- Abstract: Learning based methods have shown very promising results for the task of depth estimation in single images.
- By exploiting epipolar geometry constraints, we generate disparity images by training our networks with an image reconstruction loss.
- Most existing approaches treat depth prediction as a supervised regression problem and as a result, require vast quantities of corresponding ground truth depth data for training.
- Our method produces state of the art results for monocular depth estimation on the KITTI driving dataset, even outperforming supervised methods that have been trained with ground truth depth.
- We propose a novel training objective that enables our convolutional neural network to learn to perform single image depth estimation, despite the absence of ground truth depth data.
Continue reading “[1609.03677v1] Unsupervised Monocular Depth Estimation with Left-Right Consistency”