  In this study, we present a method for all-around depth estimation from
multiple omnidirectional images for indoor environments. In particular, we
focus on plane-sweeping stereo as the method for depth estimation from the
images. We propose a new icosahedron-based representation and ConvNets for
omnidirectional images, which we name "CrownConv" because the representation
resembles a crown made of origami. CrownConv can be applied to both fisheye
images and equirectangular images to extract features. Furthermore, we propose
icosahedron-based spherical sweeping for generating the cost volume on an
icosahedron from the extracted features. The cost volume is regularized using
the three-dimensional CrownConv, and the final depth is obtained by depth
regression from the cost volume. Our proposed method is robust to camera
alignments by using the extrinsic camera parameters; therefore, it can achieve
precise depth estimation even when the camera alignment differs from that in
the training dataset. We evaluate the proposed model on synthetic datasets and
demonstrate its effectiveness. As our proposed method is computationally
efficient, the depth is estimated from four fisheye images in less than a
second using a laptop with a GPU. Therefore, it is suitable for real-world
robotics applications. Our source code is available at
https://github.com/matsuren/crownconv360depth.
