no code implementations • 4 Nov 2022 • Risto Vuorio, Johann Brehmer, Hanno Ackermann, Daniel Dijkman, Taco Cohen, Pim de Haan
Standard imitation learning can fail when the expert demonstrators have different sensory inputs than the imitating agent.
no code implementations • NeurIPS 2021 • Farhad Ghazvinian Zanjani, Ilia Karmanov, Hanno Ackermann, Daniel Dijkman, Simone Merlin, Max Welling, Fatih Porikli
This work presents a data-driven approach for the indoor localization of an observer on a 2D topological map of the environment.
1 code implementation • ICCV 2021 • Yuren Cong, Wentong Liao, Hanno Ackermann, Bodo Rosenhahn, Michael Ying Yang
Compared to the task of scene graph generation from images, it is more challenging because of the dynamic relationships between objects and the temporal dependencies between frames allowing for a richer semantic interpretation.
1 code implementation • CVPR 2021 • Florian Kluger, Hanno Ackermann, Eric Brachmann, Michael Ying Yang, Bodo Rosenhahn
A RANSAC estimator guided by a neural network fits these primitives to 3D features, such as a depth map.
1 code implementation • ECCV 2020 • Cong Yuren, Hanno Ackermann, Wentong Liao, Michael Ying Yang, Bodo Rosenhahn
Detected objects, their labels and the discovered relations can be used to construct a scene graph which provides an abstract semantic interpretation of an image.
Ranked #8 on Scene Graph Generation on Visual Genome
3 code implementations • CVPR 2020 • Florian Kluger, Eric Brachmann