  Recently, end-to-end trainable deep neural networks have significantly
improved stereo depth estimation for perspective images. However, 360{\deg}
images captured under equirectangular projection cannot benefit from directly
adopting existing methods due to distortion introduced (i.e., lines in 3D are
not projected onto lines in 2D). To tackle this issue, we present a novel
architecture specifically designed for spherical disparity using the setting of
top-bottom 360{\deg} camera pairs. Moreover, we propose to mitigate the
distortion issue by (1) an additional input branch capturing the position and
relation of each pixel in the spherical coordinate, and (2) a cost volume built
upon a learnable shifting filter. Due to the lack of 360{\deg} stereo data, we
collect two 360{\deg} stereo datasets from Matterport3D and Stanford3D for
training and evaluation. Extensive experiments and ablation study are provided
to validate our method against existing algorithms. Finally, we show promising
results on real-world environments capturing images with two consumer-level
cameras.
