构建首个3D网格评估数据集与基准，提升自动评价准确性。
DB-3DME: From Dataset to Benchmark for Human-aligned Automatic 3D Mesh Evaluation
- 构建包含2619个合成网格的评估数据集，含人工评分。
- 发现3D视觉编码是影响评估性能的关键因素。
- 微调开源多模态模型，实现更贴近人类判断的自动评估。
3D生成技术虽显著提升真实感、可控性与效率，但3D资产评估仍缺乏系统研究。现有评估方法如人工评价、学习型指标及视觉-语言模型（VLM）作为裁判，存在成本高、可扩展性差、分辨率处理难或任务对齐不足等问题。本文聚焦3D网格评估，提出DB-3DME——3D网格评估的数据集与基准。该数据集包含2,619个合成3D网格及其在几何与提示一致性上的人工评分。基于此，我们系统性地评测了当前主流VLM，并发现3D表示的视觉编码是实现人类对齐评估性能的关键因素。受此启发，我们通过适配视觉编码器、冻结语言模型的方式，对开源多模态模型Qwen-2.5-VL-7B进行微调，使其在多个评估维度上显著优于现有预训练VLM，确立了新的自动3D网格评估基准。相关数据集已公开于GitHub与Hugging Face，以促进后续研究。
原文摘要 · Abstract (English)
Recent advances in 3D generation have led to substantial improvements in realism, controllability, and efficiency, yet the evaluation of 3D assets remains underexplored. Existing evaluation paradigms, including human evaluation, learned metrics, and vision-language models (VLMs) as judges, suffer from limitations in cost, scalability, resolution handling, or task-specific alignment. In this work, we focus on 3D mesh evaluation and introduce DB-3DME, the Dataset and Benchmark for 3D Mesh Evaluation. DB-3DME contains 2,619 synthetic 3D meshes paired with human ratings on Geometry and Prompt Adherence. Using this dataset, we systematically benchmark state-of-the-art VLMs and identify visual encoding of 3D representations as a key factor for human-aligned evaluation performance. Motivated by this finding, we fine-tune an open-weight VLM, Qwen-2.5-VL-7B, for 3D mesh evaluation by adapting the visual encoder while freezing the language model. The fine-tuned model substantially outperforms existing pre-trained VLMs across multiple evaluation dimensions, establishing a new benchmark for automatic 3D mesh evaluation. We publicly release the benchmark dataset on GitHub and Hugging Face to facilitate future research.
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