自动驾驶数据集

SemanticKITTI-C是用于自动驾驶空间理解的3D语义分割数据集，旨在评估激光雷达语义分割模型在复杂现实环境中的稳健性。数据集包含近一百个点云场景，覆盖16种域外损坏情况，分为恶劣天气、测量噪声和跨设备差异三组。其特点在于提供360度全景扫描，构建全面的稳健性基准。该数据集适用于自动驾驶汽车在复杂条件下感知能力的训练与评估。

SemanticKITTI-C is a 3D semantic segmentation dataset for spatial understanding in autonomous driving, designed to evaluate the robustness of LiDAR semantic segmentation models in complex real-world environments. It contains nearly one hundred point cloud scenes, covering 16 types of out-of-distribution corruptions, which are grouped into three categories: adverse weather, measurement noise, and cross-device variations. Featuring 360-degree panoramic scans, it establishes a comprehensive robustness benchmark. This dataset is suitable for training and evaluating the perception capabilities of autonomous vehicles under complex conditions.

搜集汇总

背景与挑战

背景概述

该数据集是SemanticKITTI-C，一个专注于自动驾驶空间理解的3D语义分割数据集，旨在评估激光雷达模型在复杂现实环境中的稳健性。其特点在于包含近一百个点云场景，覆盖16种域外损坏情况，分为恶劣天气、测量噪声和跨设备差异三组，总容量为113.57GB，适用于自动驾驶领域的模型微调和评测。