  Depth estimation from a monocular 360{\deg} image is a burgeoning problem
owing to its holistic sensing of a scene. Recently, some methods, \eg,
OmniFusion, have applied the tangent projection (TP) to represent a
360{\deg}image and predicted depth values via patch-wise regressions, which are
merged to get a depth map with equirectangular projection (ERP) format.
However, these methods suffer from 1) non-trivial process of merging plenty of
patches; 2) capturing less holistic-with-regional contextual information by
directly regressing the depth value of each pixel. In this paper, we propose a
novel framework, \textbf{HRDFuse}, that subtly combines the potential of
convolutional neural networks (CNNs) and transformers by collaboratively
learning the \textit{holistic} contextual information from the ERP and the
\textit{regional} structural information from the TP. Firstly, we propose a
spatial feature alignment (\textbf{SFA}) module that learns feature
similarities between the TP and ERP to aggregate the TP features into a
complete ERP feature map in a pixel-wise manner. Secondly, we propose a
collaborative depth distribution classification (\textbf{CDDC}) module that
learns the \textbf{holistic-with-regional} histograms capturing the ERP and TP
depth distributions. As such, the final depth values can be predicted as a
linear combination of histogram bin centers. Lastly, we adaptively combine the
depth predictions from ERP and TP to obtain the final depth map. Extensive
experiments show that our method predicts\textbf{ more smooth and accurate
depth} results while achieving \textbf{favorably better} results than the SOTA
methods.
