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ORCIDDaniel Cremers
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- affiliation: Technical University Munich, Germany
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2020 – today
- 2021
[i127]Mariia Gladkova, Rui Wang, Niclas Zeller, Daniel Cremers:
Tight Integration of Feature-Based Relocalization in Monocular Direct Visual Odometry. CoRR abs/2102.01191 (2021)- [i126]Philip Müller, Vladimir Golkov, Valentina Tomassini, Daniel Cremers:
Rotation-Equivariant Deep Learning for Diffusion MRI. CoRR abs/2102.06942 (2021) - [i125]Thomas Frerix, Dmitrii Kochkov, Jamie A. Smith, Daniel Cremers, Michael P. Brenner, Stephan Hoyer:
Variational Data Assimilation with a Learned Inverse Observation Operator. CoRR abs/2102.11192 (2021) - [i124]Mark Weber, Jun Xie, Maxwell D. Collins, Yukun Zhu, Paul Voigtlaender, Hartwig Adam, Bradley Green, Andreas Geiger, Bastian Leibe, Daniel Cremers, Aljosa Osep, Laura Leal-Taixé, Liang-Chieh Chen:
STEP: Segmenting and Tracking Every Pixel. CoRR abs/2102.11859 (2021) - 2020
- [j83]Emanuel Laude
, Peter Ochs, Daniel Cremers:
Bregman Proximal Mappings and Bregman-Moreau Envelopes Under Relative Prox-Regularity. J. Optim. Theory Appl. 184(3): 724-761 (2020) - [j82]Bjoern Haefner, Songyou Peng, Alok Verma, Yvain Quéau, Daniel Cremers:
Photometric Depth Super-Resolution. IEEE Trans. Pattern Anal. Mach. Intell. 42(10): 2453-2464 (2020) - [j81]Vladyslav Usenko, Nikolaus Demmel, David Schubert, Jörg Stückler, Daniel Cremers:
Visual-Inertial Mapping With Non-Linear Factor Recovery. IEEE Robotics Autom. Lett. 5(2): 422-429 (2020) - [j80]Lukas von Stumberg, Patrick Wenzel, Qadeer Khan, Daniel Cremers:
GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization. IEEE Robotics Autom. Lett. 5(2): 890-897 (2020) - [j79]Christiane Sommer, Yumin Sun, Leonidas J. Guibas, Daniel Cremers, Tolga Birdal:
From Planes to Corners: Multi-Purpose Primitive Detection in Unorganized 3D Point Clouds. IEEE Robotics Autom. Lett. 5(2): 1764-1771 (2020) - [c283]Nikolaus Demmel, Maolin Gao, Emanuel Laude, Tao Wu, Daniel Cremers:
Distributed Photometric Bundle Adjustment. 3DV 2020: 140-149 - [c282]Benjamin Holzschuh, Zorah Lähner, Daniel Cremers:
Simulated Annealing for 3D Shape Correspondence. 3DV 2020: 252-260 - [c281]Mehmet Aygün, Zorah Lähner, Daniel Cremers:
Unsupervised Dense Shape Correspondence using Heat Kernels. 3DV 2020: 573-582 - [c280]Vladimir Golkov, Marcin J. Skwark, Atanas Mirchev, Georgi Dikov, Alexander R. Geanes, Jeffrey L. Mendenhall, Jens Meiler, Daniel Cremers:
3D Deep Learning for Biological Function Prediction from Physical Fields. 3DV 2020: 928-937 - [c279]Lukas von Stumberg, Patrick Wenzel, Nan Yang, Daniel Cremers:
LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization. 3DV 2020: 968-977 - [c278]Zhenzhang Ye, Thomas Möllenhoff, Tao Wu, Daniel Cremers:
Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning. AISTATS 2020: 657-668 - [c277]Nan Yang, Lukas von Stumberg, Rui Wang, Daniel Cremers:
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry. CVPR 2020: 1278-1289 - [c276]Thomas Frerix, Matthias Nießner, Daniel Cremers:
Homogeneous Linear Inequality Constraints for Neural Network Activations. CVPR Workshops 2020: 3229-3234 - [c275]Sebastian Weiss, Robert Maier, Daniel Cremers, Rüdiger Westermann, Nils Thuerey:
Correspondence-Free Material Reconstruction using Sparse Surface Constraints. CVPR 2020: 4685-4694 - [c274]Christiane Sommer, Vladyslav Usenko, David Schubert, Nikolaus Demmel, Daniel Cremers:
Efficient Derivative Computation for Cumulative B-Splines on Lie Groups. CVPR 2020: 11145-11153 - [c273]Marvin Eisenberger, Zorah Lähner, Daniel Cremers:
Smooth Shells: Multi-Scale Shape Registration With Functional Maps. CVPR 2020: 12262-12271 - [c272]Marvin Eisenberger, Daniel Cremers:
Hamiltonian Dynamics for Real-World Shape Interpolation. ECCV (4) 2020: 179-196 - [c271]Juan Du, Rui Wang, Daniel Cremers:
DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization. ECCV (4) 2020: 744-762 - [c270]Christiane Sommer, Yumin Sun, Erik Bylow, Daniel Cremers:
PrimiTect: Fast Continuous Hough Voting for Primitive Detection. ICRA 2020: 8404-8410 - [c269]Rui Wang, Nan Yang, Jörg Stückler, Daniel Cremers:
DirectShape: Direct Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation. ICRA 2020: 11067-11073 - [c268]Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Daniel Cremers:
Deep Shells: Unsupervised Shape Correspondence with Optimal Transport. NeurIPS 2020 - [c267]Jiayu Liu, Ioannis Chiotellis, Rudolph Triebel, Daniel Cremers:
Effective Version Space Reduction for Convolutional Neural Networks. ECML/PKDD (2) 2020: 85-100 - [c266]Lu Sang, Bjoern Haefner, Daniel Cremers:
Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach. WACV 2020: 1-10 - [p7]Vladyslav Usenko, Lukas von Stumberg, Jörg Stückler, Daniel Cremers:
TUM Flyers: Vision - Based MAV Navigation for Systematic Inspection of Structures. EuRoC 2020: 189-209 - [i123]Christiane Sommer, Yumin Sun, Leonidas J. Guibas, Daniel Cremers, Tolga Birdal:
From Planes to Corners: Multi-Purpose Primitive Detection in Unorganized 3D Point Clouds. CoRR abs/2001.07360 (2020) - [i122]Hamid Rezatofighi, Roman Kaskman, Farbod T. Motlagh, Qinfeng Shi, Anton Milan, Daniel Cremers, Laura Leal-Taixé, Ian D. Reid:
Learn to Predict Sets Using Feed-Forward Neural Networks. CoRR abs/2001.11845 (2020) - [i121]Zhenzhang Ye, Thomas Möllenhoff, Tao Wu, Daniel Cremers:
Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning. CoRR abs/2002.12236 (2020) - [i120]Nan Yang, Lukas von Stumberg, Rui Wang, Daniel Cremers:
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry. CoRR abs/2003.01060 (2020) - [i119]Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian D. Reid, Stefan Roth, Konrad Schindler, Laura Leal-Taixé:
MOT20: A benchmark for multi object tracking in crowded scenes. CoRR abs/2003.09003 (2020) - [i118]Marvin Eisenberger, Daniel Cremers:
Hamiltonian Dynamics for Real-World Shape Interpolation. CoRR abs/2004.05199 (2020) - [i117]Christiane Sommer, Yumin Sun, Erik Bylow, Daniel Cremers:
PrimiTect: Fast Continuous Hough Voting for Primitive Detection. CoRR abs/2005.07457 (2020) - [i116]Jiayu Liu, Ioannis Chiotellis, Rudolph Triebel, Daniel Cremers:
