Panoptic-CUDAL: Rural Australia Point Cloud Dataset in Rainy Conditions
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Tseng, Tzu-Yun Nekrasov, Alexey Burdorf, Malcolm Leibe, Bastian Berrio, Julie Stephany Shan, Mao Ming, Zhenxing Worrall, Stewart |
| author_facet | Tseng, Tzu-Yun Nekrasov, Alexey Burdorf, Malcolm Leibe, Bastian Berrio, Julie Stephany Shan, Mao Ming, Zhenxing Worrall, Stewart |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16378 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Panoptic-CUDAL: Rural Australia Point Cloud Dataset in Rainy Conditions Tseng, Tzu-Yun Nekrasov, Alexey Burdorf, Malcolm Leibe, Bastian Berrio, Julie Stephany Shan, Mao Ming, Zhenxing Worrall, Stewart Computer Vision and Pattern Recognition 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 |
| title | Panoptic-CUDAL: Rural Australia Point Cloud Dataset in Rainy Conditions |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.16378 |