# Complexity-Calibrated Reconstruction Discrepancy for No-Reference Point Cloud Quality Assessment

arXiv: https://arxiv.org/abs/2610.13219 · HTML: https://mlsanity.com/paper/2610.13219/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 9 Oct 2026.

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

- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [Masked Autoencoders for Point Cloud Self-supervised Learning](https://mlsanity.com/paper/2203.06604/) (2022)
- [Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective](https://mlsanity.com/paper/2303.14968/) (2023)
- [Neural Kernel Surface Reconstruction](https://mlsanity.com/paper/2305.19590/) (2023)
- [Point Cloud Quality Assessment: Dataset Construction and Learning-based No-Reference Metric](https://mlsanity.com/paper/2012.11895/) (2020)
- [No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models](https://mlsanity.com/paper/2107.02041/) (2021)
- [MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2209.00244/) (2022)
- [Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compression](https://mlsanity.com/paper/2011.12688/) (2020)
- [GMS-3DQA: Projection-based Grid Mini-patch Sampling for 3D Model Quality Assessment](https://mlsanity.com/paper/2306.05658/) (2023)
- [CLIP-PCQA: Exploring Subjective-Aligned Vision-Language Modeling for Point Cloud Quality Assessment](https://mlsanity.com/paper/2501.10071/) (2025)
- [No-Reference Point Cloud Quality Assessment via Graph Convolutional Network](https://mlsanity.com/paper/2411.07728/) (2024)
- [Stochasticity-aware No-Reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2401.08926/) (2024)
- [Full reference point cloud quality assessment using support vector regression](https://mlsanity.com/paper/2406.10520/) (2024)

## Abstract (arXiv)

The discrepancy induced by surface reconstruction in a distorted point cloud is jointly influenced by its intrinsic structural complexity and signal degradation. This observation motivates a referencefree point cloud quality assessment (PCQA) strategy that estimates and compensates for the complexity-induced component. We implement this strategy through successive reconstruction. Specifically, reconstructing the distorted point cloud produces an input-toreconstruction discrepancy that combines the effects of signal degradation and content complexity. Reapplying the reconstruction operator with stronger smoothing produces a discrepancy between successive reconstructions, which serves as a proxy for the complexityinduced component. This proxy is then used to calibrate the inputto-reconstruction discrepancy. We apply the same strategy to 3D geometry patches and their aligned 2D regions, enabling local comparison of the resulting discrepancies. The calibrated geometric and projected-view discrepancies are then fused into a final degradation score. For both branches, the discrepancies are measured using frozen pretrained perceptual representations. Our method does not require PCQA-specific training or fine-tuning. Experiments on three public databases show that the proposed method consistently outperforms most generic baselines and achieves performance comparable to several full-reference metrics.The code is available at https://github.com/HC-W-whc/CCRD-NR-PCQA.git.