Spatial Reasoning with Vision-Language Models in Ego-Centric Multi-View Scenes

Understand

Understanding 3D spatial relationships remains a major limitation of current Vision-Language Models (VLMs).

• Prior work has addressed this issue by creating spatial question-answering (QA) datasets based on single images or indoor videos.
• However, real-world embodied AI agents such as robots and self-driving cars typically rely on ego-centric, multi-view observations.
• To this end, we introduce Ego3D-Bench, a new benchmark designed to evaluate the spatial reasoning abilities of VLMs using ego-centric, multi-view outdoor data.