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.

提出一个名为TiO-Depth的两合一自监督深度估计网络，能同时处理单目和双目深度估计任务，并提高预测准确度。TiO-Depth采用Siamese架构，其中每个子网络可用作单目深度估计模型。对于双目深度估计，提出了一个单目特征匹配模块，用于融合两个图像之间的立体知识，并使用完整的TiO-Depth来预测深度。通过结合它们的相对优势，设计了一个多阶段联合训练策略，以提高TiO-Depth在两个任务中的性能。在KITTI、Cityscapes和DDAD数据集上的实验结果表明，TiO-Depth在大多数情况下优于目前最先进的单目和双目方法，并进一步验证了使用两合一网络进行单目和双目深度估计的可行性。

一体化深度：填补单目和双目自监督深度估计间的差距