TY - GEN
T1 - ROBUST CALIBRATION-MARKER AND LASER-LINE DETECTION FOR UNDERWATER 3D SHAPE RECONSTRUCTION BY DEEP NEURAL NETWORK
AU - Wang, Hanbin
AU - Iwaguchi, Takafumi
AU - Kawasaki, Hiroshi
N1 - Funding Information:
This work was supported by JSPS/KAKENHI JP20H00611, JP18H04119, JP21H01457 in Japan.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - There are various demands for underwater 3D reconstruction, however, since most active stereo 3D reconstruction methods focus on the air environment, it is difficult to directly apply them to underwater due to the several critical reasons, such as refraction, water flow and severe attenuation. Typically, calibration-markers or laser-lines are strongly blurred and saturated by attenuation, which makes difficult to recover shape in the water. Another problem is that it is difficult to keep cameras, projectors and objects static in the water because of strong water flow, which prevents accurate calibration. In this paper, we propose a method to solve those problems by novel algorithm using deep neural network (DNN), epipolar constraint and specially designed devices. We also built a real system and tested it in the water, e.g., pool and sea. Experimental results confirmed the effectiveness of the proposed method. We also demonstrated real 3D scan in the sea.
AB - There are various demands for underwater 3D reconstruction, however, since most active stereo 3D reconstruction methods focus on the air environment, it is difficult to directly apply them to underwater due to the several critical reasons, such as refraction, water flow and severe attenuation. Typically, calibration-markers or laser-lines are strongly blurred and saturated by attenuation, which makes difficult to recover shape in the water. Another problem is that it is difficult to keep cameras, projectors and objects static in the water because of strong water flow, which prevents accurate calibration. In this paper, we propose a method to solve those problems by novel algorithm using deep neural network (DNN), epipolar constraint and specially designed devices. We also built a real system and tested it in the water, e.g., pool and sea. Experimental results confirmed the effectiveness of the proposed method. We also demonstrated real 3D scan in the sea.
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U2 - 10.1109/ICIP46576.2022.9897733
DO - 10.1109/ICIP46576.2022.9897733
M3 - Conference contribution
AN - SCOPUS:85146688420
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 4243
EP - 4247
BT - 2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
PB - IEEE Computer Society
T2 - 29th IEEE International Conference on Image Processing, ICIP 2022
Y2 - 16 October 2022 through 19 October 2022
ER -