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 more…
  • Chui, Jason; Klenk, Simon; Cremers, Daniel: Event-Based Feature Tracking in Continuous Time with Sliding Window Optimization. , 2021 more…
  • Demmel, Nikolaus; Schubert, David; Sommer, Christiane; Cremers, Daniel; Usenko, Vladyslav: Square Root Marginalization for Sliding-Window Bundle Adjustment. , 2021 more…
  • Demmel, Nikolaus; Sommer, Christiane; Cremers, Daniel; Usenko, Vladyslav: Square Root Bundle Adjustment for Large-Scale Reconstruction. arXiv, 2021 more…
  • Eisenberger, Marvin; Novotny, David; Kerchenbaum, Gael; Labatut, Patrick; Neverova, Natalia; Cremers, Daniel; Vedaldi, Andrea: NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go. , 2021 more…
  • Frerix, Thomas; Kochkov, Dmitrii; Smith, Jamie A.; Cremers, Daniel; Brenner, Michael P.; Hoyer, Stephan: Variational Data Assimilation with a Learned Inverse Observation Operator. , 2021 more…
  • Gladkova, Mariia; Wang, Rui; Zeller, Niclas; Cremers, Daniel: Tight Integration of Feature-based Relocalization in Monocular Direct Visual Odometry. , 2021 more…
  • 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 more…
  • Khan, Qadeer; Wenzel, Patrick; Cremers, Daniel: Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry. , 2021 more…
  • Klenk, Simon; Chui, Jason; Demmel, Nikolaus; Cremers, Daniel: TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset. , 2021 more…
  • Koestler, Lukas; Yang, Nan; Zeller, Niclas; Cremers, Daniel: TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo. , 2021 more…
  • Mozes, Maximilian; Schmitt, Martin; Golkov, Vladimir; Schütze, Hinrich; Cremers, Daniel: Scene Graph Generation for Better Image Captioning? , 2021 more…
  • 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 more…
  • Müller, Philip; Golkov, Vladimir; Tomassini, Valentina; Cremers, Daniel: Rotation-Equivariant Deep Learning for Diffusion MRI. , 2021 more…
  • 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 more…
  • Tomani, Christian; Cremers, Daniel; Buettner, Florian: Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration. , 2021 more…
  • Tomani, Christian; Gruber, Sebastian; Erdem, Muhammed Ebrar; Cremers, Daniel; Buettner, Florian: Post-hoc Uncertainty Calibration for Domain Drift Scenarios. arXiv, 2021 more…
  • Weber, S; Demmel, N; Cremers, D: Multidirectional Conjugate Gradients for Scalable Bundle Adjustment. German Conference on Pattern Recognition (GCPR), 2021 more…
  • Wenzel, Patrick; Schön, Torsten; Leal-Taixé, Laura; Cremers, Daniel: Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning. , 2021 more…
  • 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 more…
  • 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 more…
  • 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 more…
  • 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 more…

