mlsanityData to 7 Oct 2026

Subjective Quality Database and Objective Study of Compressed Point Clouds With 6DoF Head-Mounted Display

arXiv 2008.02501 ↗First posted 6 Aug 2020eess.IVcited by 16

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Abstract (arXiv)

In this paper, we focus on subjective and objective Point Cloud Quality Assessment (PCQA) in an immersive environment and study the effect of geometry and texture attributes in compression distortion. Using a Head-Mounted Display (HMD) with six degrees of freedom, we establish a subjective PCQA database, named SIAT Point Cloud Quality Database (SIAT-PCQD). Our database consists of 340 distorted point clouds compressed by the MPEG point cloud encoder with the combination of 20 sequences and 17 pairs of geometry and texture quantization parameters. The impact of distorted geometry and texture attributes is further discussed in this paper. Then, we propose two projection-based objective quality evaluation methods, i.e., a weighted view projection based model and a patch projection based model. Our subjective database and findings can be used in point cloud processing, transmission, and coding, especially for virtual reality applications. The subjective dataset has been released in the public repository.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Aug 2020.

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Citing papers per year: 2022: 3; 2023: 1; 2024: 6; 2025: 4; 2026: 2.

16 papers.

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  1. 2022TCDM: Transformational Complexity Based Distortion Metric for Perceptual Point Cloud Quality Assessmentcited by 10
  2. 2024Perception-Guided Quality Metric of 3D Point Clouds Using Hybrid Strategycited by 5
  3. 2022Progressive Knowledge Transfer Based on Human Visual Perception Mechanism for Perceptual Quality Assessment of Point Cloudscited by 4
  4. 2022Learning a Task-specific Descriptor for Robust Matching of 3D Point Cloudscited by 3
  5. 2023No-Reference Point Cloud Quality Assessment via Weighted Patch Quality Predictioncited by 3
  6. 2024Stochasticity-aware No-Reference Point Cloud Quality Assessmentcited by 3
  7. 2024Asynchronous Feedback Network for Perceptual Point Cloud Quality Assessmentcited by 2
  8. 2024LFIC-DRASC: Deep Light Field Image Compression Using Disentangled Representation and Asymmetrical Strip Convolutioncited by 1
  9. 2025No-reference geometry quality assessment for colorless point clouds via list-wise rank learningcited by 1
  10. 2025Deep-JGAC: End-to-End Deep Joint Geometry and Attribute Compression for Dense Colored Point Cloudscited by 1
  11. 2025Low-Complexity Patch-based No-Reference Point Cloud Quality Metric exploiting Weighted Structure and Texture Featurescited by 1
  12. 2024Perceptual Quality Assessment of Trisoup-Lifting Encoded 3D Point Cloudscited by 0
4 more
  1. 2024Enhancing Content Representation for AR Image Quality Assessment Using Knowledge Distillationcited by 0
  2. 2025Deep Learning based Joint Geometry and Attribute Up-sampling for Large-Scale Colored Point Cloudscited by 0
  3. 2026UGD: An Unsupervised Geometric Distance for Evaluating Real-world Noisy Point Cloud Denoisingcited by 0
  4. 2026PointQ-Bench: Benchmarking Diagnostic and Interpretable Point Cloud Quality Assessmentcited by 0