外观
论文arXiv 2610.1766515 天前
RoTA-Bench：通过交互式投影对齐评估3D空间推理
原文链接：RoTA-Bench: Evaluating 3D Spatial Reasoning through Interactive Projection Alignment（arXiv 2610.17665 · 发布日期：2026-10-13）
RoTA-Bench: Evaluating 3D Spatial Reasoning through Interactive Projection Alignment
RoTA-Bench用交互式3D到2D投影对齐评估视觉语言模型的3D空间推理，并在构建任务集上验证。
核心要点
- 关注模型能否从2D观察形成支持精确动作的3D理解，包括几何、旋转和反馈修正。
- 给定目标轮廓，模型观察旋转物体并在有限动作预算内连续选择旋转恢复目标投影。
- 自动流程由2D图标构造保轮廓3D物体，并用可逆初始化保证任务可解。
- 实验显示GPT-5.6-Sol成功率仅17%，与20名本科生100%完成测试集存在明显差距。
验证范围与局限：验证范围限于674个投影对齐任务及分层100任务测试集。
英文摘要 (Abstract)
Can vision-language models turn 2D observations into 3D understanding that supports precise action? This requires inferring object geometry, anticipating the effects of rotation, and refining actions through visual feedback. We introduce RoTA-Bench to evaluate these abilities through interactive 3D-to-2D projection alignment. Given a target silhouette, a model observes a rotated object and chooses successive rotations to recover the target projection within a limited action budget. Our automated pipeline constructs silhouette-preserving 3D objects from 2D icons and uses reversible initialization to guarantee solvability, yielding 674 tasks and a stratified 100-task test set. Empirically, we find that (i) a substantial human-model gap persists: GPT-5.6-Sol succeeds on only 17% of tasks, whereas 20 undergraduate participants collectively complete the test set four times with a 100% success rate; (ii) image-space and pose-space progress can diverge: at matched states, the model more often improves silhouette overlap while worsening pose alignment than humans (19.4% vs. 10.9%); and (iii) fine-grained control is sensitive to interaction history: removing it leaves 96.8% of the model's actions at a coarse 30-degree step size and sharply reduces observed task success. These findings highlight the challenge of converting visual evidence into precise 3D control, beyond recognizing or matching individual images.
作者：Qingchuan Ma, Yuexiao Ma, Xiang Meng, Yixuan Zou, Tianyu Xie, Wang Chen, Yuhui Zeng, Yuhang Wu, Rongrong Ji, Xiawu Zheng
来源
本页事实对应的公开页面，以原文为准。