AI RESEARCH
ODI-Bench: Can MLLMs Understand Immersive Omnidirectional Environments?
arXiv CS.CV
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ArXi:2510.11549v2 Announce Type: replace Omnidirectional images (ODIs) provide full 360x180 view which are widely adopted in VR, AR and embodied intelligence applications. While multi-modal large language models (MLLMs) have nstrated remarkable performance on conventional 2D image and video understanding benchmarks, their ability to comprehend the immersive environments captured by ODIs remains largely unexplored. To address this gap, we first present ODI-Bench, a novel comprehensive benchmark specifically designed for omnidirectional image understanding.