IR3D-Bench: Evaluating Vision-Language Model Scene Understanding as Agentic Inverse Rendering

Liu Hengyu, Chenxin Li, Zhengxin Li, Yipeng Wu, Wuyang Li, zhiqin yang, Zhenyuan Zhang, Yunlong Lin, Sirui Han, Brandon Y. Feng

IR3D-Bench: Evaluating Vision-Language Model Scene Understanding as Agentic Inverse Rendering: 8 upvotes on Hugging Face Daily Papers, #13 of 18 papers on 2025-07-02. Day-by-day upvote history.

Vision-language models (VLMs) excel at descriptive tasks, but whether they truly understand scenes from visual observations remains uncertain. We introduce IR3D-Bench, a benchmark challenging VLMs to demonstrate understanding through active creation rather than passive recognition. Grounded in the analysis-by-synthesis paradigm, IR3D-Bench tasks Vision-Language Agents (VLAs) with actively using programming and rendering tools to recreate the underlying 3D structure of an input image, achieving agentic inverse rendering through tool use. This "understanding-by-creating" approach probes the tool-using generative capacity of VLAs, moving beyond the descriptive or conversational capacity measured by traditional scene understanding benchmarks. We provide a comprehensive suite of metrics to evaluate geometric accuracy, spatial relations, appearance attributes, and overall plausibility. Initial experiments on agentic inverse rendering powered by various state-of-the-art VLMs highlight current limitations, particularly in visual precision rather than basic tool usage. IR3D-Bench, including data and evaluation protocols, is released to facilitate systematic study and development of tool-using VLAs towards genuine scene understanding by creating.

Paper page on Hugging Face · arXiv

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