Autocalibration for Structure from Motion

This paper is about the estimation of calibration parameters of images to be used in Structure from Motion (SfM) pipelines and 3D reconstruction from image feature correspondences. It addresses the estimation of calibration parameters when they are not available, so that additional images may be included in the 3D reconstruction and so that the initial model may be closer to the true geometry of the scene. The approach is to take advantage of known calibration information of some of the images, to estimate calibration information of uncalibrated views, calibration information is therefore extended to images where visual features of the same objects are detected. The approach is based on the standard fundamental matrix, and extended versions of the fundamental matrix that embed the radial distortion model, named radial fundamental matrices. It is shown that the distortion model may be extracted from radial fundamental matrices, along with the standard fundamental matrix, and that the focal length may be subsequently estimated from it. By integrating a few of methods, the number of images that can be used in a large scale 3D reconstruction may be augmented and a better geometric model may be reconstructed. With this approach, the initial values of the parameters and the reconstructed geometry are close to the true solution, so that an optimization step may converge without getting stuck in local minima.