| Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling J Wu, C Zhang, T Xue, B Freeman, J Tenenbaum Advances in neural information processing systems 29, 2016 | 2404 | 2016 |
| Pix3d: Dataset and methods for single-image 3d shape modeling X Sun, J Wu, X Zhang, Z Zhang, C Zhang, T Xue, JB Tenenbaum, ... Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 572 | 2018 |
| Visual object networks: Image generation with disentangled 3d representations JY Zhu, Z Zhang, C Zhang, J Wu, A Torralba, J Tenenbaum, B Freeman Advances in neural information processing systems 31, 2018 | 272 | 2018 |
| Learning shape priors for single-view 3d completion and reconstruction J Wu, C Zhang, X Zhang, Z Zhang, WT Freeman, JB Tenenbaum Proceedings of the European conference on computer vision (ECCV), 646-662, 2018 | 227 | 2018 |
| Learning to reconstruct shapes from unseen classes X Zhang, Z Zhang, C Zhang, J Tenenbaum, B Freeman, J Wu Advances in neural information processing systems 31, 2018 | 179 | 2018 |
| A comparative evaluation of approximate probabilistic simulation and deep neural networks as accounts of human physical scene understanding R Zhang, J Wu, C Zhang, WT Freeman, JB Tenenbaum arXiv preprint arXiv:1605.01138, 2016 | 52 | 2016 |
| Learning 3D shape priors for shape completion and reconstruction J Wu, C Zhang, X Zhang, Z Zhang, WT Freeman, JB Tenenbaum European Conference on Computer Vision (ECCV) 3, 2018 | 34 | 2018 |
| Seeing tree structure from vibration T Xue, J Wu, Z Zhang, C Zhang, JB Tenenbaum, WT Freeman Proceedings of the European Conference on Computer Vision (ECCV), 748-764, 2018 | 14 | 2018 |
| Generative adversarial modeling of 3D shapes C Zhang Massachusetts Institute of Technology, 2018 | 1 | 2018 |
| A Comparative Evaluation of Approximate Probabilistic Simulation and DeepNeural R Zhang, J Wu, C Zhang Proceedings of the Annual Meeting of the Cognitive Science Society, 38 (0), 2016 | | 2016 |
Jiajun WuStanford UniversityAdresse e-mail validée de cs.stanford.edu