Fine pose estimation of known objects in cluttered scene images

Understanding the precise 3D structure of an environment is one of the fundamental goals of computer vision and is challenging due to a variety of factors such as appearance variation, illumination, pose, noise, occlusion and scene clutter. A generic solution to the problem is ill-posed due to the loss of depth information during imaging. In this paper, we consider a specific but common situation, where the scene contains known objects. Given 3D models of a set of known objects and a cluttered scene image, we try to detect these objects in the image, and align 3D models to their images to find their exact pose. We develop an approach that poses this as a 3D-to-2D alignment problem. We also deal with pose estimation of 3D articulated objects in images. We evaluate our proposed method on BigBird dataset and our own tabletop dataset, and present experimental comparisons with state-of-the-art methods.