| Deep Hough Voting for 3D Object Detection in Point Clouds CR Qi, O Litany, K He, LJ Guibas ICCV 2019 (Oral, Best Paper Nomination), 2019 | 1381 | 2019 |
| PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding S Xie, J Gu, D Guo, CR Qi, LJ Guibas, O Litany ECCV 2020 Spotlight, 2020 | 617 | 2020 |
| Get3d: A generative model of high quality 3d textured shapes learned from images J Gao, T Shen, Z Wang, W Chen, K Yin, D Li, O Litany, Z Gojcic, S Fidler Advances In Neural Information Processing Systems 35, 31841-31854, 2022 | 384 | 2022 |
| Lion: Latent point diffusion models for 3d shape generation A Vahdat, F Williams, Z Gojcic, O Litany, S Fidler, K Kreis Advances in Neural Information Processing Systems 35, 10021-10039, 2022 | 368 | 2022 |
| Neural fields in visual computing and beyond Y Xie, T Takikawa, S Saito, O Litany, S Yan, N Khan, F Tombari, ... Computer Graphics Forum 41 (2), 641-676, 2022 | 350 | 2022 |
| Deep functional maps: Structured prediction for dense shape correspondence O Litany, T Remez, E Rodola, A Bronstein, M Bronstein Proceedings of the IEEE international conference on computer vision, 5659-5667, 2017 | 313 | 2017 |
| ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes CR Qi, X Chen, O Litany, LJ Guibas CVPR 2020, 2020 | 311 | 2020 |
| Vector Neurons: A General Framework for SO (3)-Equivariant Networks C Deng, O Litany, Y Duan, A Poulenard, A Tagliasacchi, L Guibas ICCV 2021, 2021 | 279 | 2021 |
| Deformable shape completion with graph convolutional autoencoders O Litany, A Bronstein, M Bronstein, A Makadia Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 267 | 2018 |
| Unsupervised learning of dense shape correspondence O Halimi, O Litany, E Rodola, AM Bronstein, R Kimmel Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 182 | 2019 |
| Class-aware fully convolutional Gaussian and Poisson denoising T Remez, O Litany, R Giryes, AM Bronstein IEEE Transactions on Image Processing 27 (11), 5707-5722, 2018 | 169* | 2018 |
| Mix3D: Out-of-Context Data Augmentation for 3D Scenes A Nekrasov, J Schult, O Litany, B Leibe, F Engelmann 3DV 2021, 2021 | 145 | 2021 |
| 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection H Wang, Y Cong, O Litany, Y Gao, LJ Guibas CVPR 2021, 2020 | 131 | 2020 |
| Language-Grounded Indoor 3D Semantic Segmentation in the Wild D Rozenberszki, O Litany, A Dai ECCV 2022, 2022 | 129 | 2022 |
| Fully Spectral Partial Shape Matching O Litany, E Rodolà, AM Bronstein, MM Bronstein Computer Graphics Forum 36 (2), 2017 | 124 | 2017 |
| On Learning Sets of Symmetric Elements H Maron, O Litany, G Chechik, E Fetaya ICML 2020 -- Outstanding paper award, 2020 | 123 | 2020 |
| Mask3d: Mask transformer for 3d semantic instance segmentation J Schult, F Engelmann, A Hermans, O Litany, S Tang, B Leibe 2023 IEEE International Conference on Robotics and Automation (ICRA), 8216-8223, 2023 | 117 | 2023 |
| Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior D Rempe, J Philion, LJ Guibas, S Fidler, O Litany CVPR 2022, 2021 | 109 | 2021 |
| Weakly Supervised Learning of Rigid 3D Scene Flow Z Gojcic, O Litany, A Wieser, LJ Guibas, T Birdal CVPR 2021, 2021 | 105 | 2021 |
| Efficient deformable shape correspondence via kernel matching Z Lähner, M Vestner, A Boyarski, O Litany, R Slossberg, T Remez, ... 3DV, 2017 | 104* | 2017 |
Leonidas GuibasProfessor of Computer Science, Stanford University在 cs.stanford.edu 的电子邮件经过验证
