Extrinsic calibration of a laser displacement sensor in a non-contact coordinate measuring machine

In order to implement 3D scanning of those complicated parts such as blades in the aviation field,a non-contact optical measuring system is established in the paper,which integrates a laser displacement sensor,a probe head,the frame of a coordinate measuring machine（CMM）,etc.As the output of the laser sensor directly obtained possesses the 1D length of the laser beam,it needs to determine the unit direction vector of the laser beam denoted as（l,m,n）by calibration so as to convert the 1D values into 3D coordinates of target points.Therefore,an extrinsic calibration method based on a standard sphere is proposed to accomplish this task in the paper.During the calibration procedure,the laser sensor moves along with the motion of the CMM and gathers the required data on the spherical surface.Then,both the output of the laser sensor and the grating readings of the CMM are substituted into the constraint equation of the spherical surface,in which an over-determined nonlinear equation group containing unknown parameters is established.For the purpose of solving the equation group,a method based on non-linear least squares optimization is put forward.Finally,the system after calibration is utilized to measure the diameter of a metallic sphere 10 times from different orientations to verify the calibration accuracy.In the experiment,the errors between the measured results and the true values are all smaller than 0.03 mm,which manifests the validity and practicality of the extrinsic calibration method presented in the paper.

3D Face Reconstruction Using Images from Cameras with Varying Parameters

In this paper, we present a new technique of 3D face reconstruction from a sequence of images taken with cameras having varying parameters without the need to grid. This method is based on the estimation of the projection matrices of the cameras from a symmetry property which characterizes the face, these projections matrices are used with points matching in each pair of images to determine the 3D points cloud, subsequently, 3D mesh of the face is constructed with 3D Crust algorithm. Lastly, the 2D image is projected on the 3D model to generate the texture mapping. The strong point of the proposed approach is to minimize the constraints of the calibration system： we calibrated the cameras from a symmetry property which characterizes the face, this property gives us the opportunity to know some points of 3D face in a specific well-chosen global reference, to formulate a system of linear and nonlinear equations according to these 3D points, their projection in the image plan and the elements of the projections matrix. Then to solve these equations, we use a genetic algorithm which consists of finding the global optimum without the need of the initial estimation and allows to avoid the local minima of the formulated cost function. Our study is conducted on real data to demonstrate the validity and the performance of the proposed approach in terms of robustness, simplicity, stability and convergence.