Camera calibration correction in Shape from Inconsistent Silhouette

Abstract

The use of shape from silhouette for reconstruction tasks is plagued by two types of real-world errors: camera calibration error and silhouette segmentation error. When either error is present, we call the problem the Shape from Inconsistent Silhouette (SfIS) problem. In this paper, we show how small camera calibration error can be corrected when using a previously-published SfIS technique to generate a reconstruction, by using an Iterative Closest Point (ICP) approach. We give formulations under two scenarios: the first of which is only external camera calibration parameters rotation and translation need to be corrected for each camera and the second of which is that both internal and external parameters need to be corrected. We formulate the problem as a 2D-3D ICP problem and find approximate solutions using a nonlinear minimization algorithm, the Levenberg-Marquadt method. We demonstrate the ability of our algorithm to create more representative reconstructions of both synthetic and real datasets of thin objects as compared to uncorrected datasets.