  In this paper, we propose a learning-based method for predicting dense depth
values of a scene from a monocular omnidirectional image. An omnidirectional
image has a full field-of-view, providing much more complete descriptions of
the scene than perspective images. However, fully-convolutional networks that
most current solutions rely on fail to capture rich global contexts from the
panorama. To address this issue and also the distortion of equirectangular
projection in the panorama, we propose Cubemap Vision Transformers (CViT), a
new transformer-based architecture that can model long-range dependencies and
extract distortion-free global features from the panorama. We show that cubemap
vision transformers have a global receptive field at every stage and can
provide globally coherent predictions for spherical signals. To preserve
important local features, we further design a convolution-based branch in our
pipeline (dubbed GLPanoDepth) and fuse global features from cubemap vision
transformers at multiple scales. This global-to-local strategy allows us to
fully exploit useful global and local features in the panorama, achieving
state-of-the-art performance in panoramic depth estimation.
