# QD-PCQA: Quality-Aware Domain Adaptation for Point Cloud Quality Assessment

arXiv: https://arxiv.org/abs/2603.03726 · HTML: https://mlsanity.com/paper/2603.03726/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 42 references that are in the map.
- First posted on arXiv 4 Mar 2026.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [Deep Learning for 3D Point Clouds: A Survey](https://mlsanity.com/paper/1912.12033/) (2019)
- [TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment](https://mlsanity.com/paper/2308.03060/) (2023)
- [RankIQA: Learning from Rankings for No-reference Image Quality Assessment](https://mlsanity.com/paper/1707.08347/) (2017)
- [Beyond Sharing Weights for Deep Domain Adaptation](https://mlsanity.com/paper/1603.06432/) (2016)
- [3D Point Cloud Processing and Learning for Autonomous Driving](https://mlsanity.com/paper/2003.00601/) (2020)
- [Inferring Point Cloud Quality via Graph Similarity](https://mlsanity.com/paper/2006.00497/) (2020)
- [Transferability in Deep Learning: A Survey](https://mlsanity.com/paper/2201.05867/) (2022)
- [C-Mixup: Improving Generalization in Regression](https://mlsanity.com/paper/2210.05775/) (2022)
- [No-Reference Point Cloud Quality Assessment via Domain Adaptation](https://mlsanity.com/paper/2112.02851/) (2021)
- [No-reference Screen Content Image Quality Assessment with Unsupervised Domain Adaptation](https://mlsanity.com/paper/2008.08561/) (2020)
- [Temporal Consistency Learning of inter-frames for Video Super-Resolution](https://mlsanity.com/paper/2211.01639/) (2022)
- [Universal Domain Adaptation for Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2301.11387/) (2023)
- [StyleAM: Perception-Oriented Unsupervised Domain Adaption for Non-reference Image Quality Assessment](https://mlsanity.com/paper/2207.14489/) (2022)
- [JNMR: Joint Non-linear Motion Regression for Video Frame Interpolation](https://mlsanity.com/paper/2206.04231/) (2022)
- [COD: Learning Conditional Invariant Representation for Domain Adaptation Regression](https://mlsanity.com/paper/2408.06638/) (2024)
- [MUGSQA: Novel Multi-Uncertainty-Based Gaussian Splatting Quality Assessment Method, Dataset, and Benchmarks](https://mlsanity.com/paper/2511.06830/) (2025)
- [SeeClear: Semantic Distillation Enhances Pixel Condensation for Video Super-Resolution](https://mlsanity.com/paper/2410.05799/) (2024)
- [Scaling Audio-Visual Quality Assessment Dataset via Crowdsourcing](https://mlsanity.com/paper/2602.22659/) (2026)

## Built on by (most cited first, 2 of 2)

- [GT-PCQA: Geometry-Texture Decoupled Point Cloud Quality Assessment with MLLM](https://mlsanity.com/paper/2603.14951/) (2026)
- [PCQA-R1: Advancing Generalized 3D Point Cloud Quality Assessment with Reinforcement Learning](https://mlsanity.com/paper/2608.18627/) (2026)

## Abstract (arXiv)

No-Reference Point Cloud Quality Assessment (NR-PCQA) still struggles with generalization, primarily due to the scarcity of annotated point cloud datasets. Since the Human Visual System (HVS) drives perceptual quality assessment independently of media types, prior knowledge on quality learned from images can be repurposed for point clouds. This insight motivates adopting Unsupervised Domain Adaptation (UDA) to transfer quality-relevant priors from labeled images to unlabeled point clouds. However, existing UDA-based PCQA methods often overlook key characteristics of perceptual quality, such as sensitivity to quality ranking and quality-aware feature alignment, thereby limiting their effectiveness. To address these issues, we propose a novel Quality-aware Domain adaptation framework for PCQA, termed QD-PCQA. The framework comprises two main components: i) a Rank-weighted Conditional Alignment (RCA) strategy that aligns features under consistent quality levels and adaptively emphasizes misranked samples to reinforce perceptual quality ranking awareness; and ii) a Quality-guided Feature Augmentation (QFA) strategy, which includes quality-guided style mixup, multi-layer extension, and dual-domain augmentation modules to augment perceptual feature alignment. Extensive cross-domain experiments demonstrate that QD-PCQA significantly improves generalization in NR-PCQA tasks.