Research Publications

Artisense continually pushes for state-of-the-art approaches to computer vision and visual SLAM. Here is a list of publications developed by the researchers at Artisense and TUM (Technical University of Munich):

 

Datasets

  1. 4Seasons Dataset: This dataset covers multiple challenging perceptual conditions for autonomous driving. We provide globally consistent reference poses with centimeter accuracy obtained from the Artisense Visual Inertial Navigation System. [dataset website]

Research

  1. L. Koestler, N. Yang, N. Zeller and D. Cremers: TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo, In Conference on Robot Learning (CoRL), 2021. [arXiv]

  2. M. Gladkova, R. Wang, N. Zeller and D. Cremers: Tight Integration of Feature-based Relocalization in Monocular Direct Visual Odometry, In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2021. [arXiv]

  3. F. Wimbauer, N. Yang, L. von Stumberg, N. Zeller and D Cremers: MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. [arXiv]

  4. L. von Stumberg, P. Wenzel, N. Yang, D. Cremers: LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization, International Conference on 3D Vision (3DV), 2020. [arXiv]

  5. P. Wenzel, R. Wang, N. Yang, Q. Cheng, Q. Khan, L. von Stumberg, N. Zeller, D. Cremers: 4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving, German Conference on Pattern Recognition (GCPR), 2020. [arXiv]

  6. L. Koestler, N. Yang, R. Wang, D. Cremers: Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels, German Conference on Pattern Recognition (GCPR), 2020. [arXiv]

  7. N. Yang, L. von Stumberg, R. Wang, D. Cremers: D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [arXiv]

  8. R. Wang, N. Yang, J. Stückler, D. Cremers: DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation, IEEE International Conference on Robotics and Automation (ICRA), 2020. [arXiv]

  9. L. von Stumberg, P. Wenzel, Q. Khan, D. Cremers, GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization, IEEE Robotics and Automation Letters (RA-L) & IEEE International Conference on Robotics and Automation (ICRA), 2020. [arXiv]

  10. J. Du, R. Wang, D. Cremers: DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization, European Conference on Computer Vision (ECCV), 2020. [arXiv]

  11. D. Schubert, N. Demmel, L. von Stumberg, V. Usenko, D. Cremers: Rolling-Shutter Modelling for Visual-Inertial Odometry, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019. [arXiv]

  12. Q. Khan, P. Wenzel, D. Cremers, L. Leal-Taixé: Towards Generalizing Sensorimotor Control Across Weather Conditions, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019. [arXiv]

  13. E. Jung, N. Yang, D. Cremers: Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light, Conference on Robot Learning (CoRL), 2019. [arXiv]

  14. P. Wenzel, Q. Khan, D. Cremers, L. Leal-Taixé: Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs, Conference on Robot Learning (CoRL), 2018. [arXiv]

  15. N. Yang, R. Wang, J. Stückler, D. Cremers: Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry, European Conference on Computer Vision (ECCV), 2018. [arXiv]

  16. H. Matsuki, L. von Stumberg, V. Usenko, J. Stückler, D. Cremers: Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras, IEEE Robotics and Automation Letters (RA-L) & IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018. [arXiv]

  17. N. Yang, R. Wang, X. Gao, D. Cremers: Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect, IEEE Robotics and Automation Letters (RA-L) & IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018. [arXiv]

  18. P. Bergmann, R. Wang, D. Cremers: Online Photometric Calibration of Auto Exposure Video for Realtime Visual Odometry and SLAM, IEEE Robotics and Automation Letters (RA-L) & IEEE International Conference on Robotics and Automation (ICRA), 2018. [arXiv]

  19. X. Gao, R. Wang, N. Demmel, D. Cremers, LDSO: Direct Sparse Odometry with Loop Closure, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018. [arXiv]

  20. L. von Stumberg, V. Usenko, D. Cremers: Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization, International Conference on Robotics and Automation (ICRA), 2018. [arXiv]

  21. J. Engel, V. Koltun, D. Cremers: Direct Sparse Odometry, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2018. [arVix]

  22. R. Wang, M. Schwörer, D. Cremers: Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras, International Conference on Computer Vision (ICCV), 2017. [arXiv]