2020

  • Anees Kazi, Luca Cosmo, Nassir Navab, Michael Bronstein: Differentiable Graph Module (DGM) Graph Convolutional Networks. 2020 more…
  • Aygün, Mehmet; Lähner, Zorah; Cremers, Daniel: Unsupervised Dense Shape Correspondence using Heat Kernels. , 2020 more…
  • 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 more…
  • Chiotellis, Ioannis; Cremers, Daniel: Neural Online Graph Exploration. , 2020 more…
  • Demmel, Nikolaus; Gao, Maolin; Laude, Emanuel; Wu, Tao; Cremers, Daniel: Distributed Photometric Bundle Adjustment. 2020 International Conference on 3D Vision (3DV), IEEE, 2020 more…
  • Du, Juan; Wang, Rui; Cremers, Daniel: DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization. , 2020 more…
  • Eisenberger, Marvin; Cremers, Daniel: Hamiltonian Dynamics for Real-World Shape Interpolation. , 2020 more…
  • Eisenberger, Marvin; Toker, Aysim; Leal-Taixé, Laura; Cremers, Daniel: Deep Shells: Unsupervised Shape Correspondence with Optimal Transport. , 2020 more…
  • Fabbro, Giorgio; Golkov, Vladimir; Kemp, Thomas; Cremers, Daniel: Speech Synthesis and Control Using Differentiable DSP. , 2020 more…
  • Frerix, Thomas; Nießner, Matthias; Cremers, Daniel: Homogeneous Linear Inequality Constraints for Neural Network Activations. arXiv, 2020 more…
  • Gao, Maolin; Lähner, Zorah; Thunberg, Johan; Cremers, Daniel; Bernard, Florian: Isometric Multi-Shape Matching. , 2020 more…
  • 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 more…
  • Holzschuh, Benjamin; Lahner, Zorah; Cremers, Daniel: Simulated Annealing for 3D Shape Correspondence. 2020 International Conference on 3D Vision (3DV), IEEE, 2020 more…
  • Koestler, L.; Yang, N.; Wang, R.; Cremers, D.: Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels. , 2020 more…
  • Laude, Emanuel; Ochs, Peter; Cremers, Daniel: Bregman Proximal Mappings and Bregman-Moreau Envelopes under Relative Prox-Regularity. arXiv, 2020 more…
  • Liu, Jiayu; Chiotellis, Ioannis; Triebel, Rudolph; Cremers, Daniel: Effective Version Space Reduction for Convolutional Neural Networks. , 2020 more…
  • Maier, Robert; Cremers, Daniel: RGB-D Vision. In: Encyclopedia of Robotics. Springer Berlin Heidelberg, 2020 more…
  • 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 more…
  • 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 more…
  • 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 more…
  • 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 more…
  • 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 more…
  • 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 more…
  • Wang, Rui; Yang, Nan; Stueckler, Joerg; Cremers, Daniel: DirectShape: Direct Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation. arXiv, 2020 more…
  • 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 more…
  • 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 more…
  • Yang, Nan; von Stumberg, Lukas; Wang, Rui; Cremers, Daniel: D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry. , 2020 more…
  • Ye, Zhenzhang; Möllenhoff, Thomas; Wu, Tao; Cremers, Daniel: Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning. , 2020 more…
  • 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 more…
  • von Stumberg, Lukas; Wenzel, Patrick; Yang, Nan; Cremers, Daniel: LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization. , 2020 more…

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 more…
  • Bréchet, Pierre; Wu, Tao; Möllenhoff, Thomas; Cremers, Daniel: Informative GANs via Structured Regularization of Optimal Transport. , 2019 more…
  • Della Libera, Luca; Golkov, Vladimir; Zhu, Yue; Mielke, Arman; Cremers, Daniel: Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods. , 2019 more…
  • 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 more…
  • Eisenberger, Marvin; Lähner, Zorah; Cremers, Daniel: Smooth Shells: Multi-Scale Shape Registration with Functional Maps. , 2019 more…
  • Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, Michael M. Bronstein: Fake News Detection on Social Media using Geometric Deep Learning. 2019 more…
  • 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 more…
  • Haefner, Bjoern; Peng, Songyou; Verma, Alok; Quéau, Yvain; Cremers, Daniel: Photometric Depth Super-Resolution. arXiv, 2019 more…
  • Haefner, Bjoern; Queau, Yvain; Cremers, Daniel: Photometric Segmentation: Simultaneous Photometric Stereo and Masking. 2019 International Conference on 3D Vision (3DV), IEEE, 2019 more…
  • Haefner, Bjoern; Ye, Zhenzhang; Gao, Maolin; Wu, Tao; Quéau, Yvain; Cremers, Daniel: Variational Uncalibrated Photometric Stereo under General Lighting. arXiv, 2019 more…
  • 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 more…
  • Jung, Eunah; Yang, Nan; Cremers, Daniel: Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light. , 2019 more…
  • Laude, Emanuel; Wu, Tao; Cremers, Daniel: Optimization of Inf-Convolution Regularized Nonconvex Composite Problems. , 2019 more…
  • 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 more…
  • 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 more…
  • Moeller, Michael; Möllenhoff, Thomas; Cremers, Daniel: Controlling Neural Networks via Energy Dissipation. , 2019 more…
  • Möllenhoff, Thomas; Cremers, Daniel: Lifting Vectorial Variational Problems: A Natural Formulation based on Geometric Measure Theory and Discrete Exterior Calculus. , 2019 more…
  • Möllenhoff, Thomas; Cremers, Daniel: Flat Metric Minimization with Applications in Generative Modeling. , 2019 more…
  • 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 more…
  • 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 more…
  • Qadeer Khan, Patrick Wenzel, Daniel Cremers, Laura Leal-Taixé: Towards Generalizing Sensorimotor Control Across Weather Conditions. 2019 more…
  • 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 more…
  • 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 more…
  • Sang, Lu; Haefner, Bjoern; Cremers, Daniel: Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach. , 2019 more…
  • 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 more…
  • Schuchardt, Jan; Golkov, Vladimir; Cremers, Daniel: Learning to Evolve. , 2019 more…
  • 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 more…
  • 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 more…
  • 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 more…
  • 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 more…
  • von Stumberg, Lukas; Usenko, Vladyslav; Cremers, Daniel: A Review and Quantitative Evaluation of Direct Visual–Inertial Odometry. In: Multimodal Scene Understanding. Elsevier, 2019 more…
  • von Stumberg, Lukas; Wenzel, Patrick; Khan, Qadeer; Cremers, Daniel: GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization. , 2019 more…

