抄録
Many previous works have proposed methods for reconstructing a three-dimensional (3D) shape from a single image. Some of the methods reconstruct a 3D shape using machine learning. These methods learn the relationship between a 3D shape and a 2D image. However, they cannot learn the desirable features of 2D images for 3D reconstruction, because they use only predefined features. Therefore, this paper presents a method for reconstructing the 3D shape by learning features of a 2D image. This method reconstructs a 3D shape by using Convolutional Neural Network (CNN) for feature learning. The pooling layer and the convolutional layer of the CNN enable us to acquire spatial information of an image and automatically select the valuable feature of the image. From the experimental results using human face images, this method can reconstruct the 3D shape with better accuracy than the previous methods.
| 元の言語 | English |
|---|---|
| ホスト出版物のタイトル | 2018 International Workshop on Advanced Image Technology, IWAIT 2018 |
| 出版者 | Institute of Electrical and Electronics Engineers Inc. |
| ページ | 1-4 |
| ページ数 | 4 |
| ISBN（電子版） | 9781538626153 |
| DOI | |
| 出版物ステータス | Published - 2018 5 30 |
| イベント | 2018 International Workshop on Advanced Image Technology, IWAIT 2018 - Chiang Mai, Thailand 継続期間: 2018 1 7 → 2018 1 9 |
Other
| Other | 2018 International Workshop on Advanced Image Technology, IWAIT 2018 |
|---|---|
| 国 | Thailand |
| 市 | Chiang Mai |
| 期間 | 18/1/7 → 18/1/9 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Media Technology
これを引用
Three-dimensional shape reconstruction from a single image based on feature learning. / Sakai, Kentaro; Yasumura, Yoshiaki.
2018 International Workshop on Advanced Image Technology, IWAIT 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-4.研究成果: Conference contribution
}
TY - GEN
T1 - Three-dimensional shape reconstruction from a single image based on feature learning
AU - Sakai, Kentaro
AU - Yasumura, Yoshiaki
PY - 2018/5/30
Y1 - 2018/5/30
N2 - Many previous works have proposed methods for reconstructing a three-dimensional (3D) shape from a single image. Some of the methods reconstruct a 3D shape using machine learning. These methods learn the relationship between a 3D shape and a 2D image. However, they cannot learn the desirable features of 2D images for 3D reconstruction, because they use only predefined features. Therefore, this paper presents a method for reconstructing the 3D shape by learning features of a 2D image. This method reconstructs a 3D shape by using Convolutional Neural Network (CNN) for feature learning. The pooling layer and the convolutional layer of the CNN enable us to acquire spatial information of an image and automatically select the valuable feature of the image. From the experimental results using human face images, this method can reconstruct the 3D shape with better accuracy than the previous methods.
AB - Many previous works have proposed methods for reconstructing a three-dimensional (3D) shape from a single image. Some of the methods reconstruct a 3D shape using machine learning. These methods learn the relationship between a 3D shape and a 2D image. However, they cannot learn the desirable features of 2D images for 3D reconstruction, because they use only predefined features. Therefore, this paper presents a method for reconstructing the 3D shape by learning features of a 2D image. This method reconstructs a 3D shape by using Convolutional Neural Network (CNN) for feature learning. The pooling layer and the convolutional layer of the CNN enable us to acquire spatial information of an image and automatically select the valuable feature of the image. From the experimental results using human face images, this method can reconstruct the 3D shape with better accuracy than the previous methods.
KW - 3D shape reconstruction
KW - Convolutional Neural Network
KW - feature learning
UR - http://www.scopus.com/inward/record.url?scp=85048827705&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048827705&partnerID=8YFLogxK
U2 - 10.1109/IWAIT.2018.8369636
DO - 10.1109/IWAIT.2018.8369636
M3 - Conference contribution
AN - SCOPUS:85048827705
SP - 1
EP - 4
BT - 2018 International Workshop on Advanced Image Technology, IWAIT 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -