3D Shape from Silhouette Points in Registered 2D Images Using Conjugate Gradient Method

摘要

We describe a simple and robust algorithm for estimating 3D shape given a number of silhouette points obtained from two or more viewpoints and a parametric model of the shape. Our algorithm minimizes (in the least squares sense) the distances from the lines obtained by unprojecting the silhouette points to 3D to their closest silhouette points on the 3D shape. The solution is found using an iterative approach. In each iteration, we locally approximate the least squares problem with a degree-4 polynomial function. The approximate problem is solved using a nonlinear conjugate gradient solver that takes advantage of its structure to perform exact and global line searches. We tested our algorithm by applying it to reconstruct patient-specific femur shapes from simulated biplanar X-ray images.