Abstract: This paper presents a novel approach for object classification and pose estimation which employs spherical light field rendering to generate virtual views based on synthesis parameters determined and successively refined in a two-stage analysis by synthesis process. Compared to previous object recognition techniques the presented approach provides a significant improvement in terms of object classification quality and computational efficiency. Our GPU based light field renderer exploits per-pixel depth information available with modern time-of-flight sensors such as the PMD camera for high-quality image synthesis in real-time. The renderer uses combined per-pixel RGB and depth values to minimize ghosting artefacts to a non noticeable amount and employs a spherical parameterisation to ensure full six degrees of freedom for virtual view synthesis. Synthetic views are used in our two-stage analysis by synthesis technique which implements a pre-classification and pre-pose-estimat...