Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to severe snow.
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The Winter Adverse Driving dataSet (WADS) - sequence 37.
Retrieved from: https://digitalcommons.mtu.edu/all-datasets/26
This data supports the paper "DSOR: A Scalable Statistical Filter for Removing Falling Snow from LiDAR Point Clouds in Severe Winter Weather" submitted to the IEEE. The preprint is available here: https://arxiv.org/abs/2109.07078
This dataset is part of the larger Winter Adverse Driving Dataset, which can be accessed here: https://digitalcommons.mtu.edu/wads/