  Nowadays 360 video analysis has become a significant research topic in the
field since the appearance of high-quality and low-cost 360 wearable devices.
In this paper, we propose a novel LiteFlowNet360 architecture for 360 videos
optical flow estimation. We design LiteFlowNet360 as a domain adaptation
framework from perspective video domain to 360 video domain. We adapt it from
simple kernel transformation techniques inspired by Kernel Transformer Network
(KTN) to cope with inherent distortion in 360 videos caused by the
sphere-to-plane projection. First, we apply an incremental transformation of
convolution layers in feature pyramid network and show that further
transformation in inference and regularization layers are not important, hence
reducing the network growth in terms of size and computation cost. Second, we
refine the network by training with augmented data in a supervised manner. We
perform data augmentation by projecting the images in a sphere and
re-projecting to a plane. Third, we train LiteFlowNet360 in a self-supervised
manner using target domain 360 videos. Experimental results show the promising
results of 360 video optical flow estimation using the proposed novel
architecture.
