MS-ISSM: Objective Quality Assessment of Point Clouds Using Multi-scale Implicit Structural Similarity

TL;DR

This paper introduces MS-ISSM, a novel point cloud quality assessment method using multi-scale implicit structural similarity and a specialized neural network to improve accuracy and robustness.

Contribution

The paper proposes a new implicit feature representation and a hierarchical neural network for more reliable point cloud quality assessment.

Findings

MS-ISSM outperforms existing metrics on multiple benchmarks.

The hierarchical network preserves physical semantics of different features.

Implicit function coefficients effectively measure local feature distortions.

Abstract

The unstructured and irregular nature of points poses a significant challenge for accurate point cloud quality assessment (PCQA), particularly in establishing accurate perceptual feature correspondence. To tackle this, we propose the Multi-scale Implicit Structural Similarity Measurement (MS-ISSM). Unlike traditional point-to-point matching, MS-ISSM utilizes radial basis function (RBF) to represent local features continuously, transforming distortion measurement into a comparison of implicit function coefficients. This approach effectively circumvents matching errors inherent in irregular data. Additionally, we propose a ResGrouped-MLP quality assessment network, which robustly maps multi-scale feature differences to perceptual scores. The network architecture departs from traditional flat multi-layer perceptron (MLP) by adopting a grouped encoding strategy integrated with residual blocks and channel-wise attention mechanisms. This hierarchical design allows the model to preserve the distinct physical semantics of luma, chroma, and geometry while adaptively focusing on the most salient distortion features across High, Medium, and Low scales. Experimental results on multiple benchmarks demonstrate that MS-ISSM outperforms state-of-the-art metrics in both reliability and generalization. The source code is available at: https://github.com/ZhangChen2022/MS-ISSM.