EGOSR-BENCH: UNVEILING MULTI-HOP EGOCENTRIC SPATIAL REASONING IN PANORAMIC ENVIRONMENTS

Understanding complex spatial relations from an egocentric perspective is fundamental for embodied agents to navigate, reason, and interact within real-world panoramic environments. While recent advances in three-dimensional (3D) scene understanding and visual question answering (VQA) have improved object recognition and grounding, structured multi-hop spatial reasoning remains significantly underexplored, especially under single-view constraints such as 360° panoramic observations. In this work, we introduce EgoSR-Bench, a benchmark for multi-hop spatial reasoning from panoramic egocentric input, challenging models to infer fine-grained spatial relations solely from a single red-green-blue (RGB) 360° observation. We propose a structured question-generation framework that leverages 3D scene geometry, object-level attributes, and large language model (LLM)-based linguistic refinement to construct logically grounded questions. Based on this framework, we build OcPano, the released dataset and manifest underlying EgoSR-Bench, with 3,577 structured instances from 15 scenes, multilingual variants, and dense spatial-semantic annotations. We further define evaluation protocols for diagnosing spatial reasoning performance and use post-training to study benchmark learnability.