2018

  • Aljalbout, Elie; Golkov, Vladimir; Siddiqui, Yawar; Strobel, Maximilian; Cremers, Daniel: Clustering with Deep Learning: Taxonomy and New Methods. , 2018 more…
  • 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 more…
  • 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 more…
  • 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 more…
  • 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 more…
  • Do, Binh Thanh; Golkov, Vladimir; Gurel, Goktug Erce; Cremers, Daniel: Precursor microRNA Identification Using Deep Convolutional Neural Networks. bioRxiv, 2018 more…
  • 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 more…
  • Eisenberger, Marvin; Lähner, Zorah; Cremers, Daniel: Divergence-Free Shape Interpolation and Correspondence. , 2018 more…
  • Engel, J.; Koltun, V.; Cremers, D.: Direct Sparse Odometry. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018 more…
  • Estellers, Virginia; Schmidt, Frank; Cremers, Daniel: Robust Fitting of Subdivision Surfaces for Smooth Shape Analysis. 2018 International Conference on 3D Vision (3DV), IEEE, 2018 more…
  • Gao, Xiang; Wang, Rui; Demmel, Nikolaus; Cremers, Daniel: LDSO: Direct Sparse Odometry with Loop Closure. , 2018 more…
  • Gehre, Anne; Lim, Isaak; Kobbelt, Leif: Feature Curve Co-Completion in Noisy Data. Computer Graphics Forum 37 (2), 2018, 1-12 more…
  • 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 more…
  • 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 more…
  • 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 more…
  • Jan Svoboda, Jonathan Masci, Federico Monti, Michael M. Bronstein, Leonidas Guibas: PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks. 2018 more…
  • 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 more…
  • Laehner, Zorah; Cremers, Daniel; Tung, Tony: DeepWrinkles: Accurate and Realistic Clothing Modeling. , 2018 more…
  • 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 more…
  • 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 more…
  • Ma, Lingni; Stückler, Jörg; Wu, Tao; Cremers, Daniel: Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform. , 2018 more…
  • Matsuki, Hidenobu; von Stumberg, Lukas; Usenko, Vladyslav; Stückler, Jörg; Cremers, Daniel: Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras. arXiv, 2018 more…
  • 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 more…
  • Mengyu Chu, You Xie, Jonas Mayer, Laura Leal-Taixé, Nils Thuerey: Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation. 2018 more…
  • Moeller, Michael; Cremers, Daniel: Image Denoising—Old and New. In: Denoising of Photographic Images and Video. Springer International Publishing, 2018 more…
  • 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 more…
  • Möllenhoff, Thomas; Ye, Zhenzhang; Wu, Tao; Cremers, Daniel: Combinatorial Preconditioners for Proximal Algorithms on Graphs. , 2018 more…
  • 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 more…
  • 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 more…
  • Schubert, David; Demmel, Nikolaus; Usenko, Vladyslav; Stückler, Jörg; Cremers, Daniel: Direct Sparse Odometry with Rolling Shutter. arXiv, 2018 more…
  • Schubert, David; Goll, Thore; Demmel, Nikolaus; Usenko, Vladyslav; Stückler, Jörg; Cremers, Daniel: The TUM VI Benchmark for Evaluating Visual-Inertial Odometry. arXiv, 2018 more…
  • 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 more…
  • Sommer, Christiane; Cremers, Daniel: Joint Representation of Primitive and Non-primitive Objects for 3D Vision. 2018 International Conference on 3D Vision (3DV), IEEE, 2018 more…
  • Tjaden, Henning; Schwanecke, Ulrich; Schömer, Elmar; Cremers, Daniel: A Region-based Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking. arXiv, 2018 more…
  • Usenko, Vladyslav; Demmel, Nikolaus; Cremers, Daniel: The Double Sphere Camera Model. arXiv, 2018 more…
  • 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 more…
  • Wenzel, Patrick; Khan, Qadeer; Cremers, Daniel; Leal-Taixé, Laura: Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs. , 2018 more…
  • 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 more…
  • Yang, Nan; Wang, Rui; Stückler, Jörg; Cremers, Daniel: Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry. , 2018 more…
  • von Stumberg, Lukas; Usenko, Vladyslav; Cremers, Daniel: Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization. arXiv, 2018 more…

