论文标题
校准自我监督的单眼深度估计
Calibrating Self-supervised Monocular Depth Estimation
论文作者
论文摘要
近年来，许多方法证明了神经网络学习深度和构成一系列图像的变化的能力，仅使用自主视力作为训练信号。尽管网络实现良好的性能，但经常被遮盖的细节是，由于单眼视觉的固有歧义，它们将深度预测到未知的缩放因素。然后，通常在测试时从LiDar地面真相获得缩放系数，这严重限制了这些方法的实际应用。在本文中，我们表明，结合了有关相机配置和环境的先前信息，我们可以删除比例歧义并直接预测深度，但仍使用自我监督的配方，而不依赖任何其他传感器。
In the recent years, many methods demonstrated the ability of neural networks to learn depth and pose changes in a sequence of images, using only self-supervision as the training signal. Whilst the networks achieve good performance, the often over-looked detail is that due to the inherent ambiguity of monocular vision they predict depth up to an unknown scaling factor. The scaling factor is then typically obtained from the LiDAR ground truth at test time, which severely limits practical applications of these methods. In this paper, we show that incorporating prior information about the camera configuration and the environment, we can remove the scale ambiguity and predict depth directly, still using the self-supervised formulation and not relying on any additional sensors.
