# BASICS: Broad quality Assessment of Static point clouds In Compression Scenarios

arXiv: https://arxiv.org/abs/2302.04796 · HTML: https://mlsanity.com/paper/2302.04796/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2023.

## Built on by (most cited first, 20 of 20)

- [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)
- [3DGCQA: A Quality Assessment Database for 3D AI-Generated Contents](https://mlsanity.com/paper/2409.07236/) (2024)
- [Simple Baselines for Projection-based Full-reference and No-reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2310.17147/) (2023)
- [PointPCA+: Extending PointPCA objective quality assessment metric](https://mlsanity.com/paper/2311.13880/) (2023)
- [PCE-GAN: A Generative Adversarial Network for Point Cloud Attribute Quality Enhancement based on Optimal Transport](https://mlsanity.com/paper/2503.00047/) (2025)
- [Full reference point cloud quality assessment using support vector regression](https://mlsanity.com/paper/2406.10520/) (2024)
- [Image-Guided Outdoor LiDAR Perception Quality Assessment for Autonomous Driving](https://mlsanity.com/paper/2406.17265/) (2024)
- [Full-reference Point Cloud Quality Assessment Using Spectral Graph Wavelets](https://mlsanity.com/paper/2406.09762/) (2024)
- [Asynchronous Feedback Network for Perceptual Point Cloud Quality Assessment](https://mlsanity.com/paper/2407.09806/) (2024)
- [RBFIM: Perceptual Quality Assessment for Compressed Point Clouds Using Radial Basis Function Interpolation](https://mlsanity.com/paper/2503.14154/) (2025)
- [No-reference geometry quality assessment for colorless point clouds via list-wise rank learning](https://mlsanity.com/paper/2502.11726/) (2025)
- [Low-Complexity Patch-based No-Reference Point Cloud Quality Metric exploiting Weighted Structure and Texture Features](https://mlsanity.com/paper/2503.15001/) (2025)
- [DPCD: A Quality Assessment Database for Dynamic Point Clouds](https://mlsanity.com/paper/2505.12431/) (2025)
- [Point Cloud Compression and Objective Quality Assessment: A Survey](https://mlsanity.com/paper/2506.22902/) (2025)
- [Activating Frequency and ViT for 3D Point Cloud Quality Assessment without Reference](https://mlsanity.com/paper/2312.05972/) (2023)
- [Perceptual Quality Assessment of Trisoup-Lifting Encoded 3D Point Clouds](https://mlsanity.com/paper/2410.06689/) (2024)
- [ARIQA-3DS: A Stereoscopic Image Quality Assessment Dataset for Realistic Augmented Reality](https://mlsanity.com/paper/2604.03112/) (2026)
- [PointQ-Bench: Benchmarking Diagnostic and Interpretable Point Cloud Quality Assessment](https://mlsanity.com/paper/2605.28241/) (2026)
- [DAL-PCQA: Enabling Distortion-Level and Language-Driven Reasoning for Point Cloud Quality Assessment](https://mlsanity.com/paper/2606.07938/) (2026)

## Abstract (arXiv)

Point clouds have become increasingly prevalent in representing 3D scenes within virtual environments, alongside 3D meshes. Their ease of capture has facilitated a wide array of applications on mobile devices, from smartphones to autonomous vehicles. Notably, point cloud compression has reached an advanced stage and has been standardized. However, the availability of quality assessment datasets, which are essential for developing improved objective quality metrics, remains limited. In this paper, we introduce BASICS, a large-scale quality assessment dataset tailored for static point clouds. The BASICS dataset comprises 75 unique point clouds, each compressed with four different algorithms including a learning-based method, resulting in the evaluation of nearly 1500 point clouds by 3500 unique participants. Furthermore, we conduct a comprehensive analysis of the gathered data, benchmark existing point cloud quality assessment metrics and identify their limitations. By publicly releasing the BASICS dataset, we lay the foundation for addressing these limitations and fostering the development of more precise quality metrics.