[2603.16179] 360° Image Perception with MLLMs: A Comprehensive Benchmark and a Training-Free Method

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.16179 (cs) [Submitted on 17 Mar 2026 (v1), last revised 26 Mar 2026 (this version, v2)] Title:360° Image Perception with MLLMs: A Comprehensive Benchmark and a Training-Free Method Authors:Huyen T. T. Tran, Van-Quang Nguyen, Farros Alferro, Kang-Jun Liu, Takayuki Okatani View a PDF of the paper titled 360{\deg} Image Perception with MLLMs: A Comprehensive Benchmark and a Training-Free Method, by Huyen T. T. Tran and 4 other authors View PDF HTML (experimental) Abstract:Multimodal Large Language Models (MLLMs) have shown impressive abilities in understanding and reasoning over conventional images. However, their perception of 360° images remains largely underexplored. Unlike conventional images, 360° images capture the entire surrounding environment, enabling holistic spatial reasoning but introducing challenges such as geometric distortion and complex spatial relations. To comprehensively assess MLLMs' capabilities to perceive 360° images, we introduce 360Bench, a Visual Question Answering (VQA) benchmark featuring 7K-resolution 360° images, seven representative (sub)tasks with annotations carefully curated by human annotators. Using 360Bench, we systematically evaluate seven MLLMs and six enhancement methods, revealing their shortcomings in 360° image perception. To address these challenges, we propose Free360, a training-free scene-graph-based framework for high-resolution 360° VQA. Free360 decomposes ...