3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction

Understand

Inspired by the recent success of methods that employ shape priors to achieve robust 3D reconstructions, we propose a novel recurrent neural network architecture that we call the 3D Recurrent Reconstruction Neural Network (3D-R2N2).

• The network learns a mapping from images of objects to their underlying 3D shapes from a large collection of synthetic data.
• Our network takes in one or more images of an object instance from arbitrary viewpoints and outputs a reconstruction of the object in the form of a 3D occupancy grid.
• Unlike most of the previous works, our network does not require any image annotations or object class labels for training or testing.