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

• URL: http://arxiv.org/abs/2509.06266v2
• Date: Tue, 30 Sep 2025 04:28:17 GMT
• Title: Spatial Reasoning with Vision-Language Models in Ego-Centric Multi-View Scenes
• Authors: Mohsen Gholami, Ahmad Rezaei, Zhou Weimin, Sitong Mao, Shunbo Zhou, Yong Zhang, Mohammad Akbari,
• Abstract summary: Understanding 3D spatial relationships remains a major limitation of current Vision-Language Models (VLMs)<n>We introduce Ego3D-Bench, a new benchmark designed to evaluate the spatial reasoning abilities of VLMs using ego-centric, multi-view outdoor data.<n>We propose Ego3D-VLM, a post-training framework that enhances 3D spatial reasoning of VLMs.
• Score: 14.268621981134293
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Ego3D-Bench comprises over 8,600 QA pairs, created with significant involvement from human annotators to ensure quality and diversity. We benchmark 16 SOTA VLMs, including GPT-4o, Gemini1.5-Pro, InternVL3, and Qwen2.5-VL. Our results reveal a notable performance gap between human level scores and VLM performance, highlighting that current VLMs still fall short of human level spatial understanding. To bridge this gap, we propose Ego3D-VLM, a post-training framework that enhances 3D spatial reasoning of VLMs. Ego3D-VLM generates cognitive map based on estimated global 3D coordinates, resulting in 12% average improvement on multi-choice QA and 56% average improvement on absolute distance estimation. Ego3D-VLM is modular and can be integrated with any existing VLM. Together, Ego3D-Bench and Ego3D-VLM offer valuable tools for advancing toward human level spatial understanding in real-world, multi-view environments.

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