  Omnidirectional 3D information is essential for a wide range of applications
such as Virtual Reality, Autonomous Driving, Robotics, etc. In this paper, we
propose a novel, model-agnostic, two-stage pipeline for omnidirectional
monocular depth estimation. Our proposed framework PanoDepth takes one 360
image as input, produces one or more synthesized views in the first stage, and
feeds the original image and the synthesized images into the subsequent stereo
matching stage. In the second stage, we propose a differentiable Spherical
Warping Layer to handle omnidirectional stereo geometry efficiently and
effectively. By utilizing the explicit stereo-based geometric constraints in
the stereo matching stage, PanoDepth can generate dense high-quality depth. We
conducted extensive experiments and ablation studies to evaluate PanoDepth with
both the full pipeline as well as the individual modules in each stage. Our
results show that PanoDepth outperforms the state-of-the-art approaches by a
large margin for 360 monocular depth estimation.
