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Semantic ScholarDaniel Cremers
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- affiliation: Technical University Munich, Germany
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2010 – today
- 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? International Journal of Computer Vision 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. Journal of Mathematical Imaging and Vision 60(3): 313-340 (2018) - [j73]Jean Mélou, Yvain Quéau, Jean-Denis Durou, Fabien Castan, Daniel Cremers:
Variational Reflectance Estimation from Multi-view Images. Journal of Mathematical Imaging and Vision 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 and Automation Letters 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 and Automation Letters 3(4): 2878-2885 (2018) - [j68]Hidenobu Matsuki, Lukas von Stumberg, Vladyslav C. Usenko, Jörg Stückler, Daniel Cremers:
Omnidirectional DSO: Direct Sparse Odometry With Fisheye Cameras. IEEE Robotics and Automation Letters 3(4): 3693-3700 (2018) - [c253]Christiane Sommer, Daniel Cremers:
Joint Representation of Primitive and Non-primitive Objects for 3D Vision. 3DV 2018: 160-169 - [c252]Virginia Estellers, Frank R. Schmidt, Daniel Cremers:
Robust Fitting of Subdivision Surfaces for Smooth Shape Analysis. 3DV 2018: 277-285 - [c251]Vladyslav C. Usenko, Nikolaus Demmel, Daniel Cremers:
The Double Sphere Camera Model. 3DV 2018: 552-560 - [c250]Thomas Möllenhoff, Zhenzhang Ye, Tao Wu, Daniel Cremers:
Combinatorial Preconditioners for Proximal Algorithms on Graphs. AISTATS 2018: 38-47 - [c249]Emanuel Laude, Tao Wu, Daniel Cremers:
A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization. AISTATS 2018: 491-499 - [c248]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 - [c247]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 - [c246]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 - [c245]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 - [c244]Philip Häusser, Johannes Plapp, Vladimir Golkov, Elie Aljalbout, Daniel Cremers:
Associative Deep Clustering: Training a Classification Network with No Labels. GCPR 2018: 18-32 - [c243]Csaba Domokos, Frank R. Schmidt, Daniel Cremers:
MRF Optimization with Separable Convex Prior on Partially Ordered Labels. ECCV (8) 2018: 341-356 - [c242]Zorah Lähner, Daniel Cremers, Tony Tung:
DeepWrinkles: Accurate and Realistic Clothing Modeling. ECCV (4) 2018: 698-715 - [c241]David Schubert, Nikolaus Demmel, Vladyslav C. Usenko, Jörg Stückler, Daniel Cremers:
Direct Sparse Odometry with Rolling Shutter. ECCV (8) 2018: 699-714 - [c240]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 - [c239]Raluca Scona, Mariano Jaimez, Yvan R. Petillot, Maurice Fallon, Daniel Cremers:
StaticFusion: Background Reconstruction for Dense RGB-D SLAM in Dynamic Environments. ICRA 2018: 1-9 - [c238]Lukas von Stumberg, Vladyslav C. Usenko, Daniel Cremers:
Direct Sparse Visual-Inertial Odometry Using Dynamic Marginalization. ICRA 2018: 2510-2517 - [c237]David Schubert, Thore Goll, Nikolaus Demmel, Vladyslav C. 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 C. Usenko, Daniel Cremers:
Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization. CoRR abs/1804.05625 (2018) - [i70]David Schubert, Thore Goll, Nikolaus Demmel, Vladyslav C. Usenko, Jörg Stückler, Daniel Cremers:
The TUM VI Benchmark for Evaluating Visual-Inertial Odometry. CoRR abs/1804.06120 (2018) - [i69]S. 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. CoRR abs/1807.02087 (2018) - [i64]Nan Yang, Rui Wang, Jörg Stückler, Daniel Cremers:
Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry. CoRR abs/1807.02570 (2018) - [i63]Vladyslav C. Usenko, Nikolaus Demmel, Daniel Cremers:
