  Depth estimation, as a necessary clue to convert 2D images into the 3D space,
has been applied in many machine vision areas. However, to achieve an entire
surrounding 360-degree geometric sensing, traditional stereo matching
algorithms for depth estimation are limited due to large noise, low accuracy,
and strict requirements for multi-camera calibration. In this work, for a
unified surrounding perception, we introduce panoramic images to obtain larger
field of view. We extend PADENet first appeared in our previous conference work
for outdoor scene understanding, to perform panoramic monocular depth
estimation with a focus for indoor scenes. At the same time, we improve the
training process of the neural network adapted to the characteristics of
panoramic images. In addition, we fuse traditional stereo matching algorithm
with deep learning methods and further improve the accuracy of depth
predictions. With a comprehensive variety of experiments, this research
demonstrates the effectiveness of our schemes aiming for indoor scene
perception.
