Spherical cameras, which can acquire all-round information, are effective to estimate rotation for robotic applications. Recently, Convolutional Neural Networks have shown great robustness in solving such regression problems. However they are designed for planar images and cannot deal with the non-uniform distortion present in spherical images, when expressed in the planar equirectangular projection. This can lower the accuracy of motion estimation. In this research, we propose an Equirectangular-Convolutional Neural Network (E-CNN) to solve this issue. This novel network regresses 3D spherical camera rotation by uniformizing distorted optical flow patterns in the equirectangular projection. We experimentally show that this results in consistently lower error as opposed to learning from the distorted optical flow.
Published in: ICASSP 2019 – 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Date of Conference: 12-17 May 2019
Date Added to IEEE Xplore: 16 April 2019
INSPEC Accession Number: 18778074
DOI: 10.1109/ICASSP.2019.8682203
Publisher: IEEE
Conference Location: Brighton, UK