Metal surface 3D imaging based on convolutional autoencoder and light plane calibration

Abstract

Convolutional autoencoders play an important role in computer vision and image processing. In this paper, an optical stripe extraction method based on convolutional autoencoder is proposed to reconstruct reflective metal surfaces. A six-layer convolutional autoencoder network is constructed to extract the center line of the light stripe. In addition, combined with the center line extraction algorithm and line-structured light calibration algorithm, a good reconstruction effect under reflective conditions is obtained. Experimental results show that the relative error of the 3D imaging method based on the convolutional autoencoder is decreased from 6.233% to 2.183% compared with the traditional line-structured laser 3D imaging method.