Monocular 360 depth estimation is challenging due to the inherent distortion
of the equirectangular projection (ERP). This distortion causes a problem:
spherical adjacent points are separated after being projected to the ERP plane,
particularly in the polar regions. To tackle this problem, recent methods
calculate the spherical neighbors in the tangent domain. However, as the
tangent patch and sphere only have one common point, these methods construct
neighboring spherical relationships around the common point. In this paper, we
propose spherical fully-connected CRFs (SF-CRFs). We begin by evenly
partitioning an ERP image with regular windows, where windows at the equator
involve broader spherical neighbors than those at the poles. To improve the
spherical relationships, our SF-CRFs enjoy two key components. Firstly, to
involve sufficient spherical neighbors, we propose a Spherical Window Transform
(SWT) module. This module aims to replicate the equator window's spherical
relationships to all other windows, leveraging the rotational invariance of the
sphere. Remarkably, the transformation process is highly efficient, completing
the transformation of all windows in a 512X1024 ERP with 0.038 seconds on CPU.
Secondly, we propose a Planar-Spherical Interaction (PSI) module to facilitate
the relationships between regular and transformed windows, which not only
preserves the local details but also captures global structures. By building a
decoder based on the SF-CRFs blocks, we propose CRF360D, a novel 360 depth
estimation framework that achieves state-of-the-art performance across diverse
datasets. Our CRF360D is compatible with different perspective image-trained
backbones (e.g., EfficientNet), serving as the encoder.

通过构建球形全连接 CRF 并引入球形窗口变换和平面 - 球面交互模块，提出了一种新的 360 度深度估计框架 CRF360D，可以在不同数据集上实现最先进的性能。