IR3D-Bench: Evaluating Vision-Language Model Scene Understanding as
  Agentic Inverse Rendering
Authors: name: Parker Liu, name: Chenxin Li, name: Zhengxin Li, name: Yipeng Wu, name: Wuyang Li, name: Zhiqin Yang, name: Zhenyuan Zhang, name: Yunlong Lin, name: Sirui Han, name: Brandon Y. Feng

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.