Phase unwrapping based on deep learning in light field fringe projection 3D measurement
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Abstract:
Phase unwrapping is one of the key roles in fringe projection three-dimensional (3D) measurement technology. In this paper we propose a new method to phase unwrapping in camera array light filed camera fringe projection 3D measurement base on deep learning. A multi-stream convolutional neural network is proposed to learn the mapping relationship between camera array light filed wrapped phases and fringe orders of the expected central view, and is used to predict the fringe order to achieve the phase unwrapping. Experiments are performed on the light field fringe projection data generated by simulated camera array fringe projection measurement system in Blender and by experimental 3*3 camera array light field fringe projection system. The performance of the proposed network with light field wrapped phases using multi directions as network input data is studied, and the advantages of phase unwrapping based on deep learning in light filed fringe projection are demonstrated.
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Project Supported:
National Natural Science Foundation of China (No.61905178)；Natural Science Foundation of Tianjin(No. 18JCQNJC71100)
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