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. Our extensive experimental analysis shows that our reconstruction framework i) outperforms the state-of-the-art methods for single view reconstruction, and ii) enables the 3D reconstruction of objects in situations when traditional SFM/SLAM methods fail (because of lack of texture and/or wide baseline).

使用深度学习方法，构建一个名为3D-R2N2的递归神经网络结构，使用大量的合成数据将图像映射到对应的3D形状，无需图像注释或对象分类标签，可以在缺乏纹理或宽基线的情况下，实现对象的3D重建，并在单视图重建方面优于现有的最先进方法。

3D-R2N2：单视角和多视角三维物体重建的统一方法