  We introduce Multi-Cylindrical Panoramic Depth Estimation (MCPDepth), a
two-stage framework for omnidirectional depth estimation via stereo matching
between multiple cylindrical panoramas. MCPDepth uses cylindrical panoramas for
initial stereo matching and then fuses the resulting depth maps across views. A
circular attention module is employed to overcome the distortion along the
vertical axis. MCPDepth exclusively utilizes standard network components,
simplifying deployment to embedded devices and outperforming previous methods
that require custom kernels. We theoretically and experimentally compare
spherical and cylindrical projections for stereo matching, highlighting the
advantages of the cylindrical projection. MCPDepth achieves state-of-the-art
performance with an 18.8% reduction in mean absolute error (MAE) for depth on
the outdoor synthetic dataset Deep360 and a 19.9% reduction on the indoor
real-scene dataset 3D60.
