一个合成的数字城市数据集,用于深度估计模型的稳定性和概括性
Jihao Li1, Jincheng Hu1, Yanjun Huang2
1Department of Aeronautical and Automotive Engineering, Loughborough University, Leicestershire, LE11 3TU, UK.
Scientific data
|March 17, 2024
概括
一个新的合成数字城市数据集 (SDCD) 提供了高分辨率图像和深度地图,用于训练自动驾驶模型. 在SDCD上训练的模型在各种不利条件下改善了远程深度估计.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 自主驾驶系统 自主驾驶系统
背景情况:
- 现有的单眼深度估计数据集缺乏图像数量和多样性.
- 当前的数据集通常具有低分辨率图像和稀疏的深度图,限制了模型性能.
- 当前数据集中的驾驶条件不能充分反映现实世界的复杂性.
研究的目的:
- 为单眼深度估计引入一个全面的合成数字城市数据集 (SDCD).
- 提高自动驾驶深度估计模型的准确性和稳定性.
- 为评估各种不利驾驶场景下的模型性能提供一个基准.
主要方法:
- 创建了一个大规模的数据集 (930K图像),具有高分辨率的RGB图像和完美的深度图.
- 模拟了6种不同的天气条件和6种不利的数据传输干扰.
- 使用拟议的SDCD训练和评估深度估计模型,并与KITTI等现有数据集进行比较.
主要成果:
- 与在KITTI上训练的模型相比,在SDCD上训练的模型表现出更高的远程深度估计准确度,清晰度和流性.
- 建立了一个基准来分析不同不利驾驶条件下的模型性能.
- 合成方法使得在严峻的条件下能够生成数据,并且具有完美的基本真实性.
结论:
- 合成数字城市数据集 (SDCD) 有效地解决了单眼深度估计现有数据集的局限性.
- SDCD显著提高了自动驾驶系统的深度估计能力,特别是在具有挑战性的环境中.
- 生成的数据集为推进强大的自主感知研究提供了宝贵的资源.
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