The Double Sphere Camera Model. CoRR abs/1807.08957 (2018) - [i62]David Schubert, Nikolaus Demmel, Vladyslav C. Usenko, Jörg Stückler, Daniel Cremers:
Direct Sparse Odometry with Rolling Shutter. CoRR abs/1808.00558 (2018) - [i61]Xiang Gao, Rui Wang, Nikolaus Demmel, Daniel Cremers:
LDSO: Direct Sparse Odometry with Loop Closure. CoRR abs/1808.01111 (2018) - [i60]Lingni Ma, Jörg Stückler, Tao Wu, Daniel Cremers:
Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform. CoRR abs/1808.01834 (2018) - [i59]Hidenobu Matsuki, Lukas von Stumberg, Vladyslav C. Usenko, Jörg Stückler, Daniel Cremers:
Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras. CoRR abs/1808.02775 (2018) - [i58]Zorah Lähner, Daniel Cremers, Tony Tung:
DeepWrinkles: Accurate and Realistic Clothing Modeling. CoRR abs/1808.03417 (2018) - [i57]Bjoern Haefner, Songyou Peng, Alok Verma, Yvain Quéau, Daniel Cremers:
Photometric Depth Super-Resolution. CoRR abs/1809.10097 (2018) - 2017
- [j67]Luca Cosmo, Emanuele Rodolà, Andrea Albarelli, Facundo Mémoli, Daniel Cremers:
Consistent Partial Matching of Shape Collections via Sparse Modeling. Comput. Graph. Forum 36(1): 209-221 (2017) - [j66]Emanuele Rodolà, Luca Cosmo, Michael M. Bronstein, Andrea Torsello, Daniel Cremers:
Partial Functional Correspondence. Comput. Graph. Forum 36(1): 222-236 (2017) - [j65]Emanuele Rodolà, Michael Möller, Daniel Cremers:
Regularized Pointwise Map Recovery from Functional Correspondence. Comput. Graph. Forum 36(8): 700-711 (2017) - [j64]Daniel Cremers:
Computer Vision für 3-D-Rekonstruktion - Vom Nischenthema zum Mainstream. Informatik Spektrum 40(2): 205-209 (2017) - [j63]Youngwook Kee, Yegang Lee, Mohamed Souiai, Daniel Cremers, Junmo Kim:
Sequential Convex Programming for Computing Information-Theoretic Minimal Partitions: Nonconvex Nonsmooth Optimization. SIAM J. Imaging Sciences 10(4): 1845-1877 (2017) - [j62]Georg Kuschk, Pablo d'Angelo, David Gaudrie, Peter Reinartz, Daniel Cremers:
Spatially Regularized Fusion of Multiresolution Digital Surface Models. IEEE Trans. Geoscience and Remote Sensing 55(3): 1477-1488 (2017) - [c234]Matthias Vestner, Zorah Lähner, Amit Boyarski, Or Litany, Ron Slossberg, Tal Remez, Emanuele Rodolà, Alexander M. Bronstein, Michael M. Bronstein, Ron Kimmel, Daniel Cremers:
Efficient Deformable Shape Correspondence via Kernel Matching. 3DV 2017: 517-526 - [c233]Robert Maier, Raphael Schaller, Daniel Cremers:
Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction. BMVC 2017 - [c232]Yvain Quéau, Tao Wu, François Lauze, Jean-Denis Durou, Daniel Cremers:
A Non-convex Variational Approach to Photometric Stereo under Inaccurate Lighting. CVPR 2017: 350-359 - [c231]Philip Häusser, Alexander Mordvintsev, Daniel Cremers:
Learning by Association - A Versatile Semi-Supervised Training Method for Neural Networks. CVPR 2017: 626-635 - [c230]Florian Bernard, Frank R. Schmidt, Johan Thunberg, Daniel Cremers:
A Combinatorial Solution to Non-Rigid 3D Shape-to-Image Matching. CVPR 2017: 1436-1445 - [c229]Mariano Jaimez, Thomas J. Cashman, Andrew W. Fitzgibbon, Javier González Jiménez, Daniel Cremers:
An Efficient Background Term for 3D Reconstruction and Tracking with Smooth Surface Models. CVPR 2017: 2575-2583 - [c228]Sergi Caelles, Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Laura Leal-Taixé, Daniel Cremers, Luc Van Gool:
One-Shot Video Object Segmentation. CVPR 2017: 5320-5329 - [c227]Miroslava Slavcheva, Maximilian Baust, Daniel Cremers, Slobodan Ilic:
KillingFusion: Non-rigid 3D Reconstruction without Correspondences. CVPR 2017: 5474-5483 - [c226]Matthias Vestner, Roee Litman, Emanuele Rodolà, Alexander M. Bronstein, Daniel Cremers:
