This paper presents a novel approach to reconstructing the 3D shape of an object from a single image. The approach combines deep neural networks with a silhouette-based 3D reconstruction process. The optimal 3D shape is sought efficiently inside an extremely low-dimensional latent shape space, and the viewpoint and the object shape are jointly optimized based on the result of image segmentation. Evaluation of this approach shows a nearly 20 percent performance gain in viewpoint estimation subsequent to the optimization.
['Qing Rao', 'Lars Krüger', 'Klaus Dietmayer']
Monocular 3D shape reconstruction using deep neural networks