| Adapnet: Adaptive semantic segmentation in adverse environmental conditions A Valada, J Vertens, A Dhall, W Burgard 2017 IEEE International Conference on Robotics and Automation (ICRA), 4644-4651, 2017 | 226 | 2017 |
| Smsnet: Semantic motion segmentation using deep convolutional neural networks J Vertens, A Valada, W Burgard 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2017 | 80 | 2017 |
| Long-term urban vehicle localization using pole landmarks extracted from 3-D lidar scans A Schaefer, D Büscher, J Vertens, L Luft, W Burgard 2019 European Conference on Mobile Robots (ECMR), 1-7, 2019 | 67 | 2019 |
| Heatnet: Bridging the day-night domain gap in semantic segmentation with thermal images J Vertens, J Zürn, W Burgard 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 52 | 2020 |
| Long-term vehicle localization in urban environments based on pole landmarks extracted from 3-D lidar scans A Schaefer, D Büscher, J Vertens, L Luft, W Burgard Robotics and Autonomous Systems 136, 103709, 2021 | 25 | 2021 |
| Learning object placements for relational instructions by hallucinating scene representations O Mees, A Emek, J Vertens, W Burgard 2020 IEEE International Conference on Robotics and Automation (ICRA), 94-100, 2020 | 23 | 2020 |
| Measuring Respiration and Heart Rate using Two Acceleration Sensors on a Fully Embedded Platform. J Vertens, F Fischer, C Heyde, F Hoeflinger, R Zhang, LM Reindl, ... icSPORTS, 15-23, 2015 | 22 | 2015 |
| Lane graph estimation for scene understanding in urban driving J Zürn, J Vertens, W Burgard IEEE Robotics and Automation Letters 6 (4), 8615-8622, 2021 | 19 | 2021 |
| From plants to landmarks: Time-invariant plant localization that uses deep pose regression in agricultural fields F Kraemer, A Schaefer, A Eitel, J Vertens, W Burgard arXiv preprint arXiv:1709.04751, 2017 | 18 | 2017 |
| A maximum likelihood approach to extract finite planes from 3-D laser scans A Schaefer, J Vertens, D Büscher, W Burgard 2019 International Conference on Robotics and Automation (ICRA), 72-78, 2019 | 12 | 2019 |
| Perspectives on deep multimodel robot learning W Burgard, A Valada, N Radwan, T Naseer, J Zhang, J Vertens, O Mees, ... Robotics Research: The 18th International Symposium ISRR, 17-24, 2020 | 10 | 2020 |
| Heatnet: Bridging the day-night domain gap in semantic segmentation with thermal images. In 2020 IEEE J Vertens, J Zürn, W Burgard RSJ International Conference on Intelligent Robots and Systems (IROS), 8461-8468, 0 | 5 | |
| Usegscene: Unsupervised learning of depth, optical flow and ego-motion with semantic guidance and coupled networks J Vertens, W Burgard arXiv preprint arXiv:2207.07469, 2022 | 2 | 2022 |
| Realistic real-time simulation of RGB and depth sensors for dynamic scenarios using augmented image based rendering J Vertens, W Burgard 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022 | 1 | 2022 |
| Improving Deep Dynamics Models for Autonomous Vehicles with Multimodal Latent Mapping of Surfaces J Vertens, N Dorka, T Welschehold, M Thompson, W Burgard 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2023 | | 2023 |
Wolfram BurgardProfessor of Computer Science, Technical University of NurembergVerified email at utn.de
