A Survey on Self-Supervised Monocular Depth Estimation Based on Deep Neural Networks
Qiulei Dong, Zhengming Zhou, Xiaolan Qiu, Liting Zhang
Abstract
Monocular depth estimation aims to predict the corresponding scene depth map to an input image, which has wide application prospects in various fields, such as robot navigation, autonomous driving, and augmented reality. Due to the advantage that only images rather than ground truth depth maps are required for model training, self-supervised monocular depth estimation methods have received more and more attention in recent years. Although numerous self-supervised monocular depth estimation methods were proposed, there has been no a comprehensive survey on them yet. Addressing this issue, we review recent developments in the community of self-supervised monocular depth estimation in this article. First, 89 existing works in the literature are categorized and reviewed. Then, we introduce the public datasets and evaluation metrics used in monocular depth estimation. Next, the performances of some state-of-the-art methods are compared and analyzed. Finally, we summarize several open problems and possible future developments in this community.