Transformer-Based Self-Supervised Monocular Depth and Visual Odometry
Author:
Affiliation:
1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
2. 5G/6G Innovation Centre, University of Surrey, Guildford, U.K
Funder
National Key Research and Development Program of China
Funds for International Cooperation and Exchange of NSFC
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/10017147/09979761.pdf?arnumber=9979761
Reference42 articles.
1. Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera Intrinsics
2. Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
3. Make3D: Learning 3D Scene Structure from a Single Still Image
4. Depth map prediction from a single image using a multi-scale deep network;eigen;Proc Adv Neural Inf Process Syst,2014
5. Wasserstein generative adversarial networks;arjovsky;Proc Int Conf Mach Learn,2017
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