Computer Vision and Machine Learning

In the Focus Group Computer Vision & Machine Learning, Carl von Linde Senior Fellow Prof. Daniel Cremers (TUM) works together with Rudolf Diesel Industry Fellow Prof. Michael Bronstein (Intel / Imperial College / University of Lugano).

The activities of the Computer Vision and Machine Learning focus group is primarily centered around the interplay between geometry, machine learning and computer vision. Analysis of geometric objects has been a topic of computer vision and pattern recognition since the inception of the field. Classical computer vision problems of “Shape-from-X” aim at recovering the geometric structure of a 3D object from multiple images (Shape from stereo) or different illumination conditions (photometric stereo). In the recent years, the interest in 3D data has increased dramatically, fuelled in part by the commercial availability of affordable and compact 3D sensors. Such sensors are nowadays found in a broad range of applications from drones and augmented reality to self-driving cars.

In July 2018, the Focus Group Computer Vision & Machine Learning organized the workshop Machine Learning for 3D Understanding.

Publications by the Focus Group

2021

  • Bauermeister, Hartmut; Laude, Emanuel; Möllenhoff, Thomas; Moeller, Michael; Cremers, Daniel: Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields. , 2021 mehr…
  • Chui, Jason; Klenk, Simon; Cremers, Daniel: Event-Based Feature Tracking in Continuous Time with Sliding Window Optimization. , 2021 mehr…
  • Demmel, Nikolaus; Schubert, David; Sommer, Christiane; Cremers, Daniel; Usenko, Vladyslav: Square Root Marginalization for Sliding-Window Bundle Adjustment. , 2021 mehr…
  • Demmel, Nikolaus; Sommer, Christiane; Cremers, Daniel; Usenko, Vladyslav: Square Root Bundle Adjustment for Large-Scale Reconstruction. arXiv, 2021 mehr…
  • Eisenberger, Marvin; Novotny, David; Kerchenbaum, Gael; Labatut, Patrick; Neverova, Natalia; Cremers, Daniel; Vedaldi, Andrea: NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go. , 2021 mehr…
  • Frerix, Thomas; Kochkov, Dmitrii; Smith, Jamie A.; Cremers, Daniel; Brenner, Michael P.; Hoyer, Stephan: Variational Data Assimilation with a Learned Inverse Observation Operator. , 2021 mehr…
  • Gladkova, Mariia; Wang, Rui; Zeller, Niclas; Cremers, Daniel: Tight Integration of Feature-based Relocalization in Monocular Direct Visual Odometry. , 2021 mehr…
  • Haefner, Bjoern; Green, Simon; Oursland, Alan; Andersen, Daniel; Goesele, Michael; Cremers, Daniel; Newcombe, Richard; Whelan, Thomas: Recovering Real-World Reflectance Properties and Shading From HDR Imagery. 2021 International Conference on 3D Vision (3DV), IEEE, 2021 mehr…
  • Khan, Qadeer; Wenzel, Patrick; Cremers, Daniel: Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry. , 2021 mehr…
  • Klenk, Simon; Chui, Jason; Demmel, Nikolaus; Cremers, Daniel: TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset. , 2021 mehr…
  • Koestler, Lukas; Yang, Nan; Zeller, Niclas; Cremers, Daniel: TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo. , 2021 mehr…
  • Mozes, Maximilian; Schmitt, Martin; Golkov, Vladimir; Schütze, Hinrich; Cremers, Daniel: Scene Graph Generation for Better Image Captioning? , 2021 mehr…
  • Mukkamala, Mahesh Chandra; Westerkamp, Felix; Laude, Emanuel; Cremers, Daniel; Ochs, Peter: Bregman Proximal Gradient Algorithms for Deep Matrix Factorization. In: Lecture Notes in Computer Science. Springer International Publishing, 2021 mehr…
  • Müller, Philip; Golkov, Vladimir; Tomassini, Valentina; Cremers, Daniel: Rotation-Equivariant Deep Learning for Diffusion MRI. , 2021 mehr…
  • Naeyaert, M; Golkov, V; Cremers, D; Sijbers, J; Verhoye, M: Faster and better HARDI using FSE and holistic reconstruction. International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2021 mehr…
  • Tomani, Christian; Cremers, Daniel; Buettner, Florian: Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration. , 2021 mehr…
  • Tomani, Christian; Gruber, Sebastian; Erdem, Muhammed Ebrar; Cremers, Daniel; Buettner, Florian: Post-hoc Uncertainty Calibration for Domain Drift Scenarios. arXiv, 2021 mehr…
  • Weber, S; Demmel, N; Cremers, D: Multidirectional Conjugate Gradients for Scalable Bundle Adjustment. German Conference on Pattern Recognition (GCPR), 2021 mehr…
  • Wenzel, Patrick; Schön, Torsten; Leal-Taixé, Laura; Cremers, Daniel: Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning. , 2021 mehr…
  • Wimbauer, Felix; Yang, Nan; von Stumberg, Lukas; Zeller, Niclas; Cremers, Daniel: MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera. arXiv, 2021 mehr…
  • Wudenka, Martin; Müller, Marcus G.; Demmel, Nikolaus; Wedler, Armin; Triebel, Rudolph; Cremers, Daniel; Stürzl, Wolfgang: Towards Robust Monocular Visual Odometry for Flying Robots on Planetary Missions. , 2021 mehr…
  • Xia, Yan; Xu, Yusheng; Li, Shuang; Wang, Rui; Du, Juan; Cremers, Daniel; Stilla, Uwe: SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition. arXiv, 2021 mehr…
  • Ye, Zhenzhang; Haefner, Bjoern; Quéau, Yvain; Möllenhoff, Thomas; Cremers, Daniel: Sublabel-Accurate Multilabeling Meets Product Label Spaces. In: Lecture Notes in Computer Science. Springer International Publishing, 2021 mehr…

