We propose a monocular depth estimator SC-Depth, which requires only
unlabelled videos for training and enables the scale-consistent prediction at
inference time. Our contributions include: (i) we propose a geometry
consistency loss, which penalizes the inconsistency of predicted depths between
adjacent views; (ii) we propose a self-discovered mask to automatically
localize moving objects that violate the underlying static scene assumption and
cause noisy signals during training; (iii) we demonstrate the efficacy of each
component with a detailed ablation study and show high-quality depth estimation
results in both KITTI and NYUv2 datasets. Moreover, thanks to the capability of
scale-consistent prediction, we show that our monocular-trained deep networks
are readily integrated into the ORB-SLAM2 system for more robust and accurate
tracking. The proposed hybrid Pseudo-RGBD SLAM shows compelling results in
KITTI, and it generalizes well to the KAIST dataset without additional
training. Finally, we provide several demos for qualitative evaluation.

本文提出了一种单目深度估计器 SC-Depth，它只需要使用未标记的视频进行训练，并能在推理时实现尺度一致的预测。我们的贡献包括：(i) 我们提出了一种几何一致性损失，惩罚相邻视角之间预测深度的不一致性；(ii) 我们提出了自动定位违反基本静态场景假设并在训练过程中引起噪声信号的移动对象的自我发现掩码；(iii) 我们通过详细的消融研究证明了每个组件的功效，并展示了在 KITTI 和 NYUv2 数据集中高质量的深度估计结果。此外，由于具有尺度一致性预测的能力，我们展示了我们训练的单目深层网络可以轻松地集成到 ORB-SLAM2 系统中以实现更强大和准确的跟踪。提出的混合 Pseudo-RGBD SLAM 在 KITTI 中具有很强的性能，并且在没有额外训练的情况下很好地推广到 KAIST 数据集。最后，我们提供了几个演示以进行定性评估。