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arXiv · 2605.14704

SceneFunRI: Reasoning the Invisible for Task-Driven Functional Object Localization

Posheng Chen·Powen Cheng·Gueter Josmy Faure·Hung-Ting Su·Winston H. Hsu
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Abstract

In real-world scenes, target objects may reside in regions that are not visible. While humans can often infer the locations of occluded objects from context and commonsense knowledge, this capability remains a major challenge for vision-language models (VLMs). To address this gap, we introduce SceneFunRI, a benchmark for Reasoning the Invisible. Based on the SceneFun3D dataset, SceneFunRI formulates the task as a 2D spatial reasoning problem via a semi-automatic pipeline and comprises 855 instances. It requires models to infer the locations of invisible functional objects from task instructions and commonsense reasoning. The strongest baseline model (Gemini 3 Flash) only achieves an CAcc@75 of 15.20, an mIoU of 0.74, and a Dist of 28.65. We group our prompting analysis into three categories: Strong Instruction Prompting, Reasoning-based Prompting, and Spatial Process of Elimination (SPoE). These findings indicate that invisible-region reasoning remains an unstable capability in current VLMs, motivating future work on models that more tightly integrate task intent, commonsense priors, spatial grounding, and uncertainty-aware search.

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Posheng Chen, Powen Cheng, Gueter Josmy Faure, Hung-Ting Su, Winston H. Hsu. 2026-05-14. SceneFunRI: Reasoning the Invisible for Task-Driven Functional Object Localization. https://arxiv.org/abs/2605.14704

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