2020

  • Anees Kazi, Luca Cosmo, Nassir Navab, Michael Bronstein: Differentiable Graph Module (DGM) Graph Convolutional Networks. 2020 mehr…
  • Aygün, Mehmet; Lähner, Zorah; Cremers, Daniel: Unsupervised Dense Shape Correspondence using Heat Kernels. , 2020 mehr…
  • Brahimi, Mohammed; Quéau, Yvain; Haefner, Bjoern; Cremers, Daniel: On the Well-Posedness of Uncalibrated Photometric Stereo Under General Lighting. In: Advances in Photometric 3D-Reconstruction. Springer International Publishing, 2020 mehr…
  • Chiotellis, Ioannis; Cremers, Daniel: Neural Online Graph Exploration. , 2020 mehr…
  • Demmel, Nikolaus; Gao, Maolin; Laude, Emanuel; Wu, Tao; Cremers, Daniel: Distributed Photometric Bundle Adjustment. 2020 International Conference on 3D Vision (3DV), IEEE, 2020 mehr…
  • Du, Juan; Wang, Rui; Cremers, Daniel: DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization. , 2020 mehr…
  • Eisenberger, Marvin; Cremers, Daniel: Hamiltonian Dynamics for Real-World Shape Interpolation. , 2020 mehr…
  • Eisenberger, Marvin; Toker, Aysim; Leal-Taixé, Laura; Cremers, Daniel: Deep Shells: Unsupervised Shape Correspondence with Optimal Transport. , 2020 mehr…
  • Fabbro, Giorgio; Golkov, Vladimir; Kemp, Thomas; Cremers, Daniel: Speech Synthesis and Control Using Differentiable DSP. , 2020 mehr…
  • Frerix, Thomas; Nießner, Matthias; Cremers, Daniel: Homogeneous Linear Inequality Constraints for Neural Network Activations. arXiv, 2020 mehr…
  • Gao, Maolin; Lähner, Zorah; Thunberg, Johan; Cremers, Daniel; Bernard, Florian: Isometric Multi-Shape Matching. , 2020 mehr…
  • Golkov, Vladimir; Becker, Alexander; Plop, Daniel T.; Čuturilo, Daniel; Davoudi, Neda; Mendenhall, Jeffrey; Moretti, Rocco; Meiler, Jens; Cremers, Daniel: Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions. , 2020 mehr…
  • Holzschuh, Benjamin; Lahner, Zorah; Cremers, Daniel: Simulated Annealing for 3D Shape Correspondence. 2020 International Conference on 3D Vision (3DV), IEEE, 2020 mehr…
  • Koestler, L.; Yang, N.; Wang, R.; Cremers, D.: Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels. , 2020 mehr…
  • Laude, Emanuel; Ochs, Peter; Cremers, Daniel: Bregman Proximal Mappings and Bregman-Moreau Envelopes under Relative Prox-Regularity. arXiv, 2020 mehr…
  • Liu, Jiayu; Chiotellis, Ioannis; Triebel, Rudolph; Cremers, Daniel: Effective Version Space Reduction for Convolutional Neural Networks. , 2020 mehr…
  • Maier, Robert; Cremers, Daniel: RGB-D Vision. In: Encyclopedia of Robotics. Springer Berlin Heidelberg, 2020 mehr…
  • Naeyaert, Maarten; Aelterman, Jan; Van Audekerke, Johan; Golkov, Vladimir; Cremers, Daniel; Pižurica, Aleksandra; Sijbers, Jan; Verhoye, Marleen: Accelerating in vivo fast spin echo high angular resolution diffusion imaging with an isotropic resolution in mice through compressed sensing. Magnetic Resonance in Medicine 85 (3), 2020, 1397-1413 mehr…
  • Sommer, Christiane; Sun, Yumin; Bylow, Erik; Cremers, Daniel: PrimiTect: Fast Continuous Hough Voting for Primitive Detection. 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2020 mehr…
  • Sommer, Christiane; Sun, Yumin; Guibas, Leonidas; Cremers, Daniel; Birdal, Tolga: From Planes to Corners: Multi-Purpose Primitive Detection in Unorganized 3D Point Clouds. IEEE Robotics and Automation Letters 5 (2), 2020, 1764-1771 mehr…
  • Sommer, Christiane; Usenko, Vladyslav; Schubert, David; Demmel, Nikolaus; Cremers, Daniel: Efficient Derivative Computation for Cumulative B-Splines on Lie Groups. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2020 mehr…
  • Usenko, Vladyslav; Demmel, Nikolaus; Schubert, David; Stuckler, Jorg; Cremers, Daniel: Visual-Inertial Mapping With Non-Linear Factor Recovery. IEEE Robotics and Automation Letters 5 (2), 2020, 422-429 mehr…
  • Usenko, Vladyslav; von Stumberg, Lukas; Stückler, Jörg; Cremers, Daniel: TUM Flyers: Vision—Based MAV Navigation for Systematic Inspection of Structures. In: Bringing Innovative Robotic Technologies from Research Labs to Industrial End-users. Springer International Publishing, 2020 mehr…
  • Wang, Rui; Yang, Nan; Stueckler, Joerg; Cremers, Daniel: DirectShape: Direct Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation. arXiv, 2020 mehr…
  • Weiss, Sebastian; Maier, Robert; Cremers, Daniel; Westermann, Rudiger; Thuerey, Nils: Correspondence-Free Material Reconstruction using Sparse Surface Constraints. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2020 mehr…
  • Wenzel, Patrick; Wang, Rui; Yang, Nan; Cheng, Qing; Khan, Qadeer; von Stumberg, Lukas; Zeller, Niclas; Cremers, Daniel: 4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving. , 2020 mehr…
  • Yang, Nan; von Stumberg, Lukas; Wang, Rui; Cremers, Daniel: D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry. , 2020 mehr…
  • Ye, Zhenzhang; Möllenhoff, Thomas; Wu, Tao; Cremers, Daniel: Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning. , 2020 mehr…
  • Yenamandra, Tarun; Tewari, Ayush; Bernard, Florian; Seidel, Hans-Peter; Elgharib, Mohamed; Cremers, Daniel; Theobalt, Christian: i3DMM: Deep Implicit 3D Morphable Model of Human Heads. , 2020 mehr…
  • von Stumberg, Lukas; Wenzel, Patrick; Yang, Nan; Cremers, Daniel: LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization. , 2020 mehr…

