# TCDM: Transformational Complexity Based Distortion Metric for Perceptual Point Cloud Quality Assessment

arXiv: https://arxiv.org/abs/2210.04671 · HTML: https://mlsanity.com/paper/2210.04671/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 75 references that are in the map.
- First posted on arXiv 10 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion](https://mlsanity.com/paper/2111.12702/) (2021)
- [Inferring Point Cloud Quality via Graph Similarity](https://mlsanity.com/paper/2006.00497/) (2020)
- [Perceptual Quality Assessment of Colored 3D Point Clouds](https://mlsanity.com/paper/2111.05474/) (2021)
- [No-Reference Point Cloud Quality Assessment via Domain Adaptation](https://mlsanity.com/paper/2112.02851/) (2021)
- [Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compression](https://mlsanity.com/paper/2011.12688/) (2020)
- [MPED: Quantifying Point Cloud Distortion based on Multiscale Potential Energy Discrepancy](https://mlsanity.com/paper/2103.02850/) (2021)
- [Point Cloud Rendering after Coding: Impacts on Subjective and Objective Quality](https://mlsanity.com/paper/1912.09137/) (2019)
- [Subjective Quality Database and Objective Study of Compressed Point Clouds With 6DoF Head-Mounted Display](https://mlsanity.com/paper/2008.02501/) (2020)
- [A generalized Hausdorff distance based quality metric for point cloud geometry](https://mlsanity.com/paper/2003.13669/) (2020)
- [PointPCA: Point Cloud Objective Quality Assessment Using PCA-Based Descriptors](https://mlsanity.com/paper/2111.12663/) (2021)
- [Joint Geometry and Color Projection-based Point Cloud Quality Metric](https://mlsanity.com/paper/2108.02481/) (2021)

## Built on by (most cited first, 10 of 10)

- [Perception-Guided Quality Metric of 3D Point Clouds Using Hybrid Strategy](https://mlsanity.com/paper/2407.03885/) (2024)
- [GeodesicPSIM: Predicting the Quality of Static Mesh with Texture Map via Geodesic Patch Similarity](https://mlsanity.com/paper/2308.04928/) (2023)
- [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)
- [Deep-JGAC: End-to-End Deep Joint Geometry and Attribute Compression for Dense Colored Point Clouds](https://mlsanity.com/paper/2502.17939/) (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)
- [Perceptual Quality Assessment of Trisoup-Lifting Encoded 3D Point Clouds](https://mlsanity.com/paper/2410.06689/) (2024)
- [Objective Quality Assessment of Point Clouds Using Multi-scale Implicit Structural Similarity](https://mlsanity.com/paper/2601.01200/) (2026)
- [UGD: An Unsupervised Geometric Distance for Evaluating Real-world Noisy Point Cloud Denoising](https://mlsanity.com/paper/2604.16976/) (2026)

## Abstract (arXiv)

The goal of objective point cloud quality assessment (PCQA) research is to develop quantitative metrics that measure point cloud quality in a perceptually consistent manner. Merging the research of cognitive science and intuition of the human visual system (HVS), in this paper, we evaluate the point cloud quality by measuring the complexity of transforming the distorted point cloud back to its reference, which in practice can be approximated by the code length of one point cloud when the other is given. For this purpose, we first make space segmentation for the reference and distorted point clouds based on a 3D Voronoi diagram to obtain a series of local patch pairs. Next, inspired by the predictive coding theory, we utilize a space-aware vector autoregressive (SA-VAR) model to encode the geometry and color channels of each reference patch with and without the distorted patch, respectively. Assuming that the residual errors follow the multi-variate Gaussian distributions, the self-complexity of the reference and transformational complexity between the reference and distorted samples are computed using covariance matrices. Additionally, the prediction terms generated by SA-VAR are introduced as one auxiliary feature to promote the final quality prediction. The effectiveness of the proposed transformational complexity based distortion metric (TCDM) is evaluated through extensive experiments conducted on five public point cloud quality assessment databases. The results demonstrate that TCDM achieves state-of-the-art (SOTA) performance, and further analysis confirms its robustness in various scenarios. The code is publicly available at https://github.com/zyj1318053/TCDM.