Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering

Generating Orientated Point Clouds From RGB-D Sensor

Authors
W.B. Shan, Y. Yao, J.J. Liu, M. Guo
Corresponding Author
W.B. Shan
Available Online July 2015.
DOI
10.2991/eame-15.2015.110How to use a DOI?
Keywords
RGB-D sensor; point clouds; normal estimation; 3D reconstruction
Abstract

In order to reconstruct a surface by using the data from RGB-D sensor, this paper presents a viewpoint based method for estimating the orientation of point clouds. Firstly, a PCA method is used to compute the normal vector of each point. In this process, the orientation of the normal is determined by the viewpoint. Secondly, the point clouds generated from each frame are combined to an overall model with directional information. Finally, the quality of the model is further improved by a local smooth method. The experimental results show that the method can compute accurate normal vectors on the fly. Compared to current methods, it can estimate the sign of the normal automatically.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering
Series
Advances in Engineering Research
Publication Date
July 2015
ISBN
978-94-62520-71-4
ISSN
2352-5401
DOI
10.2991/eame-15.2015.110How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - W.B. Shan
AU  - Y. Yao
AU  - J.J. Liu
AU  - M. Guo
PY  - 2015/07
DA  - 2015/07
TI  - Generating Orientated Point Clouds From RGB-D Sensor
BT  - Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering
PB  - Atlantis Press
SP  - 391
EP  - 393
SN  - 2352-5401
UR  - https://doi.org/10.2991/eame-15.2015.110
DO  - 10.2991/eame-15.2015.110
ID  - Shan2015/07
ER  -