2019

  • Bouritsas, Giorgos; Bokhnyak, Sergiy; Ploumpis, Stylianos; Zafeiriou, Stefanos; Bronstein, Michael: Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, 2019 mehr…
  • Bréchet, Pierre; Wu, Tao; Möllenhoff, Thomas; Cremers, Daniel: Informative GANs via Structured Regularization of Optimal Transport. , 2019 mehr…
  • Della Libera, Luca; Golkov, Vladimir; Zhu, Yue; Mielke, Arman; Cremers, Daniel: Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods. , 2019 mehr…
  • Dyke, R. M.; Stride, C.; Lai, Y.-K.; Rosin, P. L.; Aubry, M.; Boyarski, A.; Bronstein, A. M.; Bronstein, M. M.; Cremers, D.; Fisher, M.; Groueix, T.; Guo, D.; Kim, V. G.; Kimmel, R.; Lähner, Z.; Li, K.; Litany, O.; Remez, T.; Rodolà, E.; Russell, B. C.; Sahillioglu, Y.; Slossberg, R.; Tam, G. K. L.; Vestner, M.; Wu, Z.; Yang, J.: Shape Correspondence with Isometric and Non-Isometric Deformations. Eurographics Workshop on 3D Object Retrieval, 2019 mehr…
  • Eisenberger, Marvin; Lähner, Zorah; Cremers, Daniel: Smooth Shells: Multi-Scale Shape Registration with Functional Maps. , 2019 mehr…
  • Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, Michael M. Bronstein: Fake News Detection on Social Media using Geometric Deep Learning. 2019 mehr…
  • Gainza, P.; Sverrisson, F.; Monti, F.; Rodolà, E.; Boscaini, D.; Bronstein, M. M.; Correia, B. E.: Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nature Methods, 2019 mehr…
  • Haefner, Bjoern; Peng, Songyou; Verma, Alok; Quéau, Yvain; Cremers, Daniel: Photometric Depth Super-Resolution. arXiv, 2019 mehr…
  • Haefner, Bjoern; Queau, Yvain; Cremers, Daniel: Photometric Segmentation: Simultaneous Photometric Stereo and Masking. 2019 International Conference on 3D Vision (3DV), IEEE, 2019 mehr…
  • Haefner, Bjoern; Ye, Zhenzhang; Gao, Maolin; Wu, Tao; Quéau, Yvain; Cremers, Daniel: Variational Uncalibrated Photometric Stereo under General Lighting. arXiv, 2019 mehr…
  • Haeusser, Philip; Plapp, Johannes; Golkov, Vladimir; Aljalbout, Elie; Cremers, Daniel: Associative Deep Clustering: Training a Classification Network with No Labels. In: Lecture Notes in Computer Science. Springer International Publishing, 2019 mehr…
  • Jung, Eunah; Yang, Nan; Cremers, Daniel: Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light. , 2019 mehr…
  • Laude, Emanuel; Wu, Tao; Cremers, Daniel: Optimization of Inf-Convolution Regularized Nonconvex Composite Problems. , 2019 mehr…
  • Levie, Ron; Monti, Federico; Bresson, Xavier; Bronstein, Michael M.: CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters. IEEE Transactions on Signal Processing 67 (1), 2019, 97-109 mehr…
  • Maninis, K.-K.; Caelles, S.; Chen, Y.; Pont-Tuset, J.; Leal-Taixe, L.; Cremers, D.; Van Gool, L.: Video Object Segmentation without Temporal Information. IEEE Transactions on Pattern Analysis and Machine Intelligence 41 (6), 2019, 1515-1530 mehr…
  • Moeller, Michael; Möllenhoff, Thomas; Cremers, Daniel: Controlling Neural Networks via Energy Dissipation. , 2019 mehr…
  • Möllenhoff, Thomas; Cremers, Daniel: Lifting Vectorial Variational Problems: A Natural Formulation based on Geometric Measure Theory and Discrete Exterior Calculus. , 2019 mehr…
  • Möllenhoff, Thomas; Cremers, Daniel: Flat Metric Minimization with Applications in Generative Modeling. , 2019 mehr…
  • Pasa, F.; Golkov, V.; Pfeiffer, F.; Cremers, D.; Pfeiffer, D.: Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization. Scientific Reports 9 (1), 2019 mehr…
  • Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, Laura Leal-Taixe: CVPR19 Tracking and Detection Challenge: How crowded can it get? 2019 mehr…
  • Qadeer Khan, Patrick Wenzel, Daniel Cremers, Laura Leal-Taixé: Towards Generalizing Sensorimotor Control Across Weather Conditions. 2019 mehr…
  • Rodolà, E.; Lähner, Z.; Bronstein, A. M.; Bronstein, M. M.; Solomon, J.: Functional Maps Representation On Product Manifolds. Computer Graphics Forum 38 (1), 2019, 678-689 mehr…
  • Roy, Susmita; Grünwald, Alexander T. D.; Alves-Pinto, Ana; Maier, Robert; Cremers, Daniel; Pfeiffer, Daniela; Lampe, Renée: A Noninvasive 3D Body Scanner and Software Tool towards Analysis of Scoliosis. BioMed Research International 2019, 2019, 1-15 mehr…
  • Sang, Lu; Haefner, Bjoern; Cremers, Daniel: Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach. , 2019 mehr…
  • Schubert, David; Demmel, Nikolaus; Stumberg, Lukas von; Usenko, Vladyslav; Cremers, Daniel: Rolling-Shutter Modelling for Direct Visual-Inertial Odometry. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2019 mehr…
  • Schuchardt, Jan; Golkov, Vladimir; Cremers, Daniel: Learning to Evolve. , 2019 mehr…
  • Swazinna, P.; Golkov, V.; Lipp, I.; Sgarlata, E.; Tomassini, V.; Jones, D. K.; Cremers, D.: Negative-Unlabeled Learning for Diffusion MRI. International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2019 mehr…
  • Veselkov, Kirill; Gonzalez, Guadalupe; Aljifri, Shahad; Galea, Dieter; Mirnezami, Reza; Youssef, Jozef; Bronstein, Michael; Laponogov, Ivan: HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods. Scientific Reports 9 (1), 2019 mehr…
  • Wang, Yue; Sun, Yongbin; Liu, Ziwei; Sarma, Sanjay E.; Bronstein, Michael M.; Solomon, Justin M.: Dynamic Graph CNN for Learning on Point Clouds. ACM Transactions on Graphics 38 (5), 2019, 1-12 mehr…
  • Weiss, Sebastian; Maier, Robert; Westermann, Rüdiger; Cremers, Daniel; Thuerey, Nils: Sparse Surface Constraints for Combining Physics-based Elasticity Simulation and Correspondence-Free Object Reconstruction. , 2019 mehr…
  • von Stumberg, Lukas; Usenko, Vladyslav; Cremers, Daniel: A Review and Quantitative Evaluation of Direct Visual–Inertial Odometry. In: Multimodal Scene Understanding. Elsevier, 2019 mehr…
  • von Stumberg, Lukas; Wenzel, Patrick; Khan, Qadeer; Cremers, Daniel: GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization. , 2019 mehr…

