How Hard is Snow? A Paired Domain Adaptation Dataset for Clear and Snowy Weather: CADC+

Evaluating the impact of snowfall on 3D object detection requires a dataset with sufficient labelled data from both snowy and clear weather conditions, ideally captured in the same driving environment. Current datasets with LiDAR point clouds either do not provide enough labelled data in both domains, or rely on de-snowing methods to generate synthetic clear weather. Synthetic data often lacks realism and introduces an additional domain shift that confounds accurate evaluations. To address these challenges, we present CADC+, the first paired weather domain adaptation dataset for autonomous driving in winter conditions.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>The dataset can be downloaded at https://uwaterloo.ca/waterloo-intelligent-systems-engineering-lab/cadc-plus. CADC+ extends the Canadian Adverse Driving Conditions (CADC) dataset using clear weather data that was recorded on the same roads and in the same period as CADC. To create CADC+, we pair each CADC sequence with a clear weather sequence that matches the snowy sequence as closely as possible. CADC+ thus minimizes the domain shift resulting from factors unrelated to the presence of snow. We also present preliminary results using CADC+ to evaluate the effect of snow on 3D object detection performance. We observe that snow introduces a combination of aleatoric and epistemic uncertainties, acting as both noise and a distinct data domain.