Product Manifold Filter: Non-rigid Shape Correspondence via Kernel Density Estimation in the Product Space. CVPR 2017: 6681-6690 - [c225]Lukas von Stumberg, Vladyslav C. Usenko, Jakob Engel, Jörg Stückler, Daniel Cremers:
From monocular SLAM to autonomous drone exploration. ECMR 2017: 1-8 - [c224]Jonas Geiping, Hendrik Dirks, Daniel Cremers, Michael Möller:
Multiframe Motion Coupling for Video Super Resolution. EMMCVPR 2017: 123-138 - [c223]Yvain Quéau, Jean Mélou, Fabien Castan, Daniel Cremers, Jean-Denis Durou:
A Variational Approach to Shape-from-Shading Under Natural Illumination. EMMCVPR 2017: 342-357 - [c222]Florian Walch, Caner Hazirbas, Laura Leal-Taixé, Torsten Sattler, Sebastian Hilsenbeck, Daniel Cremers:
Image-Based Localization Using LSTMs for Structured Feature Correlation. ICCV 2017: 627-637 - [c221]Thomas Möllenhoff, Daniel Cremers:
Sublabel-Accurate Discretization of Nonconvex Free-Discontinuity Problems. ICCV 2017: 1192-1200 - [c220]Tim Meinhardt, Michael Möller, Caner Hazirbas, Daniel Cremers:
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems. ICCV 2017: 1799-1808 - [c219]Philip Häusser, Thomas Frerix, Alexander Mordvintsev, Daniel Cremers:
Associative Domain Adaptation. ICCV 2017: 2784-2792 - [c218]Robert Maier, Kihwan Kim, Daniel Cremers, Jan Kautz, Matthias Nießner:
Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting. ICCV 2017: 3133-3141 - [c217]Rui Wang, Martin Schwörer, Daniel Cremers:
Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras. ICCV 2017: 3923-3931 - [c216]Songyou Peng, Bjoern Haefner, Yvain Quéau, Daniel Cremers:
Depth Super-Resolution Meets Uncalibrated Photometric Stereo. ICCV Workshops 2017: 2961-2968 - [c215]Maksym Dzitsiuk, Jürgen Sturm, Robert Maier, Lingni Ma, Daniel Cremers:
De-noising, stabilizing and completing 3D reconstructions on-the-go using plane priors. ICRA 2017: 3976-3983 - [c214]Mariano Jaimez, Christian Kerl, Javier González Jiménez, Daniel Cremers:
Fast odometry and scene flow from RGB-D cameras based on geometric clustering. ICRA 2017: 3992-3999 - [c213]Vladyslav C. Usenko, Lukas von Stumberg, Andrej Pangercic, Daniel Cremers:
Real-time trajectory replanning for MAVs using uniform B-splines and a 3D circular buffer. IROS 2017: 215-222 - [c212]Lingni Ma, Jörg Stückler, Christian Kerl, Daniel Cremers:
Multi-view deep learning for consistent semantic mapping with RGB-D cameras. IROS 2017: 598-605 - [c211]Georg Kuschk, Aljaz Bozic, Daniel Cremers:
Real-time variational stereo reconstruction with applications to large-scale dense SLAM. Intelligent Vehicles Symposium 2017: 1348-1355 - [c210]Daniel Bender, Wolfgang Koch, Daniel Cremers:
Map-based drone homing using shortcuts. MFI 2017: 505-511 - [c209]
- [c208]Yvain Quéau, Matthieu Pizenberg, Jean-Denis Durou, Daniel Cremers:
Microgeometry capture and RGB albedo estimation by photometric stereo without demosaicing. QCAV 2017: 103380O - [c207]Martin Benning, Michael Möller, Raz Z. Nossek, Martin Burger, Daniel Cremers, Guy Gilboa, Carola-Bibiane Schönlieb:
Nonlinear Spectral Image Fusion. SSVM 2017: 41-53 - [c206]Yvain Quéau, Tao Wu, Daniel Cremers:
Semi-calibrated Near-Light Photometric Stereo. SSVM 2017: 656-668 - [c205]Jean Mélou, Yvain Quéau, Jean-Denis Durou, Fabien Castan, Daniel Cremers:
Beyond Multi-view Stereo: Shading-Reflectance Decomposition. SSVM 2017: 694-705 - [i56]Matthias Vestner, Roee Litman, Emanuele Rodolà, Alexander M. Bronstein, Daniel Cremers:
Product Manifold Filter: Non-Rigid Shape Correspondence via Kernel Density Estimation in the Product Space. CoRR abs/1701.00669 (2017) - [i55]Vladyslav C. Usenko, Lukas von Stumberg, Andrej Pangercic, Daniel Cremers:
