Model-based 3D-motion estimation with illumination compensation

We present a model-based algorithm for the estimation of three-dimensional motion parameters of an object moving in 3D-space. Photometric effects are taken into account by adding different illumination models to the virtual scene. Using the additional information from three-dimensional geometric models of the scene leads to linear algorithms for the parameter estimation of the illumination models which are all computationally efficient. Experiments show that the peak signal noise ratio (PSNR) between camera and reconstructed synthetic images can be increased by up to 7 dB compared to global illumination compensation. The average estimation error of the motion parameters is at the same time reduced by 40%.