[1] ROBERT S, JAKUB K,GRZEGORZ M. Archiving shape and appearance of cultural heritage objects using structured light projection and multispectral imaging[J]. Optical Engineering, 2012,51(2): 021115-021118.
[2] SALVIA J, SERGIO F, TOMISLAV P, et al. A state of the art in structured light patterns for surface profilometry[J].Pattern Recognition,2010, 43(8): 2666-2680.
[3] Sansoni G, Trebeschi M, Docchio F. State-of-the-art and applications of 3D imaging sensors in industry, cultural heritage, medicine, and criminal investigation[J]. Sensors, 2009, 9(1): 568-601.
[4] RICOLFE V C, SANCHEZ S A J. Using the camera pin-hole model restrictions to calibrate the lens distortion model[J]. Optics & Laser Technology, 2011, 43(6): 996-1005.
[5] TSAI R Y. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the shelf TV cameras and lenses[J]. IEEE Journal of Robotics and Automation, 1987, 3(4): 323-344.
[6] WANG Z Z. Removal of noise and radial lens distortion during calibration of computer vision systems[J]. Optics Express ,2015, 23(9): 11341-11356.
[7] GONG Y H, MENG D, SEIBEL E J. Bound constrained bundle adjustment for reliable 3D reconstruction[J]. Optics Express ,2015, 23(8): 10771-10785.
[8] HUO J, YANG N, YANG M. Attitude measurement of spatial moving object based on vectors of light beams[J]. Acta Photonica Sinica, 2015, 44(7): 0712001.
[9] GYBENKO G. Approximation by super-positions of a sigmoidal function[J]. Mathematics of Control, Signals and Systems, 1989, 2(4): 303-314.
[10] SCARELLIA F, CHUNG A T. Universal approximation using feedforward neural networks: a survey of some existing methods, and some new results[J]. Neural Networks, 1998,11(1): 15-37.
[11] CHEN C H, YAO T K, KUO C M. Wide-angle camera distortion correction using neural back mapping[C]. IEEE Proceeding of 17th International Symposium on Consumer Electronics. Hsinchu, IEEE, 2013: 171-172.
[12] CAI H M, LI K J, LIU M L, et al. Fast-camera calibration of stereo vision system using BP neural networks[C]. SPIE Proceeding of 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Detector, Imager, Display, and Energy Conversion Technology: 76585B, 2010.
[13] YAN H, WU B. Improved neural network for binocular camera calibration[J]. Journal of southwest university of science and technology, 2013, 28(4): 66-070.
[14] YUAN M X, HU H X, JIANG Y F, et al. A new camera calibration based on neural network with tunable activation function in intelligent space[C]. IEEE Proceeding of Sixth International Symposium on Computational Intelligence and Design. 2013: 371-374.
[15] JIANG X K, FAN Y Q, WANG W. BP neural network camera calibration based on particle swarm optimization genetic algorithm[J]. Journal of Frontiers of Computer Science and Technology, 2014, 8(10): 1254-1262.
[16] HUANG J H, WANG Z, XUE Q, et al. Projector calibration with error surface compensation method in the structured light 3D measurement system[J]. Optical Engineering, 2013, 52(4): 1-10.
[17] HAGAN M T, MENHAJ M B. Training feedforward networks with the Marquardt algorithm[J]. IEEE Transactions on neural networks, 1994, 5(6): 989-993.
[18] HUANG J H. The calibration and precision improvement theories and methods of large complex surface measurement system based on structure light technology[D]. Xi′an, Xi′an Jiaotong University,2013.