mlsanityData to 14 Oct 2026

Panoptic-CUDAL: Rural Australia Point Cloud Dataset in Rainy Conditions

arXiv 2503.16378 ↗First posted 20 Mar 2025cs.CVcited by 1

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Abstract (arXiv)

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

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 30 of its 46 references that are in the map. First posted on arXiv 20 Mar 2025.

Builds on

30 of 46 references.

References · 30

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18 more
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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Drivingcited by 10