  Self-supervised learning for depth estimation possesses several advantages
over supervised learning. The benefits of no need for ground-truth depth,
online fine-tuning, and better generalization with unlimited data attract
researchers to seek self-supervised solutions. In this work, we propose a new
self-supervised framework for stereo matching utilizing multiple images
captured at aligned camera positions. A cross photometric loss, an
uncertainty-aware mutual-supervision loss, and a new smoothness loss are
introduced to optimize the network in learning disparity maps end-to-end
without ground-truth depth information. To train this framework, we build a new
multiscopic dataset consisting of synthetic images rendered by 3D engines and
real images captured by real cameras. After being trained with only the
synthetic images, our network can perform well in unseen outdoor scenes. Our
experiment shows that our model obtains better disparity maps than previous
unsupervised methods on the KITTI dataset and is comparable to supervised
methods when generalized to unseen data. Our source code and dataset will be
made public, and more results are provided in the supplement.
