Evaluation of Metric Monocular Depth Estimation Models Under Adverse WeatherConditions in Driving Scenarios

Engineering notes

In this work, we evaluate three recently proposed models that represent the state-of-the-art (Depth Anything, PackNet-SfM, and UnidDepth) using zero-shot testing on the DrivingStereo dataset across diverse weather conditions, and benchmark their performance. While UniDepth achieves notable improvements over Depth Anything and PackNet-SfM, our results show that substantial progress is still needed for robust real-world deployment.

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

Abstract. Metric monocular depth estimation has become increasingly important and is often used as a redundancy mechanism in autonomous driving, where accurate scene understanding is essential for safe decision-making. In this work, we evaluate three recently proposed models that represent the state-of-the-art (Depth Anything, PackNet-SfM, and UnidDepth) using zero-shot testing on the DrivingStereo dataset across diverse weather conditions, and benchmark their performance. Our analysis considers not only metric depth accuracy metrcis but also each model’s ability to generalize under challenging environmental variations. While UniDepth achieves notable improvements over Depth Anything and PackNet-SfM, our results show that substantial progress is still needed for robust real-world deployment. To further assess its practical suitability for autonomous driving applications, we conduct a detailed examination of UniDepth’s strengths, limitations, and failure modes.

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