| A papier-mâché approach to learning 3d surface generation T Groueix, M Fisher, VG Kim, BC Russell, M Aubry Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 1053 | 2018 |
| The wave kernel signature: A quantum mechanical approach to shape analysis M Aubry, U Schlickewei, D Cremers 2011 IEEE international conference on computer vision workshops (ICCV …, 2011 | 831 | 2011 |
| Seeing 3D chairs: exemplar part-based 2D-3D alignment using a large dataset of CAD models M Aubry, D Maturana, A Efros, B Russell, J Sivic Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2014 | 597 | 2014 |
| Learning dense correspondence via 3d-guided cycle consistency T Zhou, P Krahenbuhl, M Aubry, Q Huang, AA Efros Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 373 | 2016 |
| Dex-Net 1.0: A Cloud-Based Network of 3D Objects for Robust Grasp Planning Using a Multi-Armed Bandit Model with Correlated Rewards J Mahler, FT Pokorny, B Hou, M Roderick, M Laskey, M Aubry, K Kohlhoff, ... | 357 | 2016 |
| Cosypose: Consistent multi-view multi-object 6d pose estimation Y Labbé, J Carpentier, M Aubry, J Sivic Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 270 | 2020 |
| 3D-CODED: 3D Correspondences by Deep Deformation T Groueix, M Fisher, VG Kim, BC Russell, M Aubry Proceedings of the European Conference on Computer Vision (ECCV), 230-246, 2018 | 269 | 2018 |
| Fast local laplacian filters: Theory and applications M Aubry, S Paris, SW Hasinoff, J Kautz, F Durand ACM Transactions on Graphics (TOG) 33 (5), 1-14, 2014 | 232 | 2014 |
| Understanding deep features with computer-generated imagery M Aubry, BC Russell Proceedings of the IEEE International Conference on Computer Vision, 2875-2883, 2015 | 163 | 2015 |
| Learning elementary structures for 3d shape generation and matching T Deprelle, T Groueix, M Fisher, V Kim, B Russell, M Aubry Advances in Neural Information Processing Systems, 7435-7445, 2019 | 147 | 2019 |
| Painting-to-3D model alignment via discriminative visual elements M Aubry, BC Russell, J Sivic ACM Transactions on Graphics (ToG) 33 (2), 1-14, 2014 | 134 | 2014 |
| Deep exemplar 2d-3d detection by adapting from real to rendered views F Massa, BC Russell, M Aubry Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 110 | 2016 |
| 3d sketching using multi-view deep volumetric prediction J Delanoy, M Aubry, P Isola, AA Efros, A Bousseau Proceedings of the ACM on Computer Graphics and Interactive Techniques 1 (1 …, 2018 | 109 | 2018 |
| Crafting a multi-task CNN for viewpoint estimation F Massa, R Marlet, M Aubry arXiv preprint arXiv:1609.03894, 2016 | 88 | 2016 |
| Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learning X Shen, AA Efros, M Aubry arXiv preprint arXiv:1903.02678, 2019 | 82 | 2019 |
| A super-resolution framework for high-accuracy multiview reconstruction B Goldlücke, M Aubry, K Kolev, D Cremers International journal of computer vision 106, 172-191, 2014 | 69 | 2014 |
| Ransac-flow: generic two-stage image alignment X Shen, F Darmon, AA Efros, M Aubry Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 68 | 2020 |
| Anisotropic Laplace-Beltrami Operators for Shape Analysis M Andreux, E Rodola, M Aubry, D Cremers Computer Vision-ECCV 2014 Workshops, 299-312, 2014 | 68 | 2014 |
| Pose from Shape: Deep Pose Estimation for Arbitrary 3D Objects Y Xiao, X Qiu, PA Langlois, M Aubry, R Marlet arXiv preprint arXiv:1906.05105, 2019 | 52 | 2019 |
| Monte-carlo tree search for efficient visually guided rearrangement planning Y Labbé, S Zagoruyko, I Kalevatykh, I Laptev, J Carpentier, M Aubry, ... IEEE Robotics and Automation Letters 5 (2), 3715-3722, 2020 | 51 | 2020 |
Bryan RussellResearcher, AdobeVerified email at adobe.com
