Disconnectivity and distortion are the two problems which must be coped with
when processing 360 degrees equirectangular images. In this paper, we propose a
method of estimating the depth of monocular panoramic image with a
teacher-student model fusing equirectangular and spherical representations. In
contrast with the existing methods fusing an equirectangular representation
with a cube map representation or tangent representation, a spherical
representation is a better choice because a sampling on a sphere is more
uniform and can also cope with distortion more effectively. In this processing,
a novel spherical convolution kernel computing with sampling points on a sphere
is developed to extract features from the spherical representation, and then, a
Segmentation Feature Fusion(SFF) methodology is utilized to combine the
features with ones extracted from the equirectangular representation. In
contrast with the existing methods using a teacher-student model to obtain a
lighter model of depth estimation, we use a teacher-student model to learn the
latent features of depth images. This results in a trained model which
estimates the depth map of an equirectangular image using not only the feature
maps extracted from an input equirectangular image but also the distilled
knowledge learnt from the ground truth of depth map of a training set. In
experiments, the proposed method is tested on several well-known 360 monocular
depth estimation benchmark datasets, and outperforms the existing methods for
the most evaluation indexes.

使用教师 - 学生模型融合环形和球形表达的方法，开发了一种新的球形卷积核计算方法和分割特征融合（SFF）方法来提取特征，并使用教师 - 学生模型学习深度图像的潜在特征，从而实现对环形图像深度图的估计。在实验中，该方法在 360 度单眼深度估计基准数据集上进行了测试，并且在大多数评估指标上优于现有方法。