Comparing Monocular Camera Depth Estimation Models for Real-time Applications

Engineering notes

We identify a set of metrics to benchmark the selected approaches from different aspects, especially those related to real-time applications.

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 (MDE) is a fundamental problem in the field of Computer Vision with ongoing developments. For the case of challenging applications such as autonomous driving, where highly accurate results are required in real-time, traditional approaches fall short due to insufficient information to understand the scene geometry. Novel approaches utilizing deep neural networks show significantly improved results, especially in autonomous driving applications. Nevertheless, there now exists a number of promising approaches in literature and their performance has never been compared head-to-head. In this paper, a detailed evaluation of the performance of four selected deep learning networks is presented. We identify a set of metrics to benchmark the selected approaches from different aspects, especially those related to real-time applications. We analyze the results and present insights into the performance levels of the various approaches.

Links and sources

• Official / arXiv page
• PDF from original source

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