2018

  • Aljalbout, Elie; Golkov, Vladimir; Siddiqui, Yawar; Strobel, Maximilian; Cremers, Daniel: Clustering with Deep Learning: Taxonomy and New Methods. , 2018 mehr…
  • Bringmann, Bjoern; Cremers, Daniel; Krahmer, Felix; Moeller, Michael: The homotopy method revisited: Computing solution paths of L1-regularized problems. Mathematics of Computation 87 (313), 2018, 2343-2364 mehr…
  • Chiotellis, Ioannis; Zimmermann, Franziska; Cremers, Daniel; Triebel, Rudolph: Incremental Semi-Supervised Learning from Streams for Object Classification. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2018 mehr…
  • Choma, Nicholas; Monti, Federico; Gerhardt, Lisa; Palczewski, Tomasz; Ronaghi, Zahra; Prabhat, Prabhat; Bhimji, Wahid; Bronstein, Michael; Klein, Spencer; Bruna, Joan: Graph Neural Networks for IceCube Signal Classification. 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, 2018 mehr…
  • Cremers, Daniel; Leal-Taixé, Laura; Vidal, René: Deep Learning for Computer Vision (Dagstuhl Seminar 17391). Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Wadern/Saarbruecken, Germany, 2018 mehr…
  • Do, Binh Thanh; Golkov, Vladimir; Gurel, Goktug Erce; Cremers, Daniel: Precursor microRNA Identification Using Deep Convolutional Neural Networks. bioRxiv, 2018 mehr…
  • Domokos, Csaba; Schmidt, Frank R.; Cremers, Daniel: MRF Optimization with Separable Convex Prior on Partially Ordered Labels. In: Computer Vision – ECCV 2018. Springer International Publishing, 2018 mehr…
  • Eisenberger, Marvin; Lähner, Zorah; Cremers, Daniel: Divergence-Free Shape Interpolation and Correspondence. , 2018 mehr…
  • Engel, J.; Koltun, V.; Cremers, D.: Direct Sparse Odometry. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018 mehr…
  • Estellers, Virginia; Schmidt, Frank; Cremers, Daniel: Robust Fitting of Subdivision Surfaces for Smooth Shape Analysis. 2018 International Conference on 3D Vision (3DV), IEEE, 2018 mehr…
  • Gao, Xiang; Wang, Rui; Demmel, Nikolaus; Cremers, Daniel: LDSO: Direct Sparse Odometry with Loop Closure. , 2018 mehr…
  • Gehre, Anne; Lim, Isaak; Kobbelt, Leif: Feature Curve Co-Completion in Noisy Data. Computer Graphics Forum 37 (2), 2018, 1-12 mehr…
  • Golkov, V.; Swazinna, P.; Schmitt, M. M.; Khan, Q. A.; Tax, C. M. W.; Serahlazau, M.; Pasa, F.; Pfeiffer, F.; Biessels, G. J.; Leemans, A.; Cremers, D.: q-Space Deep Learning for Alzheimer's Disease Diagnosis: Global Prediction and Weakly-Supervised Localization. International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2018 mehr…
  • Golkov, V.; Vasilev, A.; Pasa, F.; Lipp, I.; Boubaker, W.; Sgarlata, E.; Pfeiffer, F.; Tomassini, V.; Jones, D. K.; Cremers, D.: q-Space Novelty Detection in Short Diffusion MRI Scans of Multiple Sclerosis. International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2018 mehr…
  • Haefner, Bjoern; Queau, Yvain; Mollenhoff, Thomas; Cremers, Daniel: Fight Ill-Posedness with Ill-Posedness: Single-shot Variational Depth Super-Resolution from Shading. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018 mehr…
  • Jan Svoboda, Jonathan Masci, Federico Monti, Michael M. Bronstein, Leonidas Guibas: PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks. 2018 mehr…
  • Jan Svoboda, Jonathan Masci, Federico Monti, Michael M. Bronstein, Leonidas Guibas: PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks. Proceedings of the International Conference on Learning Representations, 2018 mehr…
  • Laehner, Zorah; Cremers, Daniel; Tung, Tony: DeepWrinkles: Accurate and Realistic Clothing Modeling. , 2018 mehr…
  • Laude, Emanuel; Lange, Jan-Hendrik; Schupfer, Jonas; Domokos, Csaba; Leal-Taixe, Laura; Schmidt, Frank R.; Andres, Bjoern; Cremers, Daniel: Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018 mehr…
