Euclidean Reconstruction of Deformable Structure Using a Perspective Camera with Varying Intrinsic Parameters

In this paper we present a novel approach for the 3D Euclidean reconstruction of deformable objects observed by a perspective camera with variable intrinsic parameters. We formulate the non-rigid shape and motion estimation problem as a non-linear optimization where the objective function to be minimised is the image reprojection error. Our approach is based on the observation that often some of the points on the observed object behave rigidly, while others deform from frame to frame. We propose to use the set of rigid points to obtain an initial estimate of the camera´s varying internal parameters and the overall rigid motion. The prior information that some of the points in the object are rigid can also be added to the non-linear minimization scheme in order to avoid ambiguous configurations. Results on synthetic and real data prove the performance of our algorithm even when using a minimal set of rigid points and when varying the intrinsic camera parameters