  Estimating the depths of equirectangular (i.e., 360) images (EIs) is
challenging given the distorted 180 x 360 field-of-view, which is hard to be
addressed via convolutional neural network (CNN). Although a transformer with
global attention achieves significant improvements over CNN for EI depth
estimation task, it is computationally inefficient, which raises the need for
transformer with local attention. However, to apply local attention
successfully for EIs, a specific strategy, which addresses distorted
equirectangular geometry and limited receptive field simultaneously, is
required. Prior works have only cared either of them, resulting in
unsatisfactory depths occasionally. In this paper, we propose an
equirectangular geometry-biased transformer termed EGformer. While limiting the
computational cost and the number of network parameters, EGformer enables the
extraction of the equirectangular geometry-aware local attention with a large
receptive field. To achieve this, we actively utilize the equirectangular
geometry as the bias for the local attention instead of struggling to reduce
the distortion of EIs. As compared to the most recent EI depth estimation
studies, the proposed approach yields the best depth outcomes overall with the
lowest computational cost and the fewest parameters, demonstrating the
effectiveness of the proposed methods.
