2023 - Research.com Computer Science in Germany Leader Award
Andreas Geiger mainly focuses on Artificial intelligence, Computer vision, Object detection, Visual odometry and Robustness. The study of Artificial intelligence is intertwined with the study of Pattern recognition in a number of ways. Many of his studies on Computer vision apply to Benchmark as well.
Andreas Geiger studied Visual odometry and Odometry that intersect with Computer stereo vision, Frame rate, Template matching and Stereo camera. Andreas Geiger has researched Robustness in several fields, including Image sensor, Image registration and Grayscale. His studies deal with areas such as Optical flow and Robotics as well as Stereo cameras.
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, 3D reconstruction, Pattern recognition and Segmentation. His research on Artificial intelligence frequently connects to adjacent areas such as Machine learning. While the research belongs to areas of Computer vision, Andreas Geiger spends his time largely on the problem of Visual odometry, intersecting his research to questions surrounding Odometry, Motion planning and Stereo cameras.
The study incorporates disciplines such as Voxel, Surface, Probabilistic logic and Iterative reconstruction in addition to 3D reconstruction. His Pattern recognition study integrates concerns from other disciplines, such as RGB color model, Artificial neural network, Representation and Invariant. The Optical flow estimation research Andreas Geiger does as part of his general Optical flow study is frequently linked to other disciplines of science, such as Discrete optimization, therefore creating a link between diverse domains of science.
Andreas Geiger mostly deals with Artificial intelligence, Computer vision, Object, Rendering and 3D reconstruction. His Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition. His study ties his expertise on Representation together with the subject of Computer vision.
Andreas Geiger has included themes like Voxel and Pose in his Rendering study. His work in 3D reconstruction addresses subjects such as Iterative reconstruction, which are connected to disciplines such as Implicit function and Polygon mesh. His Benchmark study combines topics from a wide range of disciplines, such as Robotics and Synthetic data.
His primary scientific interests are in Artificial intelligence, Computer vision, 3D reconstruction, Task analysis and Pattern recognition. His Artificial intelligence study frequently draws connections between related disciplines such as State. His Computer vision research is multidisciplinary, incorporating elements of Representation and Generative model.
His work carried out in the field of 3D reconstruction brings together such families of science as Point cloud, Iterative reconstruction and Pattern recognition. His Point cloud research is multidisciplinary, relying on both Contrast, Robotics and Synthetic data. Video tracking and Data mining is closely connected to Benchmark in his research, which is encompassed under the umbrella topic of Pattern recognition.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Are we ready for autonomous driving? The KITTI vision benchmark suite
Andreas Geiger;Philip Lenz;Raquel Urtasun.
computer vision and pattern recognition (2012)
Vision meets robotics: The KITTI dataset
A Geiger;P Lenz;C Stiller;R Urtasun.
The International Journal of Robotics Research (2013)
Object scene flow for autonomous vehicles
Moritz Menze;Andreas Geiger.
computer vision and pattern recognition (2015)
StereoScan: Dense 3d reconstruction in real-time
Andreas Geiger;Julius Ziegler;Christoph Stiller.
ieee intelligent vehicles symposium (2011)
OctNet: Learning Deep 3D Representations at High Resolutions
Gernot Riegler;Ali Osman Ulusoy;Andreas Geiger.
computer vision and pattern recognition (2017)
Efficient large-scale stereo matching
Andreas Geiger;Martin Roser;Raquel Urtasun.
asian conference on computer vision (2010)
Occupancy Networks: Learning 3D Reconstruction in Function Space
Lars Mescheder;Michael Oechsle;Michael Niemeyer;Sebastian Nowozin.
computer vision and pattern recognition (2019)
Which Training Methods for GANs do actually Converge
Lars M. Mescheder;Andreas Geiger;Sebastian Nowozin.
international conference on machine learning (2018)
A new performance measure and evaluation benchmark for road detection algorithms
Jannik Fritsch;Tobias Kuhnl;Andreas Geiger.
international conference on intelligent transportation systems (2013)
Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme
Bernd Kitt;Andreas Geiger;Henning Lategahn.
ieee intelligent vehicles symposium (2010)
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