HybridMQA: Exploring Geometry-Texture Interactions for Colored Mesh Quality Assessment

TL;DR

HybridMQA introduces a novel hybrid framework that combines model-based and projection-based methods to effectively capture geometry-texture interactions for improved colored mesh quality assessment.

Contribution

It is the first to integrate graph learning and feature rendering with cross-attention to analyze geometry-texture interactions in mesh quality evaluation.

Findings

HybridMQA outperforms existing methods on multiple datasets.

The approach effectively captures complex geometry-texture interactions.

Experimental results show significant improvements in mesh quality assessment accuracy.

Abstract

Mesh quality assessment (MQA) models play a critical role in the design, optimization, and evaluation of mesh operation systems in a wide variety of applications. Current MQA models, whether model-based methods using topology-aware features or projection-based approaches working on rendered 2D projections, often fail to capture the intricate interactions between texture and 3D geometry. We introduce HybridMQA, a first-of-its-kind hybrid full-reference colored MQA framework that integrates model-based and projection-based approaches, capturing complex interactions between textural information and 3D structures for enriched quality representations. Our method employs graph learning to extract detailed 3D representations, which are then projected to 2D using a novel feature rendering process that precisely aligns them with colored projections. This enables the exploration of geometry-texture interactions via cross-attention, producing comprehensive mesh quality representations. Extensive experiments demonstrate HybridMQA's superior performance across diverse datasets, highlighting its ability to effectively leverage geometry-texture interactions for a thorough understanding of mesh quality. Our implementation will be made publicly available.