Abstract
3D reconstruction methods based on active stereo technique have been widely used for many practical systems. Many of these systems are configured with a single camera and a single projector. Since such systems can only capture one side of the target object, several attempts have been conducted to enlarge the captured area, especially multi-projector systems attract many researchers. For multi-projector based systems, overlap between multiple pattern projections is a serious problem. Even if different color channels are used for each projector, complete separation is not possible because of color crosstalks. Another open problem is decoding errors of the projected patterns, which causes a failure on extracting positional information of the projected pattern form the captured image. Among several reasons for such errors, color crosstalks are crucial because their features are similar to the main signal and difficult to be decomposed. In this paper, we solve these problems by utilizing machine learning techniques where a convolutional neural network is trained to extract low dimensional pattern features for each projector. In addition, it is trained to suppress the color crosstalks from different projectors. Using this new technique, we succeeded in reconstructing 3D shapes from images where multiple patterns are overlapped.
| Original language | English |
|---|---|
| Title of host publication | ICRA 2017 - IEEE International Conference on Robotics and Automation |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 5074-5080 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781509046331 |
| DOIs | |
| Publication status | Published - Jul 21 2017 |
| Externally published | Yes |
| Event | 2017 IEEE International Conference on Robotics and Automation, ICRA 2017 - Singapore, Singapore Duration: May 29 2017 → Jun 3 2017 |
Publication series
| Name | Proceedings - IEEE International Conference on Robotics and Automation |
|---|---|
| ISSN (Print) | 1050-4729 |
Other
| Other | 2017 IEEE International Conference on Robotics and Automation, ICRA 2017 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 5/29/17 → 6/3/17 |
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All Science Journal Classification (ASJC) codes
- Software
- Control and Systems Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering
Cite this
Learning-based feature extraction for active 3D scan with reducing color crosstalk of multiple pattern projections. / Sagawa, Ryusuke; Furukawa, Ryo; Matsumoto, Akiko; Kawasaki, Hiroshi.
ICRA 2017 - IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers Inc., 2017. p. 5074-5080 7989592 (Proceedings - IEEE International Conference on Robotics and Automation).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Learning-based feature extraction for active 3D scan with reducing color crosstalk of multiple pattern projections
AU - Sagawa, Ryusuke
AU - Furukawa, Ryo
AU - Matsumoto, Akiko
AU - Kawasaki, Hiroshi
PY - 2017/7/21
Y1 - 2017/7/21
N2 - 3D reconstruction methods based on active stereo technique have been widely used for many practical systems. Many of these systems are configured with a single camera and a single projector. Since such systems can only capture one side of the target object, several attempts have been conducted to enlarge the captured area, especially multi-projector systems attract many researchers. For multi-projector based systems, overlap between multiple pattern projections is a serious problem. Even if different color channels are used for each projector, complete separation is not possible because of color crosstalks. Another open problem is decoding errors of the projected patterns, which causes a failure on extracting positional information of the projected pattern form the captured image. Among several reasons for such errors, color crosstalks are crucial because their features are similar to the main signal and difficult to be decomposed. In this paper, we solve these problems by utilizing machine learning techniques where a convolutional neural network is trained to extract low dimensional pattern features for each projector. In addition, it is trained to suppress the color crosstalks from different projectors. Using this new technique, we succeeded in reconstructing 3D shapes from images where multiple patterns are overlapped.
AB - 3D reconstruction methods based on active stereo technique have been widely used for many practical systems. Many of these systems are configured with a single camera and a single projector. Since such systems can only capture one side of the target object, several attempts have been conducted to enlarge the captured area, especially multi-projector systems attract many researchers. For multi-projector based systems, overlap between multiple pattern projections is a serious problem. Even if different color channels are used for each projector, complete separation is not possible because of color crosstalks. Another open problem is decoding errors of the projected patterns, which causes a failure on extracting positional information of the projected pattern form the captured image. Among several reasons for such errors, color crosstalks are crucial because their features are similar to the main signal and difficult to be decomposed. In this paper, we solve these problems by utilizing machine learning techniques where a convolutional neural network is trained to extract low dimensional pattern features for each projector. In addition, it is trained to suppress the color crosstalks from different projectors. Using this new technique, we succeeded in reconstructing 3D shapes from images where multiple patterns are overlapped.
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U2 - 10.1109/ICRA.2017.7989592
DO - 10.1109/ICRA.2017.7989592
M3 - Conference contribution
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 5074
EP - 5080
BT - ICRA 2017 - IEEE International Conference on Robotics and Automation
PB - Institute of Electrical and Electronics Engineers Inc.
ER -