论文标题
语义驱动的无监督学习，以进行单眼深度和自我运动估计
Semantics-Driven Unsupervised Learning for Monocular Depth and Ego-Motion Estimation
论文作者
论文摘要
我们提出了一种以语义为导向的无监督的学习方法，以对本文的视频进行单眼深度和自我运动估计。最近的无监督学习方法在合成视图和实际图像之间采用光度误差作为训练的监督信号。在我们的方法中，我们利用语义分割信息来减轻场景中动态对象和遮挡的影响，并通过考虑深度与语义之间的相关性来改善深度预测性能。为了避免昂贵的标记过程，我们使用预先训练的语义分割网络获得的嘈杂的语义分割结果。此外，我们将相邻帧的相应点之间的位置误差最小化，以利用3D空间信息。 KITTI数据集的实验结果表明，我们的方法在深度和自我运动估计任务中都能达到良好的性能。
We propose a semantics-driven unsupervised learning approach for monocular depth and ego-motion estimation from videos in this paper. Recent unsupervised learning methods employ photometric errors between synthetic view and actual image as a supervision signal for training. In our method, we exploit semantic segmentation information to mitigate the effects of dynamic objects and occlusions in the scene, and to improve depth prediction performance by considering the correlation between depth and semantics. To avoid costly labeling process, we use noisy semantic segmentation results obtained by a pre-trained semantic segmentation network. In addition, we minimize the position error between the corresponding points of adjacent frames to utilize 3D spatial information. Experimental results on the KITTI dataset show that our method achieves good performance in both depth and ego-motion estimation tasks.
