Vehicle matching and recognition under large variations of pose and illumination

Matching vehicles subject to both large pose transformations and extreme illumination variations remains a technically challenging problem in computer vision. In this paper, we develop a new and robust framework toward matching and recognizing vehicles with both highly varying poses and drastically changing illumination conditions. By effectively estimating both pose and illumination conditions, we can re-render vehicles in the reference image to generate the relit image with the same pose and illumination conditions as the target image. We compare the relit image and the re-rendered target image to match vehicles in the original reference image and target image. Furthermore, no training is needed in our framework and re-rendered vehicle images in any other viewpoints and illumination conditions can be obtained from just one single input image. Experimental results demonstrate the robustness and efficacy of our framework, with a potential to generalize our current method from vehicles to handle other types of objects.