Effective Version Space Reduction for Convolutional Neural Networks. CoRR abs/2006.12456 (2020) - [i115]Yuesong Shen, Daniel Cremers:
Deriving Neural Network Design and Learning from the Probabilistic Framework of Chain Graphs. CoRR abs/2006.16856 (2020) - [i114]Vladimir Golkov, Alexander Becker, Daniel T. Plop, Daniel Cuturilo, Neda Davoudi, Jeffrey L. Mendenhall, Rocco Moretti, Jens Meiler, Daniel Cremers:
Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions. CoRR abs/2007.07029 (2020) - [i113]Juan Du, Rui Wang, Daniel Cremers:
DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization. CoRR abs/2007.09217 (2020) - [i112]Patrick Wenzel, Rui Wang, Nan Yang, Qing Cheng, Qadeer Khan, Lukas von Stumberg, Niclas Zeller, Daniel Cremers:
4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving. CoRR abs/2009.06364 (2020) - [i111]Lukas Koestler, Nan Yang, Rui Wang, Daniel Cremers:
Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels. CoRR abs/2010.03506 (2020) - [i110]Lukas von Stumberg, Patrick Wenzel, Nan Yang, Daniel Cremers:
LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization. CoRR abs/2010.06323 (2020) - [i109]Patrick Dendorfer, Aljosa Osep, Anton Milan, Konrad Schindler, Daniel Cremers, Ian D. Reid, Stefan Roth, Laura Leal-Taixé:
MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking. CoRR abs/2010.07548 (2020) - [i108]Mehmet Aygün, Zorah Lähner, Daniel Cremers:
Unsupervised Dense Shape Correspondence using Heat Kernels. CoRR abs/2010.12682 (2020) - [i107]Giorgio Fabbro, Vladimir Golkov, Thomas Kemp, Daniel Cremers:
Speech Synthesis and Control Using Differentiable DSP. CoRR abs/2010.15084 (2020) - [i106]Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Daniel Cremers:
Deep Shells: Unsupervised Shape Correspondence with Optimal Transport. CoRR abs/2010.15261 (2020) - [i105]Felix Wimbauer, Nan Yang, Lukas von Stumberg, Niclas Zeller, Daniel Cremers:
MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera. CoRR abs/2011.11814 (2020) - [i104]Yan Xia, Yusheng Xu, Shuang Li, Rui Wang, Juan Du, Daniel Cremers, Uwe Stilla:
SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition. CoRR abs/2011.12430 (2020) - [i103]Or Litany, Emanuele Rodolà, Alex M. Bronstein, Michael M. Bronstein, Daniel Cremers:
Non-Rigid Puzzles. CoRR abs/2011.13076 (2020) - [i102]Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, Christian Theobalt:
i3DMM: Deep Implicit 3D Morphable Model of Human Heads. CoRR abs/2011.14143 (2020) - [i101]Maolin Gao, Zorah Lähner, Johan Thunberg, Daniel Cremers, Florian Bernard:
Isometric Multi-Shape Matching. CoRR abs/2012.02689 (2020) - [i100]Ioannis Chiotellis, Daniel Cremers:
Neural Online Graph Exploration. CoRR abs/2012.03345 (2020) - [i99]Christian Tomani, Sebastian Gruber, Muhammed Ebrar Erdem, Daniel Cremers, Florian Buettner:
Post-hoc Uncertainty Calibration for Domain Drift Scenarios. CoRR abs/2012.10988 (2020)
2010 – 2019
- 2019
- [j78]Marvin Eisenberger, Zorah Lähner, Daniel Cremers:
Divergence-Free Shape Correspondence by Deformation. Comput. Graph. Forum 38(5): 1-12 (2019) - [j77]Kevis-Kokitsi Maninis, Sergi Caelles, Yuhua Chen, Jordi Pont-Tuset, Laura Leal-Taixé, Daniel Cremers, Luc Van Gool:
