  Monocular omnidirectional depth estimation is receiving considerable research
attention due to its broad applications for sensing 360{\deg} surroundings.
Existing approaches in this field suffer from limitations in recovering small
object details and data lost during the ground-truth depth map acquisition. In
this paper, a novel monocular omnidirectional depth estimation model, namely
HiMODE is proposed based on a hybrid CNN+Transformer (encoder-decoder)
architecture whose modules are efficiently designed to mitigate distortion and
computational cost, without performance degradation. Firstly, we design a
feature pyramid network based on the HNet block to extract high-resolution
features near the edges. The performance is further improved, benefiting from a
self and cross attention layer and spatial/temporal patches in the Transformer
encoder and decoder, respectively. Besides, a spatial residual block is
employed to reduce the number of parameters. By jointly passing the deep
features extracted from an input image at each backbone block, along with the
raw depth maps predicted by the transformer encoder-decoder, through a context
adjustment layer, our model can produce resulting depth maps with better visual
quality than the ground-truth. Comprehensive ablation studies demonstrate the
significance of each individual module. Extensive experiments conducted on
three datasets; Stanford3D, Matterport3D, and SunCG, demonstrate that HiMODE
can achieve state-of-the-art performance for 360{\deg} monocular depth
estimation.
