Distilled Visual and Robot Kinematics Embeddings for Metric Depth Estimation in Monocular Scene Reconstruction-Reference-Cited by-同舟云学术

Distilled Visual and Robot Kinematics Embeddings for Metric Depth Estimation in Monocular Scene Reconstruction

Published:2022-10-23 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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Author:

Wei Ruofeng¹,Li Bin²,Mo Hangjie¹,Zhong Fangxun²,Long Yonghao³,Dou Qi³,Liu Yun-Hui²,Sun Dong¹

Affiliation:

1. City University of Hong Kong,Department of Biomedical Engineering,Hong Kong,China

2. Chinese University of Hong Kong,Department of Mechanical and Automation Engineering,Hong Kong,China

3. T Stone Robotics Institute, The Chinese University of Hong Kong,Department of Computer Science and Engineering,Hong Kong,China

Funder

City University of Hong Kong

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9981026/9981028/09982145.pdf?arnumber=9982145

Reference24 articles.

1. Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery

2. Stereo dense scene reconstruction and accurate laparoscope localization for learning-based navigation in robot-assisted surgery;wei;ArXiv Preprint,2021

3. Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery

4. Hierarchical Deep Stereo Matching on High-Resolution Images

5. Depth distillation: unsupervised metric depth estimation for UAVs by finding consensus between kinematics, optical flow and deep learning

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