Dense Depth Completion Based on Multi-Scale Confidence and Self-Attention Mechanism for Intestinal Endoscopy-Reference-Cited by-同舟云学术

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Dense Depth Completion Based on Multi-Scale Confidence and Self-Attention Mechanism for Intestinal Endoscopy

Published:2023-05-29 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Robotics and Automation (ICRA)
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Author:

Liu Ruyu¹,Liu Zhengzhe¹,Zhang Haoyu¹,Zhang Guodao²,Zuo Zhigui³,Sheng Weiguo¹

Affiliation:

1. School of Information Science and Technology, Hangzhou Normal University,Hangzhou,China,311121

2. Hangzhou Dianzi University,Department of Digital Media Technology,Hangzhou,China,310018

3. The First Affiliated Hospital of Wenzhou Medical University,Department of Colorectal Surgery,Wenzhou,China,325035

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10160211/10160212/10161549.pdf?arnumber=10161549

Reference38 articles.

1. Panoramic depth estimation via supervised and unsupervised learning in indoor scenes

2. VR-Caps: A Virtual Environment for Capsule Endoscopy

3. PENet: Towards Precise and Efficient Image Guided Depth Completion

4. Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimation

5. EndoSLAM dataset and an unsupervised monocular visual odometry and depth estimation approach for endoscopic videos

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1. Panorama-LiDAR Fusion for Dense Omnidirectional Depth Completion in 3D Street Scene;IEEE Transactions on Consumer Electronics;2024-05

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