Dense 3D Object Reconstruction from a Single Depth View

In this paper, we propose a novel approach, <italic>3D-RecGAN++</italic>, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN++ only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid with <italic>a high resolution of</italic> <inline-formula><tex-math notation="LaTeX">$\boldsymbol{256^3}$</tex-math><alternatives><mml:math><mml:msup><mml:mn mathvariant="bold">256</mml:mn><mml:mn mathvariant="bold">3</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="yang-ieq1-2868195.gif"/></alternatives></inline-formula> by recovering the occluded/missing regions. The key idea is to combine the generative capabilities of 3D encoder-decoder and the conditional adversarial networks framework, to infer accurate and fine-grained 3D structures of objects in high-dimensional voxel space. Extensive experiments on large synthetic datasets and real-world Kinect datasets show that the proposed 3D-RecGAN++ significantly outperforms the state of the art in single view 3D object reconstruction, and is able to reconstruct unseen types of objects.