  • Litany, Or; Bronstein, Alex; Bronstein, Michael; Makadia, Ameesh: Deformable Shape Completion with Graph Convolutional Autoencoders. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018 mehr…
  • Ma, Lingni; Stückler, Jörg; Wu, Tao; Cremers, Daniel: Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform. , 2018 mehr…
  • Matsuki, Hidenobu; von Stumberg, Lukas; Usenko, Vladyslav; Stückler, Jörg; Cremers, Daniel: Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras. arXiv, 2018 mehr…
  • Mayer, Nikolaus; Ilg, Eddy; Fischer, Philipp; Hazirbas, Caner; Cremers, Daniel; Dosovitskiy, Alexey; Brox, Thomas: What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation? arXiv, 2018 mehr…
  • Mengyu Chu, You Xie, Jonas Mayer, Laura Leal-Taixé, Nils Thuerey: Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation. 2018 mehr…
  • Moeller, Michael; Cremers, Daniel: Image Denoising—Old and New. In: Denoising of Photographic Images and Video. Springer International Publishing, 2018 mehr…
  • Monti, Federico; Otness, Karl; Bronstein, Michael M.: MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS. 2018 IEEE Data Science Workshop (DSW), IEEE, 2018 mehr…
  • Möllenhoff, Thomas; Ye, Zhenzhang; Wu, Tao; Cremers, Daniel: Combinatorial Preconditioners for Proximal Algorithms on Graphs. , 2018 mehr…
  • Nogneng, D.; Melzi, S.; Rodolà, E.; Castellani, U.; Bronstein, M.; Ovsjanikov, M.: Improved Functional Mappings via Product Preservation. Computer Graphics Forum 37 (2), 2018, 179-190 mehr…
  • Quéau, Yvain; Mélou, Jean; Castan, Fabien; Cremers, Daniel; Durou, Jean-Denis: A Variational Approach to Shape-from-Shading Under Natural Illumination. In: Lecture Notes in Computer Science. Springer International Publishing, 2018 mehr…
  • Schubert, David; Demmel, Nikolaus; Usenko, Vladyslav; Stückler, Jörg; Cremers, Daniel: Direct Sparse Odometry with Rolling Shutter. arXiv, 2018 mehr…
  • Schubert, David; Goll, Thore; Demmel, Nikolaus; Usenko, Vladyslav; Stückler, Jörg; Cremers, Daniel: The TUM VI Benchmark for Evaluating Visual-Inertial Odometry. arXiv, 2018 mehr…
  • Scona, Raluca; Jaimez, Mariano; Petillot, Yvan R.; Fallon, Maurice; Cremers, Daniel: StaticFusion: Background Reconstruction for Dense RGB-D SLAM in Dynamic Environments. 2018 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2018 mehr…
  • Sommer, Christiane; Cremers, Daniel: Joint Representation of Primitive and Non-primitive Objects for 3D Vision. 2018 International Conference on 3D Vision (3DV), IEEE, 2018 mehr…
  • Tjaden, Henning; Schwanecke, Ulrich; Schömer, Elmar; Cremers, Daniel: A Region-based Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking. arXiv, 2018 mehr…
  • Usenko, Vladyslav; Demmel, Nikolaus; Cremers, Daniel: The Double Sphere Camera Model. arXiv, 2018 mehr…
  • Vasilev, Aleksei; Golkov, Vladimir; Meissner, Marc; Lipp, Ilona; Sgarlata, Eleonora; Tomassini, Valentina; Jones, Derek K.; Cremers, Daniel: q-Space Novelty Detection with Variational Autoencoders. , 2018 mehr…
  • Wenzel, Patrick; Khan, Qadeer; Cremers, Daniel; Leal-Taixé, Laura: Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs. , 2018 mehr…
  • Yang, Nan; Wang, Rui; Gao, Xiang; Cremers, Daniel: Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias, and Rolling Shutter Effect. IEEE Robotics and Automation Letters 3 (4), 2018, 2878-2885 mehr…
  • Yang, Nan; Wang, Rui; Stückler, Jörg; Cremers, Daniel: Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry. , 2018 mehr…
  • von Stumberg, Lukas; Usenko, Vladyslav; Cremers, Daniel: Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization. arXiv, 2018 mehr…

