Dense 3D Object Reconstruction from a Single Depth View
This addresses the challenge of 3D reconstruction for robotics or AR/VR applications with limited input, though it builds on existing GAN and autoencoder techniques.
The paper tackles the problem of reconstructing complete 3D objects from a single depth view, achieving high-resolution 256^3 voxel grids and outperforming state-of-the-art methods on synthetic and real-world datasets.
In this paper, we propose a novel approach, 3D-RecGAN++, 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 a high resolution of 256^3 by recovering 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 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.