  In this work, we introduce VQA 360, a novel task of visual question answering
on 360 images. Unlike a normal field-of-view image, a 360 image captures the
entire visual content around the optical center of a camera, demanding more
sophisticated spatial understanding and reasoning. To address this problem, we
collect the first VQA 360 dataset, containing around 17,000 real-world
image-question-answer triplets for a variety of question types. We then study
two different VQA models on VQA 360, including one conventional model that
takes an equirectangular image (with intrinsic distortion) as input and one
dedicated model that first projects a 360 image onto cubemaps and subsequently
aggregates the information from multiple spatial resolutions. We demonstrate
that the cubemap-based model with multi-level fusion and attention diffusion
performs favorably against other variants and the equirectangular-based models.
Nevertheless, the gap between the humans' and machines' performance reveals the
need for more advanced VQA 360 algorithms. We, therefore, expect our dataset
and studies to serve as the benchmark for future development in this
challenging task. Dataset, code, and pre-trained models are available online.
