1 research outputs found
Multi-scale CNN stereo and pattern removal technique for underwater active stereo system
Demands on capturing dynamic scenes of underwater environments are rapidly
growing. Passive stereo is applicable to capture dynamic scenes, however the
shape with textureless surfaces or irregular reflections cannot be recovered by
the technique. In our system, we add a pattern projector to the stereo camera
pair so that artificial textures are augmented on the objects. To use the
system at underwater environments, several problems should be compensated,
i.e., refraction, disturbance by fluctuation and bubbles. Further, since
surface of the objects are interfered by the bubbles, projected patterns, etc.,
those noises and patterns should be removed from captured images to recover
original texture. To solve these problems, we propose three approaches; a
depth-dependent calibration, Convolutional Neural Network(CNN)-stereo method
and CNN-based texture recovery method. A depth-dependent calibration is our
analysis to find the acceptable depth range for approximation by center
projection to find the certain target depth for calibration. In terms of CNN
stereo, unlike common CNNbased stereo methods which do not consider strong
disturbances like refraction or bubbles, we designed a novel CNN architecture
for stereo matching using multi-scale information, which is intended to be
robust against such disturbances. Finally, we propose a multi-scale method for
bubble and a projected-pattern removal method using CNNs to recover original
textures. Experimental results are shown to prove the effectiveness of our
method compared with the state of the art techniques. Furthermore,
reconstruction of a live swimming fish is demonstrated to confirm the
feasibility of our techniques.Comment: International Conference on 3D Vision 201