测试顶级视觉语言模型用代码重建3D模型的能力，发现工具调用越丰富效果越好。
3DHarnessBench: Probing Agentic 3D-to-Code Capabilities of Frontier Vision-Language Models
- 设计四层递进式评估框架，支持主动探索与工具调用
- 模型在完整控制3D场景时的代码生成准确率显著提升
- 适合研究多模态大模型智能体能力的研究者参考
我们提出3DHarnessBench，一个用于评估前沿视觉语言模型（VLMs）从多种输入中恢复3D几何结构为Blender Python代码的智能体能力的基准。不同于以往仅使用固定输入（如单视角图像或文本描述）的方法，3DHarnessBench通过四种渐进式测试设置评估模型：单视图、多视图、主动视觉（任意视角访问）和全3D交互（通过Blender函数调用完全访问目标对象）。该框架系统考察模型在视觉感知、主动推理、工具调用与自我修正方面的能力。实验表明，所有前沿模型在获得更丰富的函数调用权限后，3D几何恢复能力均显著提升，但提升幅度高度依赖具体模型，暴露出当前模型在3D-to-code智能体能力上的巨大差异。我们将公开基准、代码、输出结果及智能体轨迹，以支持可复现的3D评估。
原文摘要 · Abstract (English)
We introduce 3DHarnessBench, a benchmark that evaluates the agentic ability of frontier vision-language models (VLMs) to recover 3D geometry as Blender Python code from a variety of inputs. Unlike previous frameworks that prompt the VLMs with a fixed input (e.g., a single rendering or a text description), 3DHarnessBench evaluates four separate harness settings that progressively enable active agentic exploration, facilitated by recent Blender MCP functionality. Our hierarchy from Single-view, Multi-view, Active Visual (arbitrary viewpoint access), and Full 3D Interaction (complete access to the target object through Blender function calls) probes the models' abilities in both visual perception and active inference, tool calling, and self-correction. We observe that the ability of all frontier models to recover 3D geometry improves significantly with richer function call access, although the improvements are strongly model-dependent, revealing highly uneven agentic 3D-to-code capabilities. We will release the benchmark, code, outputs, and agent trajectories for reproducible 3D evaluation.
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