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Dense 3D shape acquisition of swimming human or live fish is an importantresearch topic for sports, biological science and so on. For this purpose,active stereo sensor is usually used in the air, however it cannot be appliedto the underwater environment because of refraction, strong light attenuationand severe interference of bubbles. Passive stereo is a simple solution forcapturing dynamic scenes at underwater environment, however the shape withtextureless surfaces or irregular reflections cannot be recovered. Recently,the stereo camera pair with a pattern projector for adding artificial textureson the objects is proposed. However, to use the system for underwaterenvironment, several problems should be compensated, i.e., disturbance byfluctuation and bubbles. Simple solution is to use convolutional neural networkfor stereo to cancel the effects of bubbles and/or water fluctuation. Since itis not easy to train CNN with small size of database with large variation, wedevelop a special bubble generation device to efficiently create real bubbledatabase of multiple size and density. In addition, we propose a transferlearning technique for multi-scale CNN to effectively remove bubbles andprojected-patterns on the object. Further, we develop a real system andactually captured live swimming human, which has not been done before.Experiments are conducted to show the effectiveness of our method compared withthe state of the art techniques.

CNN based dense underwater 3D scene reconstruction by transfer learning using bubble database