes of point clouds, we propose a NR metric ResSCNN based on sparse convolutional neural network (CNN) to accurately estimate the subjective quality of point clouds. We conduct several experiments to evaluate the performance of the proposed NR metric. The results demonstrate that ResSCNN exhibits the state-of-the-art (SOTA) performance among all the existing NR-PCQA metrics and even outperforms some FR metrics. The dataset presented in this work will be made publicly accessible at http://smt.sjtu.edu.cn. The source code for the proposed ResSCNN can be found at https://github.com/lyp22/ResSCNN.