Abstract
Omni-directional cameras have many advantages over
conventional cameras in that they have a much wider fieldof-view (FOV). Accordingly, several approaches have been
proposed recently to apply convolutional neural networks
(CNNs) to omni-directional images for various visual tasks.
However, most of them use image representations defined in
the Euclidean space after transforming the omni-directional
views originally formed in the non-Euclidean space. This
transformation leads to shape distortion due to nonuniform
spatial resolving power and the loss of continuity. These
effects make existing convolution kernels experience diffi-
culties in extracting meaningful information.
This paper presents a novel method to resolve such problems of applying CNNs to omni-directional images. The
proposed method utilizes a spherical polyhedron to represent omni-directional views. This method minimizes the
variance of the spatial resolving power on the sphere surface, and includes new convolution and pooling methods
for the proposed representation. The proposed method can
also be adopted by any existing CNN-based methods. The
feasibility of the proposed method is demonstrated through
classification, detection, and semantic segmentation tasks
with synthetic and real datasets.