| Unsupervised CNN for single view depth estimation: Geometry to the rescue R Garg, VK BG, G Carneiro, I Reid European Conference on Computer Vision, 740-756, 2016 | 2088 | 2016 |
| Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction H Zhan, R Garg, CS Weerasekera, K Li, H Agarwal, I Reid Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 842 | 2018 |
| Dense Variational Reconstruction of Non-Rigid Surfaces from Monocular Video R Garg, A Roussos, A Lourdes Computer Vision and Pattern Recognition (CVPR) 2013, 2013 | 277 | 2013 |
| A variational approach to video registration with subspace constraints R Garg, A Roussos, L Agapito International journal of computer vision 104 (3), 286-314, 2013 | 170 | 2013 |
| Retrieval augmented classification for long-tail visual recognition A Long, W Yin, T Ajanthan, V Nguyen, P Purkait, R Garg, A Blair, C Shen, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 157 | 2022 |
| DF-VO: What should be learnt for visual odometry? H Zhan, CS Weerasekera, JW Bian, R Garg, I Reid arXiv preprint arXiv:2103.00933, 2021 | 75 | 2021 |
| Data-driven approximations to np-hard problems A Milan, S Rezatofighi, R Garg, A Dick, I Reid Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 75 | 2017 |
| Dense Multibody Motion Estimation and Reconstruction from a Handheld Camera A Roussos, C Russell, R Garg, L Agapito ISMAR, 2012 | 59 | 2012 |
| Low-rank kernel subspace clustering P Ji, I Reid, R Garg, H Li, M Salzmann arXiv preprint arXiv:1707.04974 1, 2017 | 56* | 2017 |
| Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks Y Latif, R Garg, M Milford, I Reid ICRA 2018, 2017 | 46 | 2017 |
| Scaling cnns for high resolution volumetric reconstruction from a single image A Johnston, R Garg, G Carneiro, I Reid, A van den Hengel Proceedings of the IEEE International Conference on Computer Vision …, 2017 | 46 | 2017 |
| Self-supervised learning for single view depth and surface normal estimation H Zhan, CS Weerasekera, R Garg, I Reid 2019 International Conference on Robotics and Automation (ICRA), 4811-4817, 2019 | 40 | 2019 |
| Just-in-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors CS Weerasekera, T Dharmasiri, R Garg, T Drummond, I Reid ICRA 2018, 2018 | 37 | 2018 |
| Robust trajectory-space tv-l1 optical flow for non-rigid sequences R Garg, A Roussos, L Agapito International Workshop on Energy Minimization Methods in Computer Vision and …, 2011 | 35 | 2011 |
| Dense multi-frame optic flow for non-rigid objects using subspace constraints R Garg, L Pizarro, D Rueckert, L Agapito Asian Conference on Computer Vision, 460-473, 2010 | 33 | 2010 |
| Dense monocular reconstruction using surface normals CS Weerasekera, Y Latif, R Garg, I Reid 2017 IEEE international conference on robotics and automation (ICRA), 2524-2531, 2017 | 29 | 2017 |
| Semi-supervised semantic segmentation under label noise via diverse learning groups P Li, P Purkait, T Ajanthan, M Abdolshah, R Garg, H Husain, C Xu, ... Proceedings of the IEEE/CVF international conference on computer vision …, 2023 | 27 | 2023 |
| Single-view object shape reconstruction using deep shape prior and silhouette K Li, R Garg, M Cai, I Reid arXiv preprint arXiv:1811.11921, 2018 | 25* | 2018 |
| Learning deeply supervised good features to match for dense monocular reconstruction CS Weerasekera, R Garg, Y Latif, I Reid Asian Conference on Computer Vision, 609-624, 2018 | 11* | 2018 |
| Non-linear dimensionality regularizer for solving inverse problems R Garg, A Eriksson, I Reid arXiv preprint arXiv:1603.05015, 2016 | 11 | 2016 |
Ian D ReidMohammed Bin Zayed University of Artificial Intelligence and University of AdelaideVerified email at adelaide.edu.au
