Learning depth from monocular video sequences
Zhenwei Luo
Learning single image depth estimation model from monocular video sequence is
a very challenging problem. In this paper, we propose a novel training loss
which enables us to include more images for supervision during the training
process. We propose a simple yet effective model to account the frame to frame
pixel motion. We also design a novel network architecture for single image
estimation. When combined, our method produces state of the art results for
monocular depth estimation on the KITTI dataset in the self-supervised setting.