  Omni-directional cameras have many advantages overconventional cameras in
that they have a much wider field-of-view (FOV). Accordingly, several
approaches have beenproposed recently to apply convolutional neural
networks(CNNs) to omni-directional images for various visual tasks.However,
most of them use image representations defined inthe Euclidean space after
transforming the omni-directionalviews originally formed in the non-Euclidean
space. Thistransformation leads to shape distortion due to nonuniformspatial
resolving power and the loss of continuity. Theseeffects make existing
convolution kernels experience diffi-culties in extracting meaningful
information.This paper presents a novel method to resolve such prob-lems of
applying CNNs to omni-directional images. Theproposed method utilizes a
spherical polyhedron to rep-resent omni-directional views. This method
minimizes thevariance of the spatial resolving power on the sphere sur-face,
and includes new convolution and pooling methodsfor the proposed
representation. The proposed method canalso be adopted by any existing
CNN-based methods. Thefeasibility of the proposed method is demonstrated
throughclassification, detection, and semantic segmentation taskswith synthetic
and real datasets.
