Surface-common-feature descriptor of point cloud data for deep learning

Maierdan Maimaitimin; Keigo Watanabe; Shoichi Maeyama

doi:10.1109/ICMA.2016.7558618

Surface-common-feature descriptor of point cloud data for deep learning

Maierdan Maimaitimin, Keigo Watanabe, Shoichi Maeyama

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

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	https://doi.org/10.1109/ICMA.2016.7558618
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

ASJC Scopus subject areas

Mechanical Engineering
Artificial Intelligence
Computer Science Applications
Software

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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

Maimaitimin, M, Watanabe, K & Maeyama, S 2016, Surface-common-feature descriptor of point cloud data for deep learning. in 2016 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2016., 7558618, Institute of Electrical and Electronics Engineers Inc., pp. 525-529, 13th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2016, Harbin, Heilongjiang, China, 8/7/16. https://doi.org/10.1109/ICMA.2016.7558618

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