Easy calibration of a structured light vision system based on neural networks

Abstract

The structured light vision system consists of a CCD camera and a digital projector. Calibration of such a vision system plays an important means of accurate 3D reconstruction of a scene. However, the projection model for both the camera and projector is very complicated because of distorted and nonlinear factors in it. It is unlikely to accurately model a camera with only a few parameters even considering some lens distortions. In order to simplify the system calibration and 3D reconstruction, this work presents a new calibration method that is based on neural network and brought forward according to the characteristics of neural network and vision measurement. The relation between spatial points and image points is established by training the network without the parameters of the camera and the projector, such as focus, distortions besides the geometry of the system. The training set for the neural network consists of a variety of lighting patterns and their projected images and the corresponding 3D world coordinates. Such a calibration method has two distinct advantages. It possesses the complicated nonlinear relation between two-dimensional information and three-dimensional information with the neural network, which can include various kinds of distortion and other nonlinear factors during the imaging period. Experiments are carried out to demonstrate and evaluate the procedure. From the result of training we can find out that through the neural network, it may avoid non-linear operation and obtaining the three-dimensional coordinates directly.