SC-DepthV3:对于动态场景的强大的自我监督单眼深度估计
IEEE transactions on pattern analysis and machine intelligence
|October 6, 2023
概括
SC-DepthV3增强了动态场景的自我监督单眼深度估计. 它使用伪深度的先前和新浪损失来产生清晰,准确的深度图,克服了以前方法的局限性.
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:
- 自主监督的单眼深度估计在静态场景中表现出色,但在动态物体和遮方面却很难.
- 现有的方法在动态场景中失败,在对象边界产生模糊的深度地图,这是由于训练视图中的封闭.
研究的目的:
- 解决动态场景中当前自我监督单眼深度估计方法的局限性.
- 提出一种新的方法,SC-DepthV3,用于在充满挑战的动态环境中准确地预测深度图.
主要方法:
- 引入了一个外部预训练的单眼深度估计模型,以生成一个单图像深度预 (伪深度).
- 开发了新的损失功能,以利用伪深度预先来促进自我监督的培训.
- 训练有素的网络使用单眼视频的高度动态的场景.
主要成果:
- SC-DepthV3可以预测清晰而准确的深度图,即使是在高度动态的场景中.
- 该方法在六个具有挑战性的数据集上显著优于以前的方法.
- 详细的废弃性研究验证了拟议成分的有效性.
结论:
- 在单眼深度估计中,SC-DepthV3有效地克服了动态物体和遮蔽所带来的挑战.
- 拟议的伪深度先前和新损失可以在复杂的现实场景中进行强大而准确的深度预测.
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