ODI-Bench: Can MLLMs Understand Immersive Omnidirectional Environments?

• URL: http://arxiv.org/abs/2510.11549v1
• Date: Mon, 13 Oct 2025 15:51:47 GMT
• Title: ODI-Bench: Can MLLMs Understand Immersive Omnidirectional Environments?
• Authors: Liu Yang, Huiyu Duan, Ran Tao, Juntao Cheng, Sijing Wu, Yunhao Li, Jing Liu, Xiongkuo Min, Guangtao Zhai,
• Abstract summary: We first present ODI-Bench, a novel comprehensive benchmark specifically designed for omnidirectional image understanding.<n> Extensive experiments are conducted to benchmark 20 representative MLLMs, including proprietary and open-source models.<n>We further introduce Omni-CoT, a training-free method which significantly enhances MLLMs' comprehension ability in the omnidirectional environment.
• Score: 86.42854691331713
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 demonstrated 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. ODI-Bench contains 2,000 high-quality omnidirectional images and over 4,000 manually annotated question-answering (QA) pairs across 10 fine-grained tasks, covering both general-level and spatial-level ODI understanding. Extensive experiments are conducted to benchmark 20 representative MLLMs, including proprietary and open-source models, under both close-ended and open-ended settings. Experimental results reveal that current MLLMs still struggle to capture the immersive context provided by ODIs. To this end, we further introduce Omni-CoT, a training-free method which significantly enhances MLLMs' comprehension ability in the omnidirectional environment through chain-of-thought reasoning across both textual information and visual cues. Both the benchmark and the code will be released upon the publication.

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