自主监督的单眼深度估计使用双路径编码器和偏移场插值
概括
这项研究介绍了DualDepth,一种新的自我监督单眼深度估计方法. 它使用双路径编码器和可学习的偏移插值来提高动态场景的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 自主监督学习显示多深度估计的承诺.
- 现有的方法在动态目标和静态摄像头上失败.
研究的目的:
- 为动态场景开发强大的自我监督单眼深度估计方法.
- 为了提高深度预测的准确性和效率.
主要方法:
- 提出了一种使用剩余和变压器块进行特征提取的双路径编码器.
- 实施了对比式学习策略,以分离单和多功能.
- 引入了一个可学习的偏移插值 (LOI) 模块,以实现高效,单步升级样本.
主要成果:
- 双深度方法在KITTI和Cityscapes基准指标上取得了竞争性表现.
- 与最先进的方法相比,证明了更好的准确性和效率.
- 在静态自我相机条件下成功处理动态目标.
结论:
- 拟议的双深度方法为自我监督的单眼深度估计提供了有效的解决方案.
- 双路径编码和LOI模块有助于准确和高效的深度预测.
- 这种方法在具有挑战性的动态场景中提升了深度估计的能力.
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