Real-Time Trajectory Replanning for MAVs using Uniform B-splines and 3D Circular Buffer. CoRR abs/1703.01416 (2017) - [i54]Martin Benning, Michael Möller, Raz Z. Nossek, Martin Burger, Daniel Cremers, Guy Gilboa, Carola-Bibiane Schönlieb:
Nonlinear Spectral Image Fusion. CoRR abs/1703.08001 (2017) - [i53]Lingni Ma, Jörg Stückler, Christian Kerl, Daniel Cremers:
Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras. CoRR abs/1703.08866 (2017) - [i52]Yvain Quéau, Jean Mélou, Jean-Denis Durou, Daniel Cremers:
Dense Multi-view 3D-reconstruction Without Dense Correspondences. CoRR abs/1704.00337 (2017) - [i51]
- [i50]Laura Leal-Taixé, Anton Milan, Konrad Schindler, Daniel Cremers, Ian D. Reid, Stefan Roth:
Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking. CoRR abs/1704.02781 (2017) - [i49]Tim Meinhardt, Michael Möller, Caner Hazirbas, Daniel Cremers:
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems. CoRR abs/1704.03488 (2017) - [i48]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. CoRR abs/1704.04039 (2017) - [i47]Nan Yang, Rui Wang, Daniel Cremers:
Feature-based or Direct: An Evaluation of Monocular Visual Odometry. CoRR abs/1705.04300 (2017) - [i46]Emanuel Laude, Jan-Hendrik Lange, Frank R. Schmidt, Bjoern Andres, Daniel Cremers:
Discrete-Continuous Splitting for Weakly Supervised Learning. CoRR abs/1705.05020 (2017) - [i45]Roberto Henschel, Laura Leal-Taixé, Daniel Cremers, Bodo Rosenhahn:
Improvements to Frank-Wolfe optimization for multi-detector multi-object tracking. CoRR abs/1705.08314 (2017) - [i44]Philip Häusser, Alexander Mordvintsev, Daniel Cremers:
Learning by Association - A versatile semi-supervised training method for neural networks. CoRR abs/1706.00909 (2017) - [i43]Thomas Frerix, Thomas Möllenhoff, Michael Möller, Daniel Cremers:
Proximal Backpropagation. CoRR abs/1706.04638 (2017) - [i42]Yvain Quéau, Bastien Durix, Tao Wu, Daniel Cremers, François Lauze, Jean-Denis Durou:
LED-based Photometric Stereo: Modeling, Calibration and Numerical Solution. CoRR abs/1707.01018 (2017) - [i41]Zorah Lähner, Matthias Vestner, Amit Boyarski, Or Litany, Ron Slossberg, Tal Remez, Emanuele Rodolà, Alexander M. Bronstein, Michael M. Bronstein, Ron Kimmel, Daniel Cremers:
Efficient Deformable Shape Correspondence via Kernel Matching. CoRR abs/1707.08991 (2017) - [i40]Songyou Peng, Bjoern Haefner, Yvain Quéau, Daniel Cremers:
Depth Super-Resolution Meets Uncalibrated Photometric Stereo. CoRR abs/1708.00411 (2017) - [i39]Philip Häusser, Thomas Frerix, Alexander Mordvintsev, Daniel Cremers:
Associative Domain Adaptation. CoRR abs/1708.00938 (2017) - [i38]Robert Maier, Kihwan Kim, Daniel Cremers, Jan Kautz, Matthias Nießner:
Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting. CoRR abs/1708.01670 (2017) - [i37]Rui Wang, Martin Schwörer, Daniel Cremers:
Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras. CoRR abs/1708.07878 (2017) - [i36]Robert Maier, Raphael Schaller, Daniel Cremers:
Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction. CoRR abs/1709.03763 (2017) - [i35]Kevis-Kokitsi Maninis, Sergi Caelles, Yuhua Chen, Jordi Pont-Tuset, Laura Leal-Taixé, Daniel Cremers, Luc Van Gool:
Video Object Segmentation Without Temporal Information. CoRR abs/1709.06031 (2017) - [i34]Jean Mélou, Yvain Quéau, Jean-Denis Durou, Fabien Castan, Daniel Cremers:
Variational Reflectance Estimation from Multi-view Images. CoRR abs/1709.08378 (2017) - [i33]Yvain Quéau, Jean Mélou, Fabien Castan, Daniel Cremers, Jean-Denis Durou:
A Variational Approach to Shape-from-shading Under Natural Illumination. CoRR abs/1709.10354 (2017) - [i32]Paul Bergmann, Rui Wang, Daniel Cremers:
