Two-in-One Depth: Bridging the Gap Between Monocular and Binocular
  Self-supervised Depth Estimation
Zhengming Zhou Qiulei Dong
Monocular and binocular self-supervised depth estimations are two important
and related tasks in computer vision, which aim to predict scene depths from
single images and stereo image pairs respectively. In literature, the two tasks
are usually tackled separately by two different kinds of models, and binocular
models generally fail to predict depth from single images, while the prediction
accuracy of monocular models is generally inferior to binocular models. In this
paper, we propose a Two-in-One self-supervised depth estimation network, called
TiO-Depth, which could not only compatibly handle the two tasks, but also
improve the prediction accuracy. TiO-Depth employs a Siamese architecture and
each sub-network of it could be used as a monocular depth estimation model. For
binocular depth estimation, a Monocular Feature Matching module is proposed for
incorporating the stereo knowledge between the two images, and the full
TiO-Depth is used to predict depths. We also design a multi-stage
joint-training strategy for improving the performances of TiO-Depth in both two
tasks by combining the relative advantages of them. Experimental results on the
KITTI, Cityscapes, and DDAD datasets demonstrate that TiO-Depth outperforms
both the monocular and binocular state-of-the-art methods in most cases, and
further verify the feasibility of a two-in-one network for monocular and
binocular depth estimation. The code is available at
https://github.com/ZM-Zhou/TiO-Depth_pytorch.