# No-Reference Point Cloud Quality Assessment via Domain Adaptation

arXiv: https://arxiv.org/abs/2112.02851 · HTML: https://mlsanity.com/paper/2112.02851/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Deep Domain Confusion: Maximizing for Domain Invariance](https://mlsanity.com/paper/1412.3474/) (2014)
- [A Survey of Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1812.02849/) (2018)
- [Domain Separation Networks](https://mlsanity.com/paper/1608.06019/) (2016)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019)
- [Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild](https://mlsanity.com/paper/2005.13983/) (2020)
- [Inferring Point Cloud Quality via Graph Similarity](https://mlsanity.com/paper/2006.00497/) (2020)
- [PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition](https://mlsanity.com/paper/1808.07659/) (2018)
- [Domain Adaptation for Vehicle Detection from Bird's Eye View LiDAR Point Cloud Data](https://mlsanity.com/paper/1905.08955/) (2019)
- [Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compression](https://mlsanity.com/paper/2011.12688/) (2020)
- [Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network](https://mlsanity.com/paper/1901.08906/) (2019)
- [No-reference Screen Content Image Quality Assessment with Unsupervised Domain Adaptation](https://mlsanity.com/paper/2008.08561/) (2020)
- [3D-PSRNet: Part Segmented 3D Point Cloud Reconstruction From a Single Image](https://mlsanity.com/paper/1810.00461/) (2018)

## Built on by (most cited first, 30 of 38)

- [MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2209.00244/) (2022)
- [Quality Assessment in the Era of Large Models: A Survey](https://mlsanity.com/paper/2409.00031/) (2024)
- [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)
- [DG-PIC: Domain Generalized Point-In-Context Learning for Point Cloud Understanding](https://mlsanity.com/paper/2407.08801/) (2024)
- [TCDM: Transformational Complexity Based Distortion Metric for Perceptual Point Cloud Quality Assessment](https://mlsanity.com/paper/2210.04671/) (2022)
- [StyleAM: Perception-Oriented Unsupervised Domain Adaption for Non-reference Image Quality Assessment](https://mlsanity.com/paper/2207.14489/) (2022)
- [Blind Quality Assessment of 3D Dense Point Clouds with Structure Guided Resampling](https://mlsanity.com/paper/2208.14603/) (2022)
- [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)
- [TDMD: A Database for Dynamic Color Mesh Subjective and Objective Quality Explorations](https://mlsanity.com/paper/2308.01499/) (2023)
- [GeodesicPSIM: Predicting the Quality of Static Mesh with Texture Map via Geodesic Patch Similarity](https://mlsanity.com/paper/2308.04928/) (2023)
- [Stochasticity-aware No-Reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2401.08926/) (2024)
- [PAME: Self-Supervised Masked Autoencoder for No-Reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2403.10061/) (2024)
- [No-Reference Point Cloud Quality Assessment via Weighted Patch Quality Prediction](https://mlsanity.com/paper/2305.07829/) (2023)
- [Image-Guided Outdoor LiDAR Perception Quality Assessment for Autonomous Driving](https://mlsanity.com/paper/2406.17265/) (2024)
- [Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities](https://mlsanity.com/paper/2410.08534/) (2024)
- [Towards Fine-Grained Text-to-3D Quality Assessment: A Benchmark and A Two-Stage Rank-Learning Metric](https://mlsanity.com/paper/2509.23841/) (2025)
- [PIT-QMM: A Large Multimodal Model For No-Reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2510.07636/) (2025)
- [Asynchronous Feedback Network for Perceptual Point Cloud Quality Assessment](https://mlsanity.com/paper/2407.09806/) (2024)
- [Learning Disentangled Representations for Perceptual Point Cloud Quality Assessment via Mutual Information Minimization](https://mlsanity.com/paper/2411.07936/) (2024)
- [QD-PCQA: Quality-Aware Domain Adaptation for Point Cloud Quality Assessment](https://mlsanity.com/paper/2603.03726/) (2026)
- [Empir3D : A Framework for Multi-Dimensional Point Cloud Assessment](https://mlsanity.com/paper/2306.03660/) (2023)
- [From Images to Point Clouds: An Efficient Solution for Cross-media Blind Quality Assessment without Annotated Training](https://mlsanity.com/paper/2501.13387/) (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)
- [Point Cloud Compression and Objective Quality Assessment: A Survey](https://mlsanity.com/paper/2506.22902/) (2025)
- [UPDA: Unsupervised Progressive Domain Adaptation for No-Reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2602.11969/) (2026)

## Abstract (arXiv)

We present a novel no-reference quality assessment metric, the image transferred point cloud quality assessment (IT-PCQA), for 3D point clouds. For quality assessment, deep neural network (DNN) has shown compelling performance on no-reference metric design. However, the most challenging issue for no-reference PCQA is that we lack large-scale subjective databases to drive robust networks. Our motivation is that the human visual system (HVS) is the decision-maker regardless of the type of media for quality assessment. Leveraging the rich subjective scores of the natural images, we can quest the evaluation criteria of human perception via DNN and transfer the capability of prediction to 3D point clouds. In particular, we treat natural images as the source domain and point clouds as the target domain, and infer point cloud quality via unsupervised adversarial domain adaptation. To extract effective latent features and minimize the domain discrepancy, we propose a hierarchical feature encoder and a conditional-discriminative network. Considering that the ultimate purpose is regressing objective score, we introduce a novel conditional cross entropy loss in the conditional-discriminative network to penalize the negative samples which hinder the convergence of the quality regression network. Experimental results show that the proposed method can achieve higher performance than traditional no-reference metrics, even comparable results with full-reference metrics. The proposed method also suggests the feasibility of assessing the quality of specific media content without the expensive and cumbersome subjective evaluations. Code is available at https://github.com/Qi-Yangsjtu/IT-PCQA.