Fig. 1. Procedure of neural network 3D measurement

Fig. 2. Convolutional neural network structure

Fig. 3. Part data in the dataset

Fig. 4. Loss curves of the training set and validation set. (a) Loss curve of the training set; (b) loss curve of the validation set

Fig. 5. Experiment results of simulation. (a)(f)(k) Depth images; (b)(g)(l) depth images predicted by proposed method; (c)(h)(m) error images of proposed method; (d)(i)(n) depth images predicted by method in Ref.[20]; (e)(j)(o) error images of method in Ref.[20]

Fig. 6. 3D effect. (a)(d)(g) 3D shape images; (b)(e)(h) predicted 3D shape images; (c)(f)(i) error images

Fig. 7. Photograph of 3D measurement system

Fig. 8. Deformed fringe patterns obtained by camera. (a) Deformed fringe pattern of mask; (b) deformed fringe pattern of human hand

Fig. 9. 3D shape data of real objects. (a) 3D shape data of mask; (b) 3D shape data of human hand; (c) detail display of mask eye; (d) detail display of human hand finger

Fig. 10. Generalization capability analysis results. (a)(f) Depth images; (b)(g) depth images predicted by proposed method; (c)(h) error images of proposed method; (d)(i) depth images predicted by method in Ref.[20]; (e)(j) error images of method in Ref.[20]

Fig. 11. 3D effect. (a)--(c) 3D shape data, predicted 3D shape data, and error image of sample 1; (d)--(f) 3D shape data, predicted 3D shape data, and error image of sample 2

Fig. 12. Anti-noise capability analysis results. (a)(e)(i)(m) Deformed fringe images; (b)(f)(j)(n) depth images; (c)(g)(k)(o) predicted depth images; (d)(h)(l)(p) error images

Fig. 13. 3D effect. (a)(d)(g)(i) 3D shape data; (b)(e)(h)(k) predicted 3D shape data; (c)(f)(i)(l) error images