360{\deg} Image Perception with MLLMs: A Comprehensive Benchmark and a Training-Free Method

Abstract

Multimodal Large Language Models (MLLMs) have shown impressive abilities in understanding and reasoning over conventional images. However, their perception of 360{\deg} images remains largely underexplored. Unlike conventional images, 360{\deg} 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{\deg} images, we introduce 360Bench, a Visual Question Answering (VQA) benchmark featuring 7K-resolution 360{\deg} 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{\deg} image perception. To address these challenges, we propose Free360, a training-free scene-graph-based framework for high-resolution 360{\deg} VQA. Free360 decomposes the reasoning process into modular steps, applies adaptive spherical image transformations to 360{\deg} images tailored to each step, and seamlessly integrates the resulting information into a unified graph representation for answer generation. Experiments show that Free360 consistently improves its base MLLM and provides a strong training-free solution for 360{\deg} VQA tasks. The source code and dataset will be publicly released upon acceptance.