This Letter presents a multiview phase shifting (MPS) framework for full-resolution and high-speed reconstruction of arbitrary shape dynamic objects. Unlike conventional methods, this framework can directly find the corresponding points from the wrapped phase-maps. Therefore, only a minimum number of images are required for phase shifting to measure arbitrary shape objects, including discontinuous surfaces. Benefit from phase shifting MPS can achieve full spatial resolution and high, accurate 3D reconstruction. Benefit from multiview constraint MPS is also robust to discontinuities. Experimental results are presented to verify the performance of the proposed technique.

In this paper we present a novel method for determining the probing points for achieving efficient measurement and reconstruction of freeform surfaces. A B-spline is adopted for modelling the freeform surface. In the framework of Bayesian statistics, we develop a model selection strategy to obtain an optimal model structure for the freeform surface. Based on the selected model structure, a set of probing points is then determined where measurements are to be made. In order to obtain reliable parameter estimation for the B-spline model, we analyse the uncertainty of the model and use the statistical analysis of the Fisher information matrix to optimize the locations of the probing points needed in the measurements. Using a 'data cloud' of a surface acquired by a 3D vision system, we implemented the proposed method for reconstructing freeform surfaces. The experimental results show that the method is effective and promises useful applications in multi-sensor measurements including a vision guided coordinate measuring machine for reverse engineering.

We propose a novel microscopic photometric stereo (MPS) method based on a conventional optical microscope and varying illuminations for dense and refined microstructure 3D measurement. To guarantee the flexibility of the MPS, an uncalibrated photometric stereo (UPS) method, which does not require a priori knowledge of the light-source direction or the light-source intensity, is employed to recover surface normals and albedos from the captured multiple micro-images. Although the UPS has been studied before, there are some particular issues to be addressed to make it suitable for microscopic cases. For resolving the inherent generalized bas-relief (GBR) ambiguity of the UPS, we present a GBR disambiguation method based on a framework of entropy minimization, and extend it using a graph-cut energy minimization to decrease the influence of noise and further refine the recovered surface normal. The proposed MPS method has been tested on synthetic as well as real images and very encouraging results have been obtained. The experimental results show that this novel method can reconstruct dense and refined 3D points for the microstructure. It is an easy-to-implement yet effective alternative method for microstructure 3D measurement and can be applied to many potential fields.