• DOI:10.1007/978-3-319-46484-8_38
Corpus ID: 6325059

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

@inproceedings{Choy20163DR2N2AU,
  title={3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction},
  author={C. Choy and Danfei Xu and JunYoung Gwak and Kevin Chen and S. Savarese},
  booktitle={ECCV},
  year={2016}
}

• C. Choy, Danfei Xu, +2 authors S. Savarese
• Published in ECCV 2016
• Computer Science

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 [13]. Our network takes in one or more images of an object instance from arbitrary viewpoints and outputs a reconstruction of the object in… Expand

View PDF on arXiv

Save to Library

Create Alert

Cite

Launch Research Feed