Depth estimation is an important task, applied in various methods and applications of computer vision. While the traditional methods of estimating depth are based on depth cues and require specific equipment such as stereo cameras and configuring input according to the approach being used, the focus at the current time is on a single source, or monocular, depth estimation. The recent developments in Convolution Neural Networks along with the integration of classical methods in these deep learning approaches have led to a lot of advancements in the depth estimation problem. The problem of outdoor depth estimation, or depth estimation in wild, is a very scarcely researched field of study. In this paper, we give an overview of the available datasets, depth estimation methods, research work, trends, challenges, and opportunities that exist for open research. To our knowledge, no openly available survey work provides a comprehensive collection of outdoor depth estimation techniques and research scope, making our work an essential contribution for people looking to enter this field of study.
翻译:深度估算是一项重要任务,应用到各种计算机视野的方法和应用中。虽然传统的深度估算方法以深度提示为基础,需要特定设备,如立体摄像机和根据所用方法配置投入,但目前的重点是单一来源,或单眼深度估算。进化神经网络的最新发展,加上将传统方法纳入这些深层次学习方法,导致深度估算问题取得许多进展。户外深度估算或野外深度估算问题是一个研究非常稀少的领域。在本文件中,我们概述了现有的数据集、深度估算方法、研究工作、趋势、挑战和开放研究的机会。据我们所知,没有公开的调查报告全面收集户外深度估算技术和研究范围,使我们的工作成为希望进入这一研究领域的人的基本贡献。