Tao Zhang1,*, Jinxing Niu1, Shuo Liu1, Taotao Pan1, Brij B. Gupta2,3
Computer Systems Science and Engineering, Vol.36, No.1, pp. 271-280, 2021, DOI:10.32604/csse.2021.014181
- 23 December 2020
Abstract Three-dimensional (3D) reconstruction using structured light projection has the characteristics of non-contact, high precision, easy operation, and strong real-time performance. However, for actual measurement, projection modulated images are disturbed by electronic noise or other interference, which reduces the precision of the measurement system. To solve this problem, a 3D measurement algorithm of structured light based on deep learning is proposed. The end-to-end multi-convolution neural network model is designed to separately extract the coarse- and fine-layer features of a 3D image. The point-cloud model is obtained by nonlinear regression. The weighting coefficient loss function is introduced More >