首个面向雨天乡村场景的点云全景分割数据集，填补了自动驾驶在复杂环境下的数据空白。
Panoptic-CUDAL: Rural Australia Point Cloud Dataset in Rainy Conditions
- 构建雨天乡村环境的高精度点云、相机与位姿同步数据集
- 包含超过12小时采集数据，覆盖多种雨势与光照条件
- 适用于自动驾驶感知算法在恶劣天气下的测试与验证
现有自动驾驶数据集主要聚焦于结构化的城市环境和理想天气，对农村场景及恶劣天气关注不足。尽管部分数据集包含天气与光照变化，但恶劣天气样本稀少。降雨会显著影响传感器性能，导致激光雷达和摄像头数据出现噪声与反射，降低系统对环境的可靠感知能力。本文提出Panoptic-CUDAL数据集，专为雨天乡村环境下的全景分割任务设计。通过同步采集高分辨率激光雷达、相机与位姿数据，该数据集提供了丰富且多样化的挑战性场景数据。我们对采集数据进行了分析，并为激光点云上的全景分割、语义分割与3D占据预测方法提供了基线结果。数据集可通过https://robotics.sydney.edu.au/our-research/intelligent-transportation-systems 及 https://vision.rwth-aachen.de/panoptic-cudal 获取。
原文摘要 · Abstract (English)
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
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