# RBFIM: Perceptual Quality Assessment for Compressed Point Clouds Using Radial Basis Function Interpolation

arXiv: https://arxiv.org/abs/2503.14154 · HTML: https://mlsanity.com/paper/2503.14154/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 59 references that are in the map.
- First posted on arXiv 18 Mar 2025.

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

- [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)
- [Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compression](https://mlsanity.com/paper/2011.12688/) (2020)
- [GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation](https://mlsanity.com/paper/2211.16762/) (2022)
- [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)
- [GPA-Net:No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Network](https://mlsanity.com/paper/2210.16478/) (2022)
- [BASICS: Broad quality Assessment of Static point clouds In Compression Scenarios](https://mlsanity.com/paper/2302.04796/) (2023)
- [Attribute Artifacts Removal for Geometry-based Point Cloud Compression](https://mlsanity.com/paper/2112.00560/) (2021)
- [TCDM: Transformational Complexity Based Distortion Metric for Perceptual Point Cloud Quality Assessment](https://mlsanity.com/paper/2210.04671/) (2022)
- [Joint Geometry and Color Projection-based Point Cloud Quality Metric](https://mlsanity.com/paper/2108.02481/) (2021)
- [DDM: A Metric for Comparing 3D Shapes Using Directional Distance Fields](https://mlsanity.com/paper/2401.09736/) (2024)

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

- [UAV as Urban Construction Change Monitor: A New Benchmark and Change Captioning Model](https://mlsanity.com/paper/2605.04409/) (2026)
- [Objective Quality Assessment of Point Clouds Using Multi-scale Implicit Structural Similarity](https://mlsanity.com/paper/2601.01200/) (2026)

## Abstract (arXiv)

One of the main challenges in point cloud compression (PCC) is how to evaluate the perceived distortion so that the codec can be optimized for perceptual quality. Current standard practices in PCC highlight a primary issue: while single-feature metrics are widely used to assess compression distortion, the classic method of searching point-to-point nearest neighbors frequently fails to adequately build precise correspondences between point clouds, resulting in an ineffective capture of human perceptual features. To overcome the related limitations, we propose a novel assessment method called RBFIM, utilizing radial basis function (RBF) interpolation to convert discrete point features into a continuous feature function for the distorted point cloud. By substituting the geometry coordinates of the original point cloud into the feature function, we obtain the bijective sets of point features. This enables an establishment of precise corresponding features between distorted and original point clouds and significantly improves the accuracy of quality assessments. Moreover, this method avoids the complexity caused by bidirectional searches. Extensive experiments on multiple subjective quality datasets of compressed point clouds demonstrate that our RBFIM excels in addressing human perception tasks, thereby providing robust support for PCC optimization efforts.