提出直接评估带纹理3D模型的感知质量新方法
Textured Geometry Evaluation: Perceptual 3D Textured Shape Metric via 3D Latent-Geometry Network
- 基于3D网格与颜色信息联合计算真实感度，不依赖渲染
- 在真实世界失真数据集上超越现有方法
- 适合游戏、VR/AR等对3D质感要求高的场景
高保真带纹理3D模型在游戏、AR/VR和影视中至关重要，但当前人类感知对齐的评估方法仍落后于3D重建与生成技术进展。现有指标如Chamfer Distance难以反映人眼对3D形状保真度的判断。近期学习型指标虽尝试通过渲染图像与2D图像质量指标改进，但受限于结构覆盖不全、视角敏感，且多在合成失真数据上训练，与真实世界失真存在领域差距。为此，我们提出一种直接基于带纹理3D网格的保真度评估方法——文本化几何评估（TGE），联合使用几何与颜色信息，对比输入网格与参考彩色形状的保真度。为训练与评估该指标，我们构建了一个包含真实世界失真的人工标注数据集。实验表明，TGE在真实失真数据集上优于基于渲染与仅几何的方法。
原文摘要 · Abstract (English)
Textured high-fidelity 3D models are crucial for games, AR/VR, and film, but human-aligned evaluation methods still fall behind despite recent advances in 3D reconstruction and generation. Existing metrics, such as Chamfer Distance, often fail to align with how humans evaluate the fidelity of 3D shapes. Recent learning-based metrics attempt to improve this by relying on rendered images and 2D image quality metrics. However, these approaches face limitations due to incomplete structural coverage and sensitivity to viewpoint choices. Moreover, most methods are trained on synthetic distortions, which differ significantly from real-world distortions, resulting in a domain gap. To address these challenges, we propose a new fidelity evaluation method that is based directly on 3D meshes with texture, without relying on rendering. Our method, named Textured Geometry Evaluation TGE, jointly uses the geometry and color information to calculate the fidelity of the input textured mesh with comparison to a reference colored shape. To train and evaluate our metric, we design a human-annotated dataset with real-world distortions. Experiments show that TGE outperforms rendering-based and geometry-only methods on real-world distortion dataset.
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