A Survey on Self-Supervised Monocular Depth Estimation Based on Deep Neural Networks

Engineering notes

Next, the performances of some state-of-the-art methods are compared and analyzed.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original 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.

Links and sources

• Official / arXiv page

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