Panoptic-CUDAL: Rural Australia Point Cloud Dataset in Rainy Conditions
This addresses the problem of limited data for developing robust perception systems in adverse weather for autonomous vehicles in rural settings, though it is incremental as it primarily offers a new dataset.
The paper tackles the lack of datasets for autonomous driving in rural areas under rainy conditions by introducing the Panoptic-CUDAL dataset, which includes high-resolution LiDAR, camera, and pose data, and provides baseline results for panoptic segmentation, semantic segmentation, and 3D occupancy prediction.
Existing autonomous driving datasets are predominantly oriented towards well-structured urban settings and favourable weather conditions, leaving the complexities of rural environments and adverse weather conditions largely unaddressed. Although some datasets encompass variations in weather and lighting, bad weather scenarios do not appear often. Rainfall can significantly impair sensor functionality, introducing noise and reflections in LiDAR and camera data and reducing the system's capabilities for reliable environmental perception and safe navigation. This paper introduces the Panoptic-CUDAL dataset, a novel dataset purpose-built for panoptic segmentation in rural areas subject to rain. By recording high-resolution LiDAR, camera, and pose data, Panoptic-CUDAL offers a diverse, information-rich dataset in a challenging scenario. We present the analysis of the recorded data and provide baseline results for panoptic, semantic segmentation, and 3D occupancy prediction methods on LiDAR point clouds. The dataset can be found here: https://robotics.sydney.edu.au/our-research/intelligent-transportation-systems, https://vision.rwth-aachen.de/panoptic-cudal