A Multi-Modal Benchmark for Long-Range Depth Evaluation in Adverse Weather Conditions

Depth estimation is a cornerstone computer vision application that is critical for scene understanding and autonomous driving. In real-world scenarios, achieving reliable depth perception under adverse weather - e.g. in fog and rain - is crucial to ensure safety and system robustness. However, quantitatively evaluating the performances of depth estimation methods in these scenarios is challenging due to the difficulty of obtaining ground truth data. A promising approach is using weather chambers to simulate diverse weather conditions in a controlled environment. However, current datasets are limited in distance and lack a dense ground truth. To address this gap, we introduce a novel evaluation benchmark that extends depth evaluation up to 200 meters under clear, foggy, and rainy conditions. To this end, we employ a multimodal sensor setup, including state-of-the-art stereo RGB, RCCB, Gated camera systems, and a long-range LiDAR sensor. Moreover, we record a digital twin of the test facility sampled at a millimeter scale using a high-end geodesic laser scanner. This comprehensive benchmark allows for the evaluation of different models and multiple sensing modalities in a more precise and accurate manner, as well as at far distances. Data and code will be released upon publication.