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.

翻译：网格质量评估（MQA）模型在众多应用领域的网格操作系统设计、优化与评估中发挥着关键作用。当前的MQA模型，无论是利用拓扑感知特征的基于模型的方法，还是基于渲染二维投影的投影方法，通常难以捕捉纹理与三维几何之间复杂的相互作用。我们提出了HybridMQA，一种首创的混合式全参考彩色MQA框架，它集成了基于模型和基于投影的方法，通过捕捉纹理信息与三维结构之间的复杂交互来丰富质量表征。我们的方法采用图学习来提取详细的三维表征，然后通过一种新颖的特征渲染过程将其投影至二维，该过程能将其与彩色投影精确对齐。这使得能够通过交叉注意力机制探索几何-纹理交互，从而产生全面的网格质量表征。大量实验表明，HybridMQA在多个数据集上均表现出卓越的性能，突显了其有效利用几何-纹理交互以深入理解网格质量的能力。我们的实现代码将公开提供。