| Understanding the effective receptive field in deep convolutional neural networks W Luo, Y Li, R Urtasun, R Zemel Advances in neural information processing systems 29, 2016 | 1648 | 2016 |
| Pixor: Real-time 3d object detection from point clouds B Yang, W Luo, R Urtasun Proceedings of the IEEE conference on Computer Vision and Pattern …, 2018 | 1100 | 2018 |
| Efficient deep learning for stereo matching W Luo, AG Schwing, R Urtasun Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 860 | 2016 |
| Fast and furious: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net W Luo, B Yang, R Urtasun Proceedings of the IEEE conference on Computer Vision and Pattern …, 2018 | 641 | 2018 |
| Deeproadmapper: Extracting road topology from aerial images G Máttyus, W Luo, R Urtasun Proceedings of the IEEE international conference on computer vision, 3438-3446, 2017 | 396 | 2017 |
| Intentnet: Learning to predict intention from raw sensor data S Casas, W Luo, R Urtasun Conference on Robot Learning, 947-956, 2018 | 367 | 2018 |
| End-to-end interpretable neural motion planner W Zeng, W Luo, S Suo, A Sadat, B Yang, S Casas, R Urtasun Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 322 | 2019 |
| Torontocity: Seeing the world with a million eyes S Wang, M Bai, G Mattyus, H Chu, W Luo, B Yang, J Liang, J Cheverie, ... arXiv preprint arXiv:1612.00423, 2016 | 188 | 2016 |
| Exploiting semantic information and deep matching for optical flow M Bai, W Luo, K Kundu, R Urtasun Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 147 | 2016 |
| Efficient summarization with read-again and copy mechanism W Zeng, W Luo, S Fidler, R Urtasun arXiv preprint arXiv:1611.03382, 2016 | 115 | 2016 |
| Exploiting deep matching and SAR data for the geo-localization accuracy improvement of optical satellite images N Merkle, W Luo, S Auer, R Müller, R Urtasun Remote Sensing 9 (6), 586, 2017 | 95 | 2017 |
| Efficient convolutions for real-time semantic segmentation of 3d point clouds C Zhang, W Luo, R Urtasun 2018 International Conference on 3D Vision (3DV), 399-408, 2018 | 88 | 2018 |
| Latent structured active learning W Luo, A Schwing, R Urtasun Advances in Neural Information Processing Systems 26, 2013 | 82 | 2013 |
| Systems and methods for object detection, tracking, and motion prediction W Luo, B Yang, R Urtasun US Patent 11,475,351, 2022 | 43 | 2022 |
| Three Dimensional Object Detection W Luo, B Yang, R Urtasun US Patent App. 16/133,046, 2019 | 39 | 2019 |
| Multi-task machine-learned models for object intention determination in autonomous driving S Casas, W Luo, R Urtasun US Patent 11,370,423, 2022 | 37 | 2022 |
| Semantic segmentation of three-dimensional data CJH Zhang, W Luo, R Urtasun US Patent 10,970,553, 2021 | 37 | 2021 |
| End-To-End Interpretable Motion Planner for Autonomous Vehicles W Zeng, W Luo, A Sadat, B Yang, R Urtasun US Patent App. 16/541,739, 2020 | 34 | 2020 |
| Jfp: Joint future prediction with interactive multi-agent modeling for autonomous driving W Luo, C Park, A Cornman, B Sapp, D Anguelov Conference on Robot Learning, 1457-1467, 2023 | 12 | 2023 |
| Universal embeddings for spatio-temporal tagging of self-driving logs S Segal, E Kee, W Luo, A Sadat, E Yumer, R Urtasun Conference on Robot Learning, 973-983, 2021 | 4 | 2021 |
