In this paper, we propose a novel 3D-RecGAN approach, which reconstructs the
complete 3D structure of a given object from a single arbitrary depth view
using generative adversarial networks. Unlike the 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 by filling in the occluded/missing regions. The key idea is to
combine the generative capabilities of autoencoders and the conditional
Generative Adversarial Networks (GAN) framework, to infer accurate and
fine-grained 3D structures of objects in high-dimensional voxel space.
Extensive experiments on large synthetic 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. Our
code and data are available at: this https URL

本文提出了一种新颖的 3D-RecGAN 方法，使用生成对抗网络从单个任意深度视角重建给定对象的完整三维结构，在高维体素空间中通过结合自动编码器和条件生成对抗网络框架的生成能力恢复物体的准确和细粒度的三维结构。广泛的实验表明，该方法在单视角 3D 物体重建方面明显优于现有技术，且能够重建未被见过的物体类型。