2017

  • Bender, Daniel; Koch, Wolfgang; Cremers, Daniel: Map-based drone homing using shortcuts. 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), IEEE, 2017 mehr…
  • Benning, Martin; Möller, Michael; Nossek, Raz Z.; Burger, Martin; Cremers, Daniel; Gilboa, Guy; Schönlieb, Carola-Bibiane: Nonlinear Spectral Image Fusion. , 2017 mehr…
  • Bergmann, Paul; Wang, Rui; Cremers, Daniel: Online Photometric Calibration for Auto Exposure Video for Realtime Visual Odometry and SLAM. , 2017 mehr…
  • Caelles, S.; Maninis, K. -K.; Pont-Tuset, J.; Leal-Taixe, L.; Cremers, D.; Gool, L. Van: One-Shot Video Object Segmentation. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2017 mehr…
  • Cremers, Daniel: Computer Vision für 3-D-Rekonstruktion. Informatik-Spektrum 40 (2), 2017, 205-209 mehr…
  • Frerix, Thomas; Möllenhoff, Thomas; Moeller, Michael; Cremers, Daniel: Proximal Backpropagation. , 2017 mehr…
  • Golkov, Vladimir; Skwark, Marcin J.; Mirchev, Atanas; Dikov, Georgi; Geanes, Alexander R.; Mendenhall, Jeffrey; Meiler, Jens; Cremers, Daniel: 3D Deep Learning for Biological Function Prediction from Physical Fields. , 2017 mehr…
  • Haeusser, Philip; Frerix, Thomas; Mordvintsev, Alexander; Cremers, Daniel: Associative Domain Adaptation. , 2017 mehr…
  • Hazirbas, Caner; Soyer, Sebastian Georg; Staab, Maximilian Christian; Leal-Taixé, Laura; Cremers, Daniel: Deep Depth From Focus. , 2017 mehr…
  • Henschel, Roberto; Leal-Taixé, Laura; Cremers, Daniel; Rosenhahn, Bodo: Fusion of Head and Full-Body Detectors for Multi-Object Tracking. , 2017 mehr…
  • Häusser, Philip; Mordvintsev, Alexander; Cremers, Daniel: Learning by Association - A versatile semi-supervised training method for neural networks. , 2017 mehr…
  • Jaimez, M.; Cashman, T. J.; Fitzgibbon, A.; Gonzalez-Jimenez, J.; Cremers, D.: An Efficient Background Term for 3D Reconstruction and Tracking with Smooth Subdivision Surface Models. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 mehr…
  • Jaimez, Mariano; Kerl, Christian; Gonzalez-Jimenez, Javier; Cremers, Daniel: Fast odometry and scene flow from RGB-D cameras based on geometric clustering. 2017 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2017 mehr…
  • Kee, Youngwook; Lee, Yegang; Souiai, Mohamed; Cremers, Daniel; Kim, Junmo: Sequential Convex Programming for Computing Information-Theoretic Minimal Partitions: Nonconvex Nonsmooth Optimization. SIAM Journal on Imaging Sciences 10 (4), 2017, 1845-1877 mehr…
  • Krieg, Michael; Stuehmer, Jan; Cueva, Juan G.; Fetter, Richard; Spilker, Kerri; Cremers, Daniel; Shen, Kang; Dunn, Alex R.; Goodman, Miriam B.: Tau Like Proteins Reduce Torque Generation in Microtubule Bundles. Biophysical Journal 112 (3), 2017, 29a-30a mehr…
  • Krieg, Michael; Stühmer, Jan; Cueva, Juan G; Fetter, Richard; Spilker, Kerri; Cremers, Daniel; Shen, Kang; Dunn, Alexander R; Goodman, Miriam B: Genetic defects in β-spectrin and tau sensitize C. elegans axons to movement-induced damage via torque-tension coupling. eLife 6, 2017 mehr…
  • Kukačka, Jan; Golkov, Vladimir; Cremers, Daniel: Regularization for Deep Learning: A Taxonomy. , 2017 mehr…
  • Kuschk, Georg; Bozic, Aljaz; Cremers, Daniel: Real-time variational stereo reconstruction with applications to large-scale dense SLAM. 2017 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2017 mehr…
  • Kuschk, Georg; d'Angelo, Pablo; Gaudrie, David; Reinartz, Peter; Cremers, Daniel: Spatially Regularized Fusion of Multiresolution Digital Surface Models. IEEE Transactions on Geoscience and Remote Sensing 55 (3), 2017, 1477-1488 mehr…
  • Laude, Emanuel; Wu, Tao; Cremers, Daniel: A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization. , 2017 mehr…
