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
this https URL

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。