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Phase unwrapping based on deep learning in light field fringe projection 3D measurement
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作者 ZHU Xinjun ZHAO Haichuan +3 位作者 YUAN Mengkai ZHANG Zhizhi WANG Hongyi SONG Limei 《Optoelectronics Letters》 EI 2023年第9期556-562,共7页
Phase unwrapping is one of the key roles in fringe projection three-dimensional(3D)measurement technology.We propose a new method to achieve phase unwrapping in camera array light filed fringe projection 3D measuremen... Phase unwrapping is one of the key roles in fringe projection three-dimensional(3D)measurement technology.We propose a new method to achieve phase unwrapping in camera array light filed fringe projection 3D measurement based on deep learning.A multi-stream convolutional neural network(CNN)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 the simulated camera array fringe projection measurement system in Blender and by the experimental 3×3 camera array light field fringe projection system.The performance of the proposed network with light field wrapped phases using multiple directions as network input data is studied,and the advantages of phase unwrapping based on deep learning in light filed fringe projection are demonstrated. 展开更多
关键词 Phase unwrapping based on deep learning in light field fringe projection 3D measurement
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