基于视觉变压器的自动驾驶场景的全景深度估计框架
Yuqi Zhang1,2, Liang Chu1,2, Zixu Wang1,2
1College of Automotive Engineering, Jilin University, Changchun 130022, China.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
这项研究引入了一个新的框架,用于在自动驾驶中准确地估计全景深度. ViT-Fuse方法提高了距离的准确性,并提供了更流的深度图,提高了安全性.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 自主系统 自主系统
背景情况:
- 准确的深度感知对于自动驾驶安全至关重要.
- 全景图像对深度估计具有独特的挑战,原因是扭曲和不完整的数据.
研究的目的:
- 开发一个创新的框架,用于自动驾驶的全景深度估计.
- 为了应对不完美的观测和投影融合的挑战.
- 在驾驶场景中提高距离准确性和稳定性.
主要方法:
- 提出了一种补丁填充方法,用稀疏的3D点云数据构建全景深度图.
- 介绍了ViT-Fuse,这是一种新的图像上下文学习方法,适用于等直角全景.
- 解决了户外全景图像中的扭曲问题.
主要成果:
- 与基线方法相比,ViT-Fuse方法在驾驶场景中平均减少了9.15%的估计误差.
- 实现了更强大,更流的深度估计图,特别是在边缘细节上.
- 在捕捉与周围图像的距离方面表现出更高的准确性.
结论:
- 拟议的框架大大提高了对自动驾驶的全景深度估计.
- 在挑战性的户外全景成像中,ViT-Fuse为深度估计提供了强大的解决方案.
- 这些进步有助于提高自动驾驶汽车的风险感知和安全.
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