Inverse Compositional Estimation of 3D Pose And Lighting in Dynamic Scenes

Abstract

In this paper, we show how to estimate, accurately and efficiently, the 3D motion of a rigid object and time-varying lighting in a dynamic scene. This is achieved in an inverse compositional tracking framework with a novel warping function that involves a 2D --> 3D --> 2D transformation. This also allows us to extend traditional two frame inverse compositional tracking to a sequence of frames, leading to even higher computational savings. We prove the theoretical convergence of this method and show that it leads to significant reduction in computational burden. Experimental analysis on multiple video sequences shows impressive speed-up over existing methods while retaining a high level of accuracy.