  Due to difficulties in acquiring ground truth depth of equirectangular (360)
images, the quality and quantity of equirectangular depth data today is
insufficient to represent the various scenes in the world. Therefore, 360 depth
estimation studies, which relied solely on supervised learning, are destined to
produce unsatisfactory results. Although self-supervised learning methods
focusing on equirectangular images (EIs) are introduced, they often have
incorrect or non-unique solutions, causing unstable performance. In this paper,
we propose 360 monocular depth estimation methods which improve on the areas
that limited previous studies. First, we introduce a self-supervised 360 depth
learning method that only utilizes gravity-aligned videos, which has the
potential to eliminate the needs for depth data during the training procedure.
Second, we propose a joint learning scheme realized by combining supervised and
self-supervised learning. The weakness of each learning is compensated, thus
leading to more accurate depth estimation. Third, we propose a non-local fusion
block, which can further retain the global information encoded by vision
transformer when reconstructing the depths. With the proposed methods, we
successfully apply the transformer to 360 depth estimations, to the best of our
knowledge, which has not been tried before. On several benchmarks, our approach
achieves significant improvements over previous works and establishes a state
of the art.
