| 3d pose regression using convolutional neural networks S Mahendran, H Ali, R Vidal Proceedings of the IEEE International Conference on Computer Vision …, 2017 | 254 | 2017 |
| Understanding objects in detail with fine-grained attributes A Vedaldi, S Mahendran, S Tsogkas, S Maji, R Girshick, J Kannala, ... Proceedings of the IEEE conference on computer vision and pattern …, 2014 | 127 | 2014 |
| A mixed classification-regression framework for 3d pose estimation from 2d images S Mahendran, H Ali, R Vidal arXiv preprint arXiv:1805.03225, 2018 | 49 | 2018 |
| Monocular object orientation estimation using riemannian regression and classification networks S Mahendran, MY Lu, H Ali, R Vidal arXiv preprint arXiv:1807.07226, 2018 | 10 | 2018 |
| Convolutional networks for object category and 3D pose estimation from 2D images S Mahendran, H Ali, R Vidal Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 0-0, 2018 | 9 | 2018 |
| Car segmentation and pose estimation using 3d object models S Mahendran, R Vidal arXiv preprint arXiv:1512.06790, 2015 | 5 | 2015 |
| Multi-user, scalable 3d object detection in ar cloud S Choudhary, N Sekhar, S Mahendran, P Singhal Proceedings of IEEE CVPR Workshop on Computer Vision for Augmented and …, 2020 | 3 | 2020 |
| Hypothesize and bound: A computational focus of attention mechanism for simultaneous 3D shape reconstruction, pose estimation and classification from a single 2D image D Rother, S Mahendran, R Vidal arXiv preprint arXiv:1109.5730, 2011 | 3 | 2011 |
| Object recognition neural network for amodal center prediction S Mahendran, N Bansal, N Sekhar, M Gangwar, K Gupta, P Singhal US Patent App. 17/357,118, 2021 | 2 | 2021 |
| Joint object category and 3d pose estimation from 2d images S Mahendran, H Ali, R Vidal arXiv preprint arXiv:1711.07426 11, 2017 | 2 | 2017 |
| Object recognition neural network training using multiple data sources S Mahendran, N Bansal, N Sekhar, M Gangwar, K Gupta, P Singhal, ... US Patent App. 18/007,288, 2023 | 1 | 2023 |
| Scene understanding using occupancy grids D Ramnath, S Dong, S Choudhary, S Mahendran, AK Kannan, P Singhal, ... US Patent App. 18/275,468, 2024 | | 2024 |
| Scalable three-dimensional object recognition in a cross reality system S Choudhary, D Ramnath, S Dong, S Mahendran, AK Kannan, P Singhal, ... US Patent 11,704,806, 2023 | | 2023 |
| Advanced Neural Network Training System S Mahendran, T Liu, YJ Lee, M Parandehgheibi US Patent App. 17/554,858, 2023 | | 2023 |
| Vehicle semantic keypoint point cloud definition A Elogeel, A Pon, D Nandi, A Hasfura, C Bagwell, M Parandehgheibi, ... US Patent App. 17/556,612, 2023 | | 2023 |
| Using keypoints for fine-grained detection and tracking A Elogeel, A Pon, D Nandi, A Hasfura, C Bagwell, M Parandehgheibi, ... US Patent App. 17/556,523, 2023 | | 2023 |
| Training Neural Networks for Object Detection S Mahendran, YJ Lee, TL Liu, M Parandehgheibi, B Tian, A Sidorov US Patent App. 17/554,894, 2023 | | 2023 |
| Scalable three-dimensional object recognition in a cross reality system S Choudhary, D Ramnath, S Dong, S Mahendran, AK Kannan, P Singhal, ... US Patent 11,257,300, 2022 | | 2022 |
| Geometric Deep Learning for Monocular Object Orientation Estimation S Mahendran The Johns Hopkins University, 2019 | | 2019 |
Rene VidalRachleff University Professor, University of Pennsylvania; Amazon Scholar; Chief Scientist, NORCEDirección de correo verificada de jhu.edu