Online Photometric Calibration for Auto Exposure Video for Realtime Visual Odometry and SLAM. CoRR abs/1710.02081 (2017) - [i31]Jan Kukacka, Vladimir Golkov, Daniel Cremers:
Regularization for Deep Learning: A Taxonomy. CoRR abs/1710.10686 (2017) - [i30]Virginia Estellers, Frank R. Schmidt, Daniel Cremers:
Compression for Smooth Shape Analysis. CoRR abs/1711.10824 (2017) - [i29]Daniel Cremers, Laura Leal-Taixé, René Vidal:
Deep Learning for Computer Vision (Dagstuhl Seminar 17391). Dagstuhl Reports 7(9): 109-125 (2017) - 2016
- [j61]Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael M. Bronstein, Daniel Cremers:
Anisotropic Diffusion Descriptors. Comput. Graph. Forum 35(2): 431-441 (2016) - [j60]Or Litany, Emanuele Rodolà, Alexander M. Bronstein, Michael M. Bronstein, Daniel Cremers:
Non-Rigid Puzzles. Comput. Graph. Forum 35(5): 135-143 (2016) - [j59]Julia Diebold, Claudia Nieuwenhuis, Daniel Cremers:
Midrange Geometric Interactions for Semantic Segmentation - Constraints for Continuous Multi-label Optimization. International Journal of Computer Vision 117(3): 199-225 (2016) - [j58]Joan Duran, Michael Möller, Catalina Sbert, Daniel Cremers:
On the Implementation of Collaborative TV Regularization: Application to Cartoon+Texture Decomposition. IPOL Journal 6: 27-74 (2016) - [j57]Joan Duran, Michael Möller, Catalina Sbert, Daniel Cremers:
Collaborative Total Variation: A General Framework for Vectorial TV Models. SIAM J. Imaging Sciences 9(1): 116-151 (2016) - [j56]Martin Burger, Guy Gilboa, Michael Möller, Lina Eckardt, Daniel Cremers:
Spectral Decompositions Using One-Homogeneous Functionals. SIAM J. Imaging Sciences 9(3): 1374-1408 (2016) - [j55]Vladimir Golkov, Alexey Dosovitskiy, Jonathan I. Sperl, Marion I. Menzel, Michael Czisch, Philipp G. Sämann, Thomas Brox, Daniel Cremers:
q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans. IEEE Trans. Med. Imaging 35(5): 1344-1351 (2016) - [c204]Luca Cosmo, Emanuele Rodolà, Michael M. Bronstein, Andrea Torsello, Daniel Cremers, Yusuf Sahillioglu:
Partial Matching of Deformable Shapes. 3DOR 2016 - [c203]Zorah Lähner, Emanuele Rodolà, Michael M. Bronstein, Daniel Cremers, Oliver Burghard, Luca Cosmo, Alexander Dieckmann, Reinhard Klein, Yusuf Sahillioglu:
Matching of Deformable Shapes with Topological Noise. 3DOR 2016 - [c202]Caner Hazirbas, Lingni Ma, Csaba Domokos, Daniel Cremers:
FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture. ACCV (1) 2016: 213-228 - [c201]Zorah Lähner, Emanuele Rodolà, Frank R. Schmidt, Michael M. Bronstein, Daniel Cremers:
Efficient Globally Optimal 2D-to-3D Deformable Shape Matching. CVPR 2016: 2185-2193 - [c200]Thomas Möllenhoff, Emanuel Laude, Michael Möller, Jan Lellmann, Daniel Cremers:
Sublabel-Accurate Relaxation of Nonconvex Energies. CVPR 2016: 3948-3956 - [c199]Nikolaus Mayer, Eddy Ilg, Philip Häusser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, Thomas Brox:
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation. CVPR 2016: 4040-4048 - [c198]Ioannis Chiotellis, Rudolph Triebel, Thomas Windheuser, Daniel Cremers:
Non-rigid 3D Shape Retrieval via Large Margin Nearest Neighbor Embedding. ECCV (2) 2016: 327-342 - [c197]Emanuel Laude, Thomas Möllenhoff, Michael Möller, Jan Lellmann, Daniel Cremers:
Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies. ECCV (1) 2016: 614-627 - [c196]Thomas Windheuser, Daniel Cremers:
A Convex Solution to Spatially-Regularized Correspondence Problems. ECCV (2) 2016: 853-868 - [c195]Daniel Bender, Fahmi Rouatbi, Marek Schikora, Daniel Cremers, Wolfgang Koch:
Scaling the world of monocular SLAM with INS-measurements for UAS navigation. FUSION 2016: 1493-1500 - [c194]