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Demands on capturing dynamic scenes of underwater environments are rapidlygrowing. Passive stereo is applicable to capture dynamic scenes, however theshape with textureless surfaces or irregular reflections cannot be recovered bythe technique. In our system, we add a pattern projector to the stereo camerapair so that artificial textures are augmented on the objects. To use thesystem at underwater environments, several problems should be compensated,i.e., refraction, disturbance by fluctuation and bubbles. Further, sincesurface of the objects are interfered by the bubbles, projected patterns, etc.,those noises and patterns should be removed from captured images to recoveroriginal texture. To solve these problems, we propose three approaches; adepth-dependent calibration, Convolutional Neural Network(CNN)-stereo methodand CNN-based texture recovery method. A depth-dependent calibration is ouranalysis to find the acceptable depth range for approximation by centerprojection to find the certain target depth for calibration. In terms of CNNstereo, unlike common CNNbased stereo methods which do not consider strongdisturbances like refraction or bubbles, we designed a novel CNN architecturefor stereo matching using multi-scale information, which is intended to berobust against such disturbances. Finally, we propose a multi-scale method forbubble and a projected-pattern removal method using CNNs to recover originaltextures. Experimental results are shown to prove the effectiveness of ourmethod compared with the state of the art techniques. Furthermore,reconstruction of a live swimming fish is demonstrated to confirm thefeasibility of our techniques.

Multi-scale CNN stereo and pattern removal technique for underwater active stereo system