照片现实的交通场景数据集:基于视觉分析的真实和合成视角
Khulan Khalzaa1, Stephen Karungaru1, Kenji Terada1
1B1 Research Laboratory, Department of Computer Science, Tokushima University, Tokushima 770-8502, Japan.
Data in brief
|September 2, 2025
概括
这项研究引入了各种交通场景数据集,用于自动驾驶的感知. 合成和现实世界的数据,包括多个高度和深度视图,增强计算机视觉模型训练.
科学领域:
- 计算机视觉
- 自动驾驶系统
- 机器学习
背景情况:
- 对于自动驾驶和从单眼相机解读交通场景至关重要.
- 现有的数据集可能缺乏多样性或现实世界的对应物.
研究的目的:
- 提供全面的交通场景数据集.
- 支持自动驾驶计算机视觉模型的开发和培训.
主要方法:
- 将数据集组织成四个组:交通场景,顶部视图,多高度视图和深度.
- 引入了新的数据集:支持者Real,支持者虚拟,RealTop,SynthTop.
- 使用RealSense立体相机收集同步的RGB和深度图像.
主要成果:
- 感知相似性分析 (LPIPS指标) 证实合成图像与现实场景非常相似.
- 数据集设计用于零射击单眼深度模型支持.
- 数据收集和生成发生在2023-2025年之间.
结论:
- 这些数据集促进了合成到真实领域的转移学习.
- 标准化格式确保与深度学习框架的兼容性.
- 在计算机视觉应用中促进更广泛的重复使用.
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