  We propose a novel approach to compute high-resolution (2048x1024 and higher)
depths for panoramas that is significantly faster and qualitatively and
qualitatively more accurate than the current state-of-the-art method
(360MonoDepth). As traditional neural network-based methods have limitations in
the output image sizes (up to 1024x512) due to GPU memory constraints, both
360MonoDepth and our method rely on stitching multiple perspective disparity or
depth images to come out a unified panoramic depth map. However, to achieve
globally consistent stitching, 360MonoDepth relied on solving extensive
disparity map alignment and Poisson-based blending problems, leading to high
computation time. Instead, we propose to use an existing panoramic depth map
(computed in real-time by any panorama-based method) as the common target for
the individual perspective depth maps to register to. This key idea made
producing globally consistent stitching results from a straightforward task.
Our experiments show that our method generates qualitatively better results
than existing panorama-based methods, and further outperforms them
quantitatively on datasets unseen by these methods.