  • Lähner, Zorah; Vestner, Matthias; Boyarski, Amit; Litany, Or; Slossberg, Ron; Remez, Tal; Rodolà, Emanuele; Bronstein, Alex; Bronstein, Michael; Kimmel, Ron; Cremers, Daniel: Efficient Deformable Shape Correspondence via Kernel Matching. , 2017 mehr…
  • Ma, Lingni; Stückler, Jörg; Kerl, Christian; Cremers, Daniel: Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras. , 2017 mehr…
  • Maier, Robert; Kim, Kihwan; Cremers, Daniel; Kautz, Jan; Nießner, Matthias: Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting. , 2017 mehr…
  • Maier, Robert; Schaller, Raphael; Cremers, Daniel: Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction. , 2017 mehr…
  • Meinhardt, Tim; Moeller, Michael; Hazirbas, Caner; Cremers, Daniel: Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems. arXiv, 2017 mehr…
  • Melzi, S.; Rodolà, E.; Castellani, U.; Bronstein, M. M.: Localized Manifold Harmonics for Spectral Shape Analysis. Computer Graphics Forum 37 (6), 2017, 20-34 mehr…
  • Mélou, Jean; Quéau, Yvain; Durou, Jean-Denis; Castan, Fabien; Cremers, Daniel: Variational Reflectance Estimation from Multi-view Images. , 2017 mehr…
  • Mélou, Jean; Quéau, Yvain; Durou, Jean-Denis; Castan, Fabien; Cremers, Daniel: Beyond Multi-view Stereo: Shading-Reflectance Decomposition. In: Lecture Notes in Computer Science. Springer International Publishing, 2017 mehr…
  • Peng, Songyou; Haefner, Bjoern; Quéau, Yvain; Cremers, Daniel: Depth Super-Resolution Meets Uncalibrated Photometric Stereo. , 2017 mehr…
  • Queau, Yvain; Wu, Tao; Lauze, Francois; Durou, Jean-Denis; Cremers, Daniel: A Non-convex Variational Approach to Photometric Stereo under Inaccurate Lighting. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2017 mehr…
  • Quéau, Yvain; Durix, Bastien; Wu, Tao; Cremers, Daniel; Lauze, François; Durou, Jean-Denis: LED-Based Photometric Stereo: Modeling, Calibration and Numerical Solution. Journal of Mathematical Imaging and Vision 60 (3), 2017, 313-340 mehr…
  • Quéau, Yvain; Mélou, Jean; Durou, Jean-Denis; Cremers, Daniel: Dense Multi-view 3D-reconstruction Without Dense Correspondences. , 2017 mehr…
  • Quéau, Yvain; Pizenberg, Mathieu; Durou, Jean-Denis; Cremers, Daniel: Microgeometry capture and RGB albedo estimation by photometric stereo without demosaicing. SPIE Proceedings, SPIE, 2017 mehr…
  • Quéau, Yvain; Pizenberg, Matthieu; Cremers, Daniel; Durou, Jean-Denis: Stéréophotométrie microscopique sans démosaïquage. 26eme Colloque GRETSI sur le Traitement du Signal et des Images (GRETSI 2017), 2017 mehr…
  • Quéau, Yvain; Wu, Tao; Cremers, Daniel: Semi-calibrated Near-Light Photometric Stereo. In: Lecture Notes in Computer Science. Springer International Publishing, 2017 mehr…
  • Rodolà, E.; Moeller, M.; Cremers, D.: Regularized Pointwise Map Recovery from Functional Correspondence. Computer Graphics Forum 36 (8), 2017, 700-711 mehr…
  • Slavcheva, Miroslava; Baust, Maximilian; Cremers, Daniel; Ilic, Slobodan: KillingFusion: Non-rigid 3D Reconstruction without Correspondences. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2017 mehr…
  • Usenko, Vladyslav; von Stumberg, Lukas; Pangercic, Andrej; Cremers, Daniel: Real-Time Trajectory Replanning for MAVs using Uniform B-splines and a 3D Circular Buffer. arXiv, 2017 mehr…
  • Vestner, Matthias; Litman, Roee; Rodolà, Emanuele; Bronstein, Alex; Cremers, Daniel: Product Manifold Filter: Non-Rigid Shape Correspondence via Kernel Density Estimation in the Product Space. , 2017 mehr…
  • Walch, F.; Hazirbas, C.; Leal-Taixe, L.; Sattler, T.; Hilsenbeck, S.; Cremers, D.: Image-Based Localization Using LSTMs for Structured Feature Correlation. 2017 IEEE International Conference on Computer Vision (ICCV), IEEE, 2017 mehr…
  • Wang, Rui; Schwörer, Martin; Cremers, Daniel: Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras. , 2017 mehr…

