.png){kind=icon}
.png){kind=icon}
Return
Semi-Supervised Monocular Depth Estimation Based on Semantic Supervision
.png){kind=icon}
PRE
AI
DOI：10.1007/s10846-020-01205-0.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Monocular depth estimation by unsupervised learning is a potential strategy, which is mainly self-supervised by calculating view reconstruction loss from stereo pairs or monocular sequences. However, most existing works only consider the geometric information during training, without using semantics. We propose a semantic monocular depth estimation (SE-Net), a neural network framework that estimates depth using semantic information and video sequences. The whole framework is semi-supervised, because we take advantage of labelled semantic ground truth data. In view of the structural consistency between the semantically segmented image and the depth map, we first perform semantic segmentation on the image, and then use the semantic labels to guide the construction of the depth estimation network. Experiments on the KITTI dataset show that learning semantic information from images can effectively improve the effect of monocular depth estimation, and SE-Net is superior to the most advanced methods in depth estimation accuracy.
Keywords:
Monocular depth estimation
Semantic segmentation
Convolutional neural network
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
2.8
Papers:
3.8K
Citations:
6.9K