用状态空间模型统一预测有无纹理网格的视觉显著区域。
Mesh Mamba: A Unified State Space Model for Saliency Prediction in Non-Textured and Textured Meshes
- 基于状态空间模型，融合几何结构与纹理特征进行建模。
- 在有/无纹理网格上均实现显著性预测性能提升。
- 适合3D视觉、图形分析等领域的研究者参考。
网格显著性通过识别和强调自然吸引视觉注意的区域，增强了3D视觉的适应性。为探究几何结构与纹理在塑造视觉注意中的相互作用，我们构建了一个综合性网格显著性数据集，首次系统捕捉了有纹理与无纹理条件下显著性分布的差异。此外，我们提出Mesh Mamba，一种基于状态空间模型（SSM）的统一显著性预测模型，可适配多种网格类型。该模型有效分析网格几何结构，并将纹理特征无缝融入拓扑框架，确保外观增强建模的一致性。更重要的是，通过子图嵌入与双向SSM，模型实现了对局部几何与纹理的全局上下文建模，保留拓扑结构，提升对视觉细节与结构复杂性的理解。通过广泛的理论与实证验证，该模型不仅在多种网格类型上表现更优，还展现出高可扩展性与通用性，尤其在不同视觉特征的跨验证中表现突出。
原文摘要 · Abstract (English)
Mesh saliency enhances the adaptability of 3D vision by identifying and emphasizing regions that naturally attract visual attention. To investigate the interaction between geometric structure and texture in shaping visual attention, we establish a comprehensive mesh saliency dataset, which is the first to systematically capture the differences in saliency distribution under both textured and non-textured visual conditions. Furthermore, we introduce mesh Mamba, a unified saliency prediction model based on a state space model (SSM), designed to adapt across various mesh types. Mesh Mamba effectively analyzes the geometric structure of the mesh while seamlessly incorporating texture features into the topological framework, ensuring coherence throughout appearance-enhanced modeling. More importantly, by subgraph embedding and a bidirectional SSM, the model enables global context modeling for both local geometry and texture, preserving the topological structure and improving the understanding of visual details and structural complexity. Through extensive theoretical and empirical validation, our model not only improves performance across various mesh types but also demonstrates high scalability and versatility, particularly through cross validations of various visual features.
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