ROBUST CALIBRATION-MARKER AND LASER-LINE DETECTION FOR UNDERWATER 3D SHAPE RECONSTRUCTION BY DEEP NEURAL NETWORK

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.