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 more…
  • Benning, Martin; Möller, Michael; Nossek, Raz Z.; Burger, Martin; Cremers, Daniel; Gilboa, Guy; Schönlieb, Carola-Bibiane: Nonlinear Spectral Image Fusion. , 2017 more…
  • Bergmann, Paul; Wang, Rui; Cremers, Daniel: Online Photometric Calibration for Auto Exposure Video for Realtime Visual Odometry and SLAM. , 2017 more…
  • 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 more…
  • Cremers, Daniel: Computer Vision für 3-D-Rekonstruktion. Informatik-Spektrum 40 (2), 2017, 205-209 more…
  • Frerix, Thomas; Möllenhoff, Thomas; Moeller, Michael; Cremers, Daniel: Proximal Backpropagation. , 2017 more…
  • 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 more…
  • Haeusser, Philip; Frerix, Thomas; Mordvintsev, Alexander; Cremers, Daniel: Associative Domain Adaptation. , 2017 more…
  • Hazirbas, Caner; Soyer, Sebastian Georg; Staab, Maximilian Christian; Leal-Taixé, Laura; Cremers, Daniel: Deep Depth From Focus. , 2017 more…
  • Henschel, Roberto; Leal-Taixé, Laura; Cremers, Daniel; Rosenhahn, Bodo: Fusion of Head and Full-Body Detectors for Multi-Object Tracking. , 2017 more…
  • Häusser, Philip; Mordvintsev, Alexander; Cremers, Daniel: Learning by Association - A versatile semi-supervised training method for neural networks. , 2017 more…
  • 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 more…
  • 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 more…
  • 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 more…
  • 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 more…
  • 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 more…
  • Kukačka, Jan; Golkov, Vladimir; Cremers, Daniel: Regularization for Deep Learning: A Taxonomy. , 2017 more…
  • 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 more…
  • 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 more…
  • Laude, Emanuel; Wu, Tao; Cremers, Daniel: A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization. , 2017 more…
  • 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 more…
  • Ma, Lingni; Stückler, Jörg; Kerl, Christian; Cremers, Daniel: Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras. , 2017 more…
  • 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 more…
  • Maier, Robert; Schaller, Raphael; Cremers, Daniel: Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction. , 2017 more…
  • Meinhardt, Tim; Moeller, Michael; Hazirbas, Caner; Cremers, Daniel: Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems. arXiv, 2017 more…
  • Melzi, S.; Rodolà, E.; Castellani, U.; Bronstein, M. M.: Localized Manifold Harmonics for Spectral Shape Analysis. Computer Graphics Forum 37 (6), 2017, 20-34 more…
  • Mélou, Jean; Quéau, Yvain; Durou, Jean-Denis; Castan, Fabien; Cremers, Daniel: Variational Reflectance Estimation from Multi-view Images. , 2017 more…
  • 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 more…
  • Peng, Songyou; Haefner, Bjoern; Quéau, Yvain; Cremers, Daniel: Depth Super-Resolution Meets Uncalibrated Photometric Stereo. , 2017 more…
  • 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 more…
  • 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 more…
  • Quéau, Yvain; Mélou, Jean; Durou, Jean-Denis; Cremers, Daniel: Dense Multi-view 3D-reconstruction Without Dense Correspondences. , 2017 more…
  • Quéau, Yvain; Pizenberg, Mathieu; Durou, Jean-Denis; Cremers, Daniel: Microgeometry capture and RGB albedo estimation by photometric stereo without demosaicing. SPIE Proceedings, SPIE, 2017 more…
  • 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 more…
  • Quéau, Yvain; Wu, Tao; Cremers, Daniel: Semi-calibrated Near-Light Photometric Stereo. In: Lecture Notes in Computer Science. Springer International Publishing, 2017 more…
  • Rodolà, E.; Moeller, M.; Cremers, D.: Regularized Pointwise Map Recovery from Functional Correspondence. Computer Graphics Forum 36 (8), 2017, 700-711 more…
  • 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 more…
  • 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 more…
  • 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 more…
  • 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 more…
  • Wang, Rui; Schwörer, Martin; Cremers, Daniel: Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras. , 2017 more…

