D2NT: A High-Performing Depth-to-Normal Translator-Reference-Cited by-同舟云学术

D2NT: A High-Performing Depth-to-Normal Translator

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

Feng Yi¹,Xue Bohuan²,Liu Ming³,Chen Qijun¹,Fan Rui¹

Affiliation:

1. the College of Electronic & Information Engineering, Tongji University,Robotics & Artificial Intelligence Laboratory (RAIL), the State Key Laboratory of Intelligent Autonomous Systems, and Frontiers Science Center for Intelligent Autonomous Systems,Shanghai,P. R. China,201804

2. the Hong Kong University of Science and Technology (Guangzhou),Department of Computer Science & Engineering,Hong Kong SAR,P. R. China

3. the Hong Kong University of Science and Technol-ogy (Guangzhou),Robotics & Autonomous Systems Thrust of the Systems Hub,Nansha,Guangzhou,P. R. China,511400

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Science and Technology Commission of Shanghai Municipal

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10160211/10160212/10161000.pdf?arnumber=10161000

Reference21 articles.

1. Realtime segmentation of range data using continuous nearest neighbors

2. Comparison of local plane fitting methods for range data;wang;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2001

3. SDA-SNE: Spatial Discontinuity-Aware Surface Normal Estimation via Multi-Directional Dynamic Programming

4. A quantitative evaluation of surface normal estimation in point clouds

5. DIODE: A Dense Indoor and Outdoor DEpth dataset;vasiljevic;CoRR,2019

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