| Zero-1-to-3: Zero-shot one image to 3d object R Liu, R Wu, B Van Hoorick, P Tokmakov, S Zakharov, C Vondrick Proceedings of the IEEE/CVF international conference on computer vision …, 2023 | 596 | 2023 |
| DPOD: 6D Pose Object Detector and Refiner S Zakharov, I Shugurov, S Ilic IEEE International Conference on Computer Vision (ICCV), 2019 | 534 | 2019 |
| HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects R Kaskman, S Zakharov, I Shugurov, S Ilic IEEE International Conference on Computer Vision Workshops (ICCVW), 2019 | 139 | 2019 |
| Autolabeling 3D Objects with Differentiable Rendering of SDF Shape Priors S Zakharov, W Kehl, A Bhargava, A Gaidon IEEE Computer Vision and Pattern Recognition (CVPR), 2020 | 110 | 2020 |
| DeceptionNet: Network-Driven Domain Randomization S Zakharov, W Kehl, S Ilic IEEE International Conference on Computer Vision (ICCV), 2019 | 104 | 2019 |
| Depthsynth: Real-time realistic synthetic data generation from cad models for 2.5 d recognition B Planche, Z Wu, K Ma, S Sun, S Kluckner, O Lehmann, T Chen, A Hutter, ... International Conference on 3D Vision (3DV), 1-10, 2017 | 91 | 2017 |
| Multi-frame self-supervised depth with transformers V Guizilini, R Ambruș, D Chen, S Zakharov, A Gaidon Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 77 | 2022 |
| Shapo: Implicit representations for multi-object shape, appearance, and pose optimization MZ Irshad, S Zakharov, R Ambrus, T Kollar, Z Kira, A Gaidon European Conference on Computer Vision, 275-292, 2022 | 54 | 2022 |
| Dpodv2: Dense correspondence-based 6 dof pose estimation I Shugurov, S Zakharov, S Ilic IEEE transactions on pattern analysis and machine intelligence 44 (11), 7417 …, 2021 | 48 | 2021 |
| 3D Object Instance Recognition and Pose Estimation Using Triplet Loss with Dynamic Margin S Zakharov, W Kehl, B Planche, A Hutter, S Ilic IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017 | 47 | 2017 |
| When regression meets manifold learning for object recognition and pose estimation M Bui, S Zakharov, S Albarqouni, S Ilic, N Navab IEEE International Conference on Robotics and Automation (ICRA), 1-7, 2018 | 33 | 2018 |
| Keep it unreal: Bridging the realism gap for 2.5 d recognition with geometry priors only S Zakharov, B Planche, Z Wu, A Hutter, H Kosch, S Ilic International Conference on 3D Vision (3DV), 1-11, 2018 | 29 | 2018 |
| Neural groundplans: Persistent neural scene representations from a single image P Sharma, A Tewari, Y Du, S Zakharov, R Ambrus, A Gaidon, ... arXiv preprint arXiv:2207.11232, 2022 | 22* | 2022 |
| Multi-view object pose refinement with differentiable renderer I Shugurov, I Pavlov, S Zakharov, S Ilic IEEE Robotics and Automation Letters 6 (2), 2579-2586, 2021 | 22 | 2021 |
| Single-shot scene reconstruction S Zakharov, RA Ambrus, VC Guizilini, D Park, W Kehl, F Durand, ... 5th Annual Conference on Robot Learning, 2021 | 16 | 2021 |
| 6 dof pose estimation of textureless objects from multiple rgb frames R Kaskman, I Shugurov, S Zakharov, S Ilic Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020 …, 2020 | 14 | 2020 |
| Seeing beyond appearance-mapping real images into geometrical domains for unsupervised cad-based recognition B Planche, S Zakharov, Z Wu, A Hutter, H Kosch, S Ilic IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019 | 14 | 2019 |
| Spot: Spatiotemporal modeling for 3d object tracking C Stearns, D Rempe, J Li, R Ambruş, S Zakharov, V Guizilini, Y Yang, ... European Conference on Computer Vision, 639-656, 2022 | 12 | 2022 |
| Photo-realistic neural domain randomization S Zakharov, R Ambruș, V Guizilini, W Kehl, A Gaidon European Conference on Computer Vision, 310-327, 2022 | 10 | 2022 |
| ROAD: Learning an Implicit Recursive Octree Auto-Decoder to Efficiently Encode 3D Shapes S Zakharov, RA Ambrus, K Liu, A Gaidon 6th Annual Conference on Robot Learning, 2022 | 5 | 2022 |
Slobodan IlicSenior Key Expert Research Scientist, Siemens AG and Adjunct Professor at TUMVerified email at in.tum.de
