843 research outputs found
Unsupervised Monocular Depth Estimation with Left-Right Consistency
Learning based methods have shown very promising results for the task of
depth estimation in single images. However, 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. Just
recording quality depth data in a range of environments is a challenging
problem. In this paper, we innovate beyond existing approaches, replacing the
use of explicit depth data during training with easier-to-obtain binocular
stereo footage.
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. Exploiting epipolar geometry constraints, we
generate disparity images by training our network with an image reconstruction
loss. We show that solving for image reconstruction alone results in poor
quality depth images. To overcome this problem, we propose a novel training
loss that enforces consistency between the disparities produced relative to
both the left and right images, leading to improved performance and robustness
compared to existing approaches. 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.Comment: CVPR 2017 ora
- …