2016

  • Bernard, Florian; Schmidt, Frank R.; Thunberg, Johan; Cremers, Daniel: A Combinatorial Solution to Non-Rigid 3D Shape-to-Image Matching. arXiv, 2016 mehr…
  • Boscaini, D.; Masci, J.; Rodolà, E.; Bronstein, M. M.; Cremers, D.: Anisotropic Diffusion Descriptors. Computer Graphics Forum 35 (2), 2016, 431-441 mehr…
  • Cosmo, L.; Rodola, E.; Bronstein, M. M.; Torsello, A.; Cremers, D.; Sahillioglu, Y.: SHREC’16: Partial Matching of Deformable Shapes. Proc. of Eurographics Workshop on 3D Object Retrieval (3DOR), 2016 mehr…
  • Cosmo, L.; Rodolà, E.; Albarelli, A.; Mémoli, F.; Cremers, D.: Consistent Partial Matching of Shape Collections via Sparse Modeling. Computer Graphics Forum 36 (1), 2016, 209-221 mehr…
  • Cosmo, Luca; Albarelli, Andrea; Bergamasco, Filippo; Torsello, Andrea; Rodola, Emanuele; Cremers, Daniel: A game-theoretical approach for joint matching of multiple feature throughout unordered images. 2016 23rd International Conference on Pattern Recognition (ICPR), IEEE, 2016 mehr…
  • Dzitsiuk, Maksym; Sturm, Jürgen; Maier, Robert; Ma, Lingni; Cremers, Daniel: De-noising, Stabilizing and Completing 3D Reconstructions On-the-go using Plane Priors. , 2016 mehr…
  • Geiping, Jonas; Dirks, Hendrik; Cremers, Daniel; Moeller, Michael: Multiframe Motion Coupling for Video Super Resolution. , 2016 mehr…
  • Litany, O.; Rodolà, E.; Bronstein, A. M.; Bronstein, M. M.; Cremers, D.: Non-Rigid Puzzles. Computer Graphics Forum 35 (5), 2016, 135-143 mehr…
  • Lähner, Z.; Rodola, E.; Bronstein, M. M.; Cremers, D.; Burghard, O.; Cosmo, L.; Dieckmann, A.; Klein, R.; Sahillioglu, Y.: SHREC’16: Matching of Deformable Shapes with Topological Noise. Proc. of Eurographics Workshop on 3D Object Retrieval (3DOR), 2016 mehr…
  • Lähner, Zorah; Rodolà, Emanuele; Bronstein, Michael M.; Cremers, Daniel; Burghard, Oliver; Cosmo, Luca; Dieckmann, Alexander; Klein, Reinhard; Sahillioğlu, Yusuf: Matching of Deformable Shapes with Topological Noise. Eurographics Workshop on 3D Object Retrieval, 2016 mehr…
  • Lähner, Zorah; Rodolà, Emanuele; Schmidt, Frank R.; Bronstein, Michael M.; Cremers, Daniel: Efficient Globally Optimal 2D-to-3D Deformable Shape Matching. , 2016 mehr…
  • Möllenhoff, Thomas; Cremers, Daniel: Sublabel-Accurate Discretization of Nonconvex Free-Discontinuity Problems. , 2016 mehr…
  • Rodolà, E.; Cosmo, L.; Bronstein, M. M.; Torsello, A.; Cremers, D.: Partial Functional Correspondence. Computer Graphics Forum 36 (1), 2016, 222-236 mehr…
  • Vestner, Matthias; Litman, Roee; Bronstein, Alex; Rodolà, Emanuele; Cremers, Daniel: Bayesian Inference of Bijective Non-Rigid Shape Correspondence. , 2016 mehr…
  • Vestner, Matthias; Rodolà, Emanuele; Windheuser, Thomas; Bulò, Samuel Rota; Cremers, Daniel: Applying Random Forests to the Problem of Dense Non-rigid Shape Correspondence. In: Mathematics and Visualization. Springer International Publishing, 2016 mehr…
  • von Stumberg, Lukas; Usenko, Vladyslav; Engel, Jakob; Stückler, Jörg; Cremers, Daniel: From Monocular SLAM to Autonomous Drone Exploration. arXiv, 2016 mehr…

2015

  • Bergamasco, F.; Albarelli, A.; Cosmo, L.; Torsello, A.; Rodola, E.; Cremers, D.: Adopting an Unconstrained Ray Model in Light-field Cameras for 3D Shape Reconstruction. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 mehr…
  • Mecca, R.; Rodolà, E.; Cremers, D.: Realistic photometric stereo using partial differential irradiance equation ratios. Computers & Graphics 51, 2015, 8-16 mehr…
  • Mecca, Roberto; Rodolà, Emanuele; Cremers, Daniel: Analysis of surface parametrizations for modern photometric stereo modeling. Twelfth International Conference on Quality Control by Artificial Vision 2015, SPIE, 2015 mehr…
  • Rodolà, Emanuele; Albarelli, Andrea; Cremers, Daniel; Torsello, Andrea: A simple and effective relevance-based point sampling for 3D shapes. Pattern Recognition Letters 59, 2015, 41-47 mehr…
  • Rodolà, Emanuele; Cosmo, Luca; Bronstein, Michael M.; Torsello, Andrea; Cremers, Daniel: Partial Functional Correspondence. , 2015 mehr…
  • Rodolà, Emanuele; Moeller, Michael; Cremers, Daniel: Point-wise Map Recovery and Refinement from Functional Correspondence. , 2015 mehr…