SAM-Net: Semantic probabilistic and attention mechanisms of dynamic objects for self-supervised depth and camera pose estimation in visual odometry applications-Reference-Cited by-同舟云学术

SAM-Net: Semantic probabilistic and attention mechanisms of dynamic objects for self-supervised depth and camera pose estimation in visual odometry applications

Published:2022-01 Issue: Volume:153 Page:126-135
ISSN:0167-8655
Container-title:Pattern Recognition Letters
language:en
Short-container-title:Pattern Recognition Letters

Author:

Yang Binchao,Xu Xinying,Ren Jinchang,Cheng Lan,Guo Lei,Zhang Zhe

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference61 articles.

1. Anytime stereo image depth estimation on mobile devices;Wang,2019

2. Self-supervised deep pose corrections for robust visual odometry;Wagstaff,2020

3. D3VO: deep depth, deep pose and deep uncertainty for monocular visual odometry;Yang;Comput. Vis. Pattern Recognit.,2020

4. DeepVO: towards end-to-end visual odometry with deep recurrent convolutional neural networks;Wang,2017

5. GeoNet: unsupervised learning of dense depth, optical flow and camera pose;Yin;Comput. Vis. Pattern Recognit.,2018

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