  Ambisonics i.e., a full-sphere surround sound, is quintessential with
360-degree visual content to provide a realistic virtual reality (VR)
experience. While 360-degree visual content capture gained a tremendous boost
recently, the estimation of corresponding spatial sound is still challenging
due to the required sound-field microphones or information about the
sound-source locations. In this paper, we introduce a novel problem of
generating Ambisonics in 360-degree videos using the audio-visual cue. With
this aim, firstly, a novel 360-degree audio-visual video dataset of 265 videos
is introduced with annotated sound-source locations. Secondly, a pipeline is
designed for an automatic Ambisonic estimation problem. Benefiting from the
deep learning-based audio-visual feature-embedding and prediction modules, our
pipeline estimates the 3D sound-source locations and further use such locations
to encode to the B-format. To benchmark our dataset and pipeline, we
additionally propose evaluation criteria to investigate the performance using
different 360-degree input representations. Our results demonstrate the
efficacy of the proposed pipeline and open up a new area of research in
360-degree audio-visual analysis for future investigations.
