[论文] P3D-Bench: Benchmarking MLLMs for Parametric 3D Generation and Structu...

论文概要

研究领域: CV
作者: Yikang Yang, Zhanpeng Hu, Youtian Lin, Mengqi Zhou, Jingxi Xu, Feihu Zhang, Jiaheng Liu, Yao Yao
发布时间: 2026-06-09
arXiv: 2606.11152

中文摘要

P3D-Bench是评估多模态大语言模型（MLLM）参数化3D生成和结构推理的基准。不同于3D网格，参数化3D程序暴露显式尺寸、构造操作和部件关系，揭示模型是否恢复设计结构而非仅外观。覆盖文本到3D、图像到3D和装配3D三个任务族，评估可执行性、几何保真度、拓扑、文本约束、多视图语义对齐和部件级结构。评估发现：装配是最难设置，模型能恢复全局形状但难复现精确参数几何，部件级建模仍薄弱。

原文摘要

Multimodal large language models can write code to produce complex programs as well as use programs to do 3D modeling, which opens up a new avenue for 3D generation powered by their priors, world knowledge and reasoning. Yet existing benchmarks rarely evaluate 3D modeling through code. Such modeling demands more than runnable code: from a text or visual specification, a model must generate a parametric 3D program that is geometrically precise, semantically aligned and assembly-consistent. We introduce P3D-Bench, a benchmark for parametric 3D generation. Unlike a 3D mesh, a parametric 3D program exposes explicit dimensions, construction operations and part relations, revealing whether a model recovers a design's structure, not just its appearance. Under a unified protocol, P3D-Bench covers ...

自动采集于 2026-06-11

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