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% generated by bibtexbrowser % % Encoding: UTF-8 @article{yang18challenges, author = {N. Yang and R. Wang and X. Gao and D. Cremers}, title = {Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect}, journal = { In IEEE Robotics and Automation Letters (RA-L) & Int. Conference on Intelligent Robots and Systems (IROS)}, volume = {3}, issue = {4}, pages = {2878--2885}, year = {2018}, month = {Oct}, doi = {10.1109/LRA.2018.2846813}, titleurl = {yang18challenges.pdf}, keywords = {Brightness;Calibration;Cameras;Feature extraction;Optimization;Robustness;Simultaneous localization and mapping;Localization;SLAM;performance evaluation and benchmarking;vslam}, } @inproceedings{yang2018dvso, author = {N. Yang and R. Wang and J. Stueckler and D. Cremers}, title = {Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2018}, month = {September}, award = {Oral Presentation}, keywords = {dso, dvso, deep learning, monocular depth estimation, semi-supervised learning, slam, visual odometry, vslam}, } @inproceedings{wang2020directshape, author = {R. Wang and N. Yang and J. Stueckler and D. Cremers}, title = {DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation}, booktitle = {Proc. of the IEEE International Conference on Robotics and Automation (ICRA)}, year = {2020}, keywords = {stereo, 3D reconstruction, semantic SLAM, 3D object detection, scene understanding, direct shape}, } @inproceedings{jung2019corl, author = {E. Jung and N. Yang and D. Cremers}, booktitle = {Conference on Robot Learning (CoRL)}, title = {{Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light}}, award = {Full Oral Presentation}, year = {2019}, } @inproceedings{yang20d3vo, author = {N. Yang and L. von Stumberg and R. Wang and D. Cremers}, title = {D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2020}, eprint = {2003.01060}, eprinttype = {arXiv}, eprintclass = {cs.CV}, award = {Oral Presentation}, keywords = {dso,dvso, deep learning, monocular depth estimation, semi-supervised learning, slam, visual odometry,d3vo, vslam}, } @inproceedings{koestler2020learning, author = {L. Koestler and N. Yang and R. Wang and D. Cremers}, title = {Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels}, booktitle = {Proceedings of the German Conference on Pattern Recognition (GCPR)}, year = {2020}, } @inproceedings{wenzel2020fourseasons, title = {{4Seasons}: A Cross-Season Dataset for Multi-Weather {SLAM} in Autonomous Driving}, author = {P. Wenzel and R. Wang and N. Yang and Q. Cheng and Q. Khan and L. von Stumberg and N. Zeller and D. Cremers}, booktitle = {Proceedings of the German Conference on Pattern Recognition ({GCPR})}, year = {2020}, keywords = {vslam,4seasons, deep learning}, } @inproceedings{lm-reloc-2020, author = {L. von Stumberg and P. Wenzel and N. Yang and D. Cremers}, title = {LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization}, booktitle = {International Conference on 3D Vision (3DV)}, year = {2020}, keywords = {lm-reloc, slam, structure-from-motion, direct method, mapping, vslam, deep learning}, } @inproceedings{wimbauer2020monorec, title = {MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera}, author = {F. Wimbauer and N. Yang and L. von Stumberg and N. Zeller and D Cremers}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2021}, eprint = {2011.11814}, eprinttype = {arXiv}, eprintclass = {cs.CV}, keywords = {monorec, dvso, d3vo, mvs, deep learning, SLAM, vslam, reconstruction}, } @inproceedings{koestler2021tandem, author = {L Koestler and N Yang and N Zeller and D Cremers}, title = {TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo}, booktitle = {Conference on Robot Learning (CoRL)}, year = {2021}, eprint = {2111.07418}, eprinttype = {arXiv}, award = {3DV'21 Best Demo Award}, keywords = {tandem, odometry, VO, SLAM, vslam, dense reconstruction, mvs}, } @article{yenamandra2022fire, url = {https://arxiv.org/abs/2203.16284}, author = {T Yenamandra and A Tewari and N Yang and F Bernard and C Theobalt and D Cremers}, title = {FIRe🔥: Fast Inverse Rendering using Directional and Signed Distance Functions}, publisher = {arXiv}, year = {2022}, } @article{wenzel2022seasons, title = {4Seasons: Benchmarking Visual SLAM and Long-Term Localization for Autonomous Driving in Challenging Conditions}, author = {P Wenzel and N Yang and R Wang and N Zeller and D Cremers}, journal = {arXiv preprint arXiv:2301.01147}, year = {2022}, eprint = {2301.01147}, eprinttype = {arXiv}, eprintclass = {cs.CV}, } @inproceedings{wimbauer2023behind, title = {Behind the Scenes: Density Fields for Single View Reconstruction}, author = {F Wimbauer and N Yang and C Rupprecht and D Cremers}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2023}, eprinttype = {arXiv}, eprint = {2301.07668}, eprintclass = {cs.CV}, keywords = {depth prediction, volumetric, nerf, mvs, deep learning, SLAM, vslam, reconstruction}, titleurl = {wimbauer2023behind.png}, }