Monocular 3D shape reconstruction using deep neural networks
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.
2016 IEEE Intelligent Vehicles Symposium (IV)