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The fusion of multimodal sensor streams, such as camera, lidar, and radarmeasurements, plays a critical role in object detection for autonomousvehicles, which base their decision making on these inputs. While existingmethods exploit redundant information in good environmental conditions, theyfail in adverse weather where the sensory streams can be asymmetricallydistorted. These rare "edge-case" scenarios are not represented in availabledatasets, and existing fusion architectures are not designed to handle them. Toaddress this challenge we present a novel multimodal dataset acquired in over10,000km of driving in northern Europe. Although this dataset is the firstlarge multimodal dataset in adverse weather, with 100k labels for lidar,camera, radar, and gated NIR sensors, it does not facilitate training asextreme weather is rare. To this end, we present a deep fusion network forrobust fusion without a large corpus of labeled training data covering allasymmetric distortions. Departing from proposal-level fusion, we propose asingle-shot model that adaptively fuses features, driven by measuremententropy. We validate the proposed method, trained on clean data, on ourextensive validation dataset. Code and data are available herehttps://github.com/princeton-computational-imaging/SeeingThroughFog.

Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather