This paper introduces a method for metric self-calibration that is based on a novel decomposition of the fundamental matrix between two views taken by a camera with fixed internal parameters. The method blends important advantages of the Kruppa constraints and the modulus constraint: it works directly from fundamental matrices and uses a reduced-parameter representation for stability. General properties of the new decomposition are also developed, including an intuitive interpretation of the three free parameters of internal calibration. The approach is demonstrated on both real and synthetic data.

We introduce the problem of view interpolation for dynamic scenes. Our solution to this problem extends the concept of view morphing and retains the practical advantages of that method. We are specifically concerned with interpolating between two reference views captured at different times, so that there is a missing interval of time between when the views were taken. The synthetic interpolations produced by our algorithm portray one possible physically-valid version of what transpired in the scene during the missing time. It is assumed that each object in the original scene underwent a series of rigid translations. Dynamic view morphing can work with widely-spaced reference views, sparse point correspondences, and uncalibrated cameras. When the camera-to-camera transformation can be determined, the synthetic interpolation will portray scene objects moving along straight-line, constant-velocity trajectories in world space.