2016

  • Bernard, Florian; Schmidt, Frank R.; Thunberg, Johan; Cremers, Daniel: A Combinatorial Solution to Non-Rigid 3D Shape-to-Image Matching. arXiv, 2016 more…
  • Boscaini, D.; Masci, J.; Rodolà, E.; Bronstein, M. M.; Cremers, D.: Anisotropic Diffusion Descriptors. Computer Graphics Forum 35 (2), 2016, 431-441 more…
  • 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 more…
  • 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 more…
  • 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 more…
  • Dzitsiuk, Maksym; Sturm, Jürgen; Maier, Robert; Ma, Lingni; Cremers, Daniel: De-noising, Stabilizing and Completing 3D Reconstructions On-the-go using Plane Priors. , 2016 more…
  • Geiping, Jonas; Dirks, Hendrik; Cremers, Daniel; Moeller, Michael: Multiframe Motion Coupling for Video Super Resolution. , 2016 more…
  • Litany, O.; Rodolà, E.; Bronstein, A. M.; Bronstein, M. M.; Cremers, D.: Non-Rigid Puzzles. Computer Graphics Forum 35 (5), 2016, 135-143 more…
  • 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 more…
  • 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 more…
  • Lähner, Zorah; Rodolà, Emanuele; Schmidt, Frank R.; Bronstein, Michael M.; Cremers, Daniel: Efficient Globally Optimal 2D-to-3D Deformable Shape Matching. , 2016 more…
  • Möllenhoff, Thomas; Cremers, Daniel: Sublabel-Accurate Discretization of Nonconvex Free-Discontinuity Problems. , 2016 more…
  • Rodolà, E.; Cosmo, L.; Bronstein, M. M.; Torsello, A.; Cremers, D.: Partial Functional Correspondence. Computer Graphics Forum 36 (1), 2016, 222-236 more…
  • Vestner, Matthias; Litman, Roee; Bronstein, Alex; Rodolà, Emanuele; Cremers, Daniel: Bayesian Inference of Bijective Non-Rigid Shape Correspondence. , 2016 more…
  • 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 more…
  • von Stumberg, Lukas; Usenko, Vladyslav; Engel, Jakob; Stückler, Jörg; Cremers, Daniel: From Monocular SLAM to Autonomous Drone Exploration. arXiv, 2016 more…

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 more…
  • Mecca, R.; Rodolà, E.; Cremers, D.: Realistic photometric stereo using partial differential irradiance equation ratios. Computers & Graphics 51, 2015, 8-16 more…
  • 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 more…
  • 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 more…
  • Rodolà, Emanuele; Cosmo, Luca; Bronstein, Michael M.; Torsello, Andrea; Cremers, Daniel: Partial Functional Correspondence. , 2015 more…
  • Rodolà, Emanuele; Moeller, Michael; Cremers, Daniel: Point-wise Map Recovery and Refinement from Functional Correspondence. , 2015 more…