Depth estimation plays an important role in the robotic perception system.
Self-supervised monocular paradigm has gained significant attention since it
can free training from the reliance on depth annotations. Despite recent
advancements, existing self-supervised methods still underutilize the available
training data, limiting their generalization ability. In this paper, we take
two data augmentation techniques, namely Resizing-Cropping and
Splitting-Permuting, to fully exploit the potential of training datasets.
Specifically, the original image and the generated two augmented images are fed
into the training pipeline simultaneously and we leverage them to conduct
self-distillation. Additionally, we introduce the detail-enhanced DepthNet with
an extra full-scale branch in the encoder and a grid decoder to enhance the
restoration of fine details in depth maps. Experimental results demonstrate our
method can achieve state-of-the-art performance on the KITTI benchmark, with
both raw ground truth and improved ground truth. Moreover, our models also show
superior generalization performance when transferring to Make3D and NYUv2
datasets. Our codes are available at this https URL

通过使用数据增强技术和细节增强的 DepthNet，该文提出了一种自我监督单目深度估计方法，拓展了训练数据的利用，并在 KITTI、Make3D 和 NYUv2 数据集上实现了最先进的性能。