Video Object Segmentation without Temporal Information. IEEE Trans. Pattern Anal. Mach. Intell. 41(6): 1515-1530 (2019) - [j76]Henning Tjaden, Ulrich Schwanecke, Elmar Schömer, Daniel Cremers:
A Region-Based Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking. IEEE Trans. Pattern Anal. Mach. Intell. 41(8): 1797-1812 (2019) - [c265]Bjoern Haefner, Yvain Quéau, Daniel Cremers:
Photometric Segmentation: Simultaneous Photometric Stereo and Masking. 3DV 2019: 222-229 - [c264]Roberto M. Dyke, C. Stride, Yu-Kun Lai, Paul L. Rosin, Mathieu Aubry, Amit Boyarski, Alexander M. Bronstein, Michael M. Bronstein, Daniel Cremers, Matthew Fisher, Thibault Groueix, Daoliang Guo, Vladimir G. Kim, Ron Kimmel, Zorah Lähner, Kun Li, Or Litany, Tal Remez, Emanuele Rodolà, Bryan C. Russell, Yusuf Sahillioglu, Ron Slossberg, Gary K. L. Tam, Matthias Vestner, Z. Wu, Jingyu Yang:
Shape Correspondence with Isometric and Non-Isometric Deformations. 3DOR@Eurographics 2019: 111-119 - [c263]Emanuel Laude, Tao Wu, Daniel Cremers:
Optimization of Inf-Convolution Regularized Nonconvex Composite Problems. AISTATS 2019: 547-556 - [c262]Eunah Jung, Nan Yang, Daniel Cremers:
Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light. CoRL 2019: 651-660 - [c261]Thomas Möllenhoff, Daniel Cremers:
Lifting Vectorial Variational Problems: A Natural Formulation Based on Geometric Measure Theory and Discrete Exterior Calculus. CVPR 2019: 11117-11126 - [c260]Michael Möller, Thomas Möllenhoff, Daniel Cremers:
Controlling Neural Networks via Energy Dissipation. ICCV 2019: 3255-3264 - [c259]Zhenzhang Ye, Bjoern Haefner, Maolin Gao, Tao Wu, Yvain Quéau, Daniel Cremers:
Variational Uncalibrated Photometric Stereo Under General Lighting. ICCV 2019: 8538-8547 - [c258]Thomas Möllenhoff, Daniel Cremers:
Flat Metric Minimization with Applications in Generative Modeling. ICML 2019: 4626-4635 - [c257]David Schubert, Nikolaus Demmel, Lukas von Stumberg, Vladyslav Usenko, Daniel Cremers:
Rolling-Shutter Modelling for Direct Visual-Inertial Odometry. IROS 2019: 2462-2469 - [c256]Qadeer Khan, Patrick Wenzel, Daniel Cremers, Laura Leal-Taixé:
Towards Generalizing Sensorimotor Control Across Weather Conditions. IROS 2019: 4497-4503 - [i98]Yuesong Shen, Tao Wu, Csaba Domokos, Daniel Cremers:
Probabilistic Discriminative Learning with Layered Graphical Models. CoRR abs/1902.00057 (2019) - [i97]Thomas Frerix, Matthias Nießner, Daniel Cremers:
Linear Inequality Constraints for Neural Network Activations. CoRR abs/1902.01785 (2019) - [i96]Emanuel Laude, Tao Wu, Daniel Cremers:
Optimization of Inf-Convolution Regularized Nonconvex Composite Problems. CoRR abs/1903.11690 (2019) - [i95]Michael Möller, Thomas Möllenhoff, Daniel Cremers:
Controlling Neural Networks via Energy Dissipation. CoRR abs/1904.03081 (2019) - [i94]Bjoern Haefner, Zhenzhang Ye, Maolin Gao, Tao Wu, Yvain Quéau, Daniel Cremers:
Variational Uncalibrated Photometric Stereo under General Lighting. CoRR abs/1904.03942 (2019) - [i93]Vladyslav Usenko, Nikolaus Demmel, David Schubert, Jörg Stückler, Daniel Cremers:
Visual-Inertial Mapping with Non-Linear Factor Recovery. CoRR abs/1904.06504 (2019) - [i92]Rui Wang, Nan Yang, Jörg Stückler, Daniel Cremers:
DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation. CoRR abs/1904.10097 (2019) - [i91]Lukas von Stumberg, Patrick Wenzel, Qadeer Khan, Daniel Cremers:
GN-Net: The Gauss-Newton Loss for Deep Direct SLAM. CoRR abs/1904.11932 (2019) - [i90]Thomas Möllenhoff, Daniel Cremers:
Lifting Vectorial Variational Problems: A Natural Formulation based on Geometric Measure Theory and Discrete Exterior Calculus. CoRR abs/1905.00851 (2019) - [i89]Jan Schuchardt, Vladimir Golkov, Daniel Cremers:
Learning to Evolve. CoRR abs/1905.03389 (2019) - [i88]Thomas Möllenhoff, Daniel Cremers:
Flat Metric Minimization with Applications in Generative Modeling. CoRR abs/1905.04730 (2019) - [i87]Marvin Eisenberger, Zorah Lähner, Daniel Cremers:
Smooth Shells: Multi-Scale Shape Registration with Functional Maps. CoRR abs/1905.12512 (2019) - [i86]Patrick Dendorfer, Seyed Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian D. Reid, Stefan Roth, Konrad Schindler, Laura Leal-Taixé:
CVPR19 Tracking and Detection Challenge: How crowded can it get? CoRR abs/1906.04567 (2019) - [i85]Qadeer Khan, Patrick Wenzel, Daniel Cremers, Laura Leal-Taixé:
Towards Generalizing Sensorimotor Control Across Weather Conditions. CoRR abs/1907.11025 (2019) - [i84]Thomas Vogt, Evgeny Strekalovskiy, Daniel Cremers, Jan Lellmann:
Lifting methods for manifold-valued variational problems. CoRR abs/1908.03776 (2019) - [i83]Sebastian Weiss, Robert Maier, Rüdiger Westermann, Daniel Cremers, Nils Thuerey:
Sparse Surface Constraints for Combining Physics-based Elasticity Simulation and Correspondence-Free Object Reconstruction. CoRR abs/1910.01812 (2019) - [i82]Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude, Daniel Cremers, Peter Ochs:
Bregman Proximal Framework for Deep Linear Neural Networks. CoRR abs/1910.03638 (2019) - [i81]Eunah Jung, Nan Yang, Daniel Cremers:
Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light. CoRR abs/1910.06632 (2019) - [i80]Luca Della Libera, Vladimir Golkov, Yue Zhu, Arman Mielke, Daniel Cremers:
Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods. CoRR abs/1910.14594 (2019) - [i79]David Schubert, Nikolaus Demmel, Lukas von Stumberg, Vladyslav Usenko, Daniel Cremers:
Rolling-Shutter Modelling for Direct Visual-Inertial Odometry. CoRR abs/1911.01015 (2019) - [i78]Mohammed Brahimi, Yvain Quéau, Bjoern Haefner, Daniel Cremers:
On the well-posedness of uncalibrated photometric stereo under general lighting. CoRR abs/1911.07268 (2019) - [i77]Christiane Sommer, Vladyslav Usenko, David Schubert, Nikolaus Demmel, Daniel Cremers:
Efficient Derivative Computation for Cumulative B-Splines on Lie Groups. CoRR abs/1911.08860 (2019) - [i76]Pierre Bréchet, Tao Wu, Thomas Möllenhoff, Daniel Cremers:
Informative GANs via Structured Regularization of Optimal Transport. CoRR abs/1912.02160 (2019) - [i75]Lu Sang, Bjoern Haefner, Daniel Cremers:
Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach. CoRR abs/1912.06501 (2019) - 2018
- [j75]Nikolaus Mayer, Eddy Ilg, Philipp Fischer, Caner Hazirbas, Daniel Cremers, Alexey Dosovitskiy, Thomas Brox:
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation? Int. J. Comput. Vis. 126(9): 942-960 (2018) - [j74]Yvain Quéau, Bastien Durix, Tao Wu, Daniel Cremers, François Lauze, Jean-Denis Durou:
LED-Based Photometric Stereo: Modeling, Calibration and Numerical Solution. J. Math. Imaging Vis. 60(3): 313-340 (2018) - [j73]Jean Mélou, Yvain Quéau, Jean-Denis Durou, Fabien Castan, Daniel Cremers:
Variational Reflectance Estimation from Multi-view Images. J. Math. Imaging Vis. 60(9): 1527-1546 (2018) - [j72]Björn Bringmann, Daniel Cremers, Felix Krahmer, Michael Möller:
The homotopy method revisited: Computing solution paths of ℓ1-regularized problems. Math. Comput. 87(313): 2343-2364 (2018) - [j71]Jakob Engel, Vladlen Koltun, Daniel Cremers:
Direct Sparse Odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3): 611-625 (2018) - [j70]Paul Bergmann, Rui Wang, Daniel Cremers:
Online Photometric Calibration of Auto Exposure Video for Realtime Visual Odometry and SLAM. IEEE Robotics Autom. Lett. 3(2): 627-634 (2018) - [j69]Nan Yang, Rui Wang, Xiang Gao, Daniel Cremers:
Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias, and Rolling Shutter Effect. IEEE Robotics Autom. Lett. 3(4): 2878-2885 (2018) - [j68]Hidenobu Matsuki, Lukas von Stumberg, Vladyslav Usenko, Jörg Stückler, Daniel Cremers:
Omnidirectional DSO: Direct Sparse Odometry With Fisheye Cameras. IEEE Robotics Autom. Lett. 3(4): 3693-3700 (2018) - [c255]Christiane Sommer, Daniel Cremers:
Joint Representation of Primitive and Non-primitive Objects for 3D Vision. 3DV 2018: 160-169 - [c254]Virginia Estellers, Frank R. Schmidt, Daniel Cremers:
Robust Fitting of Subdivision Surfaces for Smooth Shape Analysis. 3DV 2018: 277-285 - [c253]Vladyslav Usenko, Nikolaus Demmel, Daniel Cremers:
The Double Sphere Camera Model. 3DV 2018: 552-560 - [c252]Caner Hazirbas, Sebastian Georg Soyer, Maximilian Christian Staab, Laura Leal-Taixé, Daniel Cremers:
Deep Depth from Focus. ACCV (3) 2018: 525-541 - [c251]Thomas Möllenhoff, Zhenzhang Ye, Tao Wu, Daniel Cremers:
Combinatorial Preconditioners for Proximal Algorithms on Graphs. AISTATS 2018: 38-47 - [c250]Emanuel Laude, Tao Wu, Daniel Cremers:
A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization. AISTATS 2018: 491-499 - [c249]Patrick Wenzel, Qadeer Khan, Daniel Cremers, Laura Leal-Taixé:
Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs. CoRL 2018: 253-269 - [c248]Bjoern Haefner, Yvain Quéau, Thomas Möllenhoff, Daniel Cremers:
Fight Ill-Posedness With Ill-Posedness: Single-Shot Variational Depth Super-Resolution From Shading. CVPR 2018: 164-174 - [c247]Roberto Henschel, Laura Leal-Taixé, Daniel Cremers, Bodo Rosenhahn:
Fusion of Head and Full-Body Detectors for Multi-Object Tracking. CVPR Workshops 2018: 1428-1437 - [c246]Emanuel Laude, Jan-Hendrik Lange, Jonas Schüpfer, Csaba Domokos, Laura Leal-Taixé, Frank R. Schmidt, Bjoern Andres, Daniel Cremers:
Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs. CVPR 2018: 1614-1624 - [c245]Philip Häusser, Johannes Plapp, Vladimir Golkov, Elie Aljalbout, Daniel Cremers:
Associative Deep Clustering: Training a Classification Network with No Labels. GCPR 2018: 18-32 - [c244]Csaba Domokos, Frank R. Schmidt, Daniel Cremers:
MRF Optimization with Separable Convex Prior on Partially Ordered Labels. ECCV (8) 2018: 341-356 - [c243]Zorah Lähner, Daniel Cremers, Tony Tung:
DeepWrinkles: Accurate and Realistic Clothing Modeling. ECCV (4) 2018: 698-715 - [c242]David Schubert, Nikolaus Demmel, Vladyslav Usenko, Jörg Stückler, Daniel Cremers:
Direct Sparse Odometry with Rolling Shutter. ECCV (8) 2018: 699-714 - [c241]Nan Yang, Rui Wang, Jörg Stückler, Daniel Cremers:
Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry. ECCV (8) 2018: 835-852 - [c240]Thomas Frerix, Thomas Möllenhoff, Michael Möller, Daniel Cremers:
Proximal Backpropagation. ICLR (Poster) 2018 - [c239]Raluca Scona, Mariano Jaimez, Yvan R. Petillot, Maurice F. Fallon, Daniel Cremers:
StaticFusion: Background Reconstruction for Dense RGB-D SLAM in Dynamic Environments. ICRA 2018: 1-9 - [c238]Lukas von Stumberg, Vladyslav Usenko, Daniel Cremers:
Direct Sparse Visual-Inertial Odometry Using Dynamic Marginalization. ICRA 2018: 2510-2517 - [c237]David Schubert, Thore Goll, Nikolaus Demmel, Vladyslav Usenko, Jörg Stückler, Daniel Cremers:
The TUM VI Benchmark for Evaluating Visual-Inertial Odometry. IROS 2018: 1680-1687 - [c236]Xiang Gao, Rui Wang, Nikolaus Demmel, Daniel Cremers:
LDSO: Direct Sparse Odometry with Loop Closure. IROS 2018: 2198-2204 - [c235]Ioannis Chiotellis, Franziska Zimmermann, Daniel Cremers, Rudolph Triebel:
Incremental Semi-Supervised Learning from Streams for Object Classification. IROS 2018: 5743-5749 - [i74]Thomas Möllenhoff, Zhenzhang Ye, Tao Wu, Daniel Cremers:
Combinatorial Preconditioners for Proximal Algorithms on Graphs. CoRR abs/1801.05413 (2018) - [i73]Nikolaus Mayer, Eddy Ilg, Philipp Fischer, Caner Hazirbas, Daniel Cremers, Alexey Dosovitskiy, Thomas Brox:
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation? CoRR abs/1801.06397 (2018) - [i72]Elie Aljalbout, Vladimir Golkov, Yawar Siddiqui, Daniel Cremers:
Clustering with Deep Learning: Taxonomy and New Methods. CoRR abs/1801.07648 (2018) - [i71]Lukas von Stumberg, Vladyslav Usenko, Daniel Cremers:
Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization. CoRR abs/1804.05625 (2018) - [i70]David Schubert, Thore Goll, Nikolaus Demmel, Vladyslav Usenko, Jörg Stückler, Daniel Cremers:
The TUM VI Benchmark for Evaluating Visual-Inertial Odometry. CoRR abs/1804.06120 (2018) - [i69]Seyed Hamid Rezatofighi, Roman Kaskman, Farbod T. Motlagh, Qinfeng Shi, Daniel Cremers, Laura Leal-Taixé, Ian D. Reid:
Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks. CoRR abs/1805.00613 (2018) - [i68]Aleksei Vasilev, Vladimir Golkov, Ilona Lipp, Eleonora Sgarlata, Valentina Tomassini, Derek K. Jones, Daniel Cremers:
q-Space Novelty Detection with Variational Autoencoders. CoRR abs/1806.02997 (2018) - [i67]Marvin Eisenberger, Zorah Lähner, Daniel Cremers:
Divergence-Free Shape Interpolation and Correspondence. CoRR abs/1806.10417 (2018) - [i66]Patrick Wenzel, Qadeer Khan, Daniel Cremers, Laura Leal-Taixé:
Modular Vehicle Control for Transferring Semantic Information to Unseen Weather Conditions using GANs. CoRR abs/1807.01001 (2018) - [i65]Henning Tjaden, Ulrich Schwanecke, Elmar Schömer, Daniel Cremers:
A Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking.