# Simple Baselines for Projection-based Full-reference and No-reference Point Cloud Quality Assessment

arXiv: https://arxiv.org/abs/2310.17147 · HTML: https://mlsanity.com/paper/2310.17147/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [Inferring Point Cloud Quality via Graph Similarity](https://mlsanity.com/paper/2006.00497/) (2020)
- [No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models](https://mlsanity.com/paper/2107.02041/) (2021)
- [NTIRE 2022 Challenge on Perceptual Image Quality Assessment](https://mlsanity.com/paper/2206.11695/) (2022)
- [Advancing Zero-Shot Digital Human Quality Assessment through Text-Prompted Evaluation](https://mlsanity.com/paper/2307.02808/) (2023)
- [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)
- [GMS-3DQA: Projection-based Grid Mini-patch Sampling for 3D Model Quality Assessment](https://mlsanity.com/paper/2306.05658/) (2023)
- [BASICS: Broad quality Assessment of Static point clouds In Compression Scenarios](https://mlsanity.com/paper/2302.04796/) (2023)

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

- [Quality Assessment in the Era of Large Models: A Survey](https://mlsanity.com/paper/2409.00031/) (2024)
- [MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis](https://mlsanity.com/paper/2411.11235/) (2024)
- [3DGCQA: A Quality Assessment Database for 3D AI-Generated Contents](https://mlsanity.com/paper/2409.07236/) (2024)
- [Point Cloud Compression and Objective Quality Assessment: A Survey](https://mlsanity.com/paper/2506.22902/) (2025)
- [Who is a Better Talker: Subjective and Objective Quality Assessment for AI-Generated Talking Heads](https://mlsanity.com/paper/2507.23343/) (2025)

## Abstract (arXiv)

Point clouds are widely used in 3D content representation and have various applications in multimedia. However, compression and simplification processes inevitably result in the loss of quality-aware information under storage and bandwidth constraints. Therefore, there is an increasing need for effective methods to quantify the degree of distortion in point clouds. In this paper, we propose simple baselines for projection-based point cloud quality assessment (PCQA) to tackle this challenge. We use multi-projections obtained via a common cube-like projection process from the point clouds for both full-reference (FR) and no-reference (NR) PCQA tasks. Quality-aware features are extracted with popular vision backbones. The FR quality representation is computed as the similarity between the feature maps of reference and distorted projections while the NR quality representation is obtained by simply squeezing the feature maps of distorted projections with average pooling The corresponding quality representations are regressed into visual quality scores by fully-connected layers. Taking part in the ICIP 2023 PCVQA Challenge, we succeeded in achieving the top spot in four out of the five competition tracks.