Abstract
This paper addresses the problem of feature extraction for 3d point cloud data by using autoencoder. Deep learning is one of the most active fields of artificial intelligence, especially in a variety of visual applications, such as image classification and object recognition. However it has not been successfully applied on 3d point cloud data. In this paper, a new method of analyzing the point cloud data is proposed. The method aims to convert the point cloud data to a surface-condition-feature map, which is very effective and useful in pre-training by autoencoder. The surface-condition-features in this paper are defined as upward inclined, downward inclined, upward curved, downward curved, edge and flat, where those features are converted from surface normal vectors.
| Original language | English |
|---|---|
| Title of host publication | 2016 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 525-529 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781509023943 |
| DOIs | |
| Publication status | Published - Sep 1 2016 |
| Event | 13th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2016 - Harbin, Heilongjiang, China Duration: Aug 7 2016 → Aug 10 2016 |
Other
| Other | 13th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2016 |
|---|---|
| Country | China |
| City | Harbin, Heilongjiang |
| Period | 8/7/16 → 8/10/16 |
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ASJC Scopus subject areas
- Mechanical Engineering
- Artificial Intelligence
- Computer Science Applications
- Software
Cite this
Surface-common-feature descriptor of point cloud data for deep learning. / Maimaitimin, Maierdan; Watanabe, Keigo; Maeyama, Shoichi.
2016 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 525-529 7558618.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Surface-common-feature descriptor of point cloud data for deep learning
AU - Maimaitimin, Maierdan
AU - Watanabe, Keigo
AU - Maeyama, Shoichi
PY - 2016/9/1
Y1 - 2016/9/1
N2 - This paper addresses the problem of feature extraction for 3d point cloud data by using autoencoder. Deep learning is one of the most active fields of artificial intelligence, especially in a variety of visual applications, such as image classification and object recognition. However it has not been successfully applied on 3d point cloud data. In this paper, a new method of analyzing the point cloud data is proposed. The method aims to convert the point cloud data to a surface-condition-feature map, which is very effective and useful in pre-training by autoencoder. The surface-condition-features in this paper are defined as upward inclined, downward inclined, upward curved, downward curved, edge and flat, where those features are converted from surface normal vectors.
AB - This paper addresses the problem of feature extraction for 3d point cloud data by using autoencoder. Deep learning is one of the most active fields of artificial intelligence, especially in a variety of visual applications, such as image classification and object recognition. However it has not been successfully applied on 3d point cloud data. In this paper, a new method of analyzing the point cloud data is proposed. The method aims to convert the point cloud data to a surface-condition-feature map, which is very effective and useful in pre-training by autoencoder. The surface-condition-features in this paper are defined as upward inclined, downward inclined, upward curved, downward curved, edge and flat, where those features are converted from surface normal vectors.
UR - http://www.scopus.com/inward/record.url?scp=84991222063&partnerID=8YFLogxK
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U2 - 10.1109/ICMA.2016.7558618
DO - 10.1109/ICMA.2016.7558618
M3 - Conference contribution
SP - 525
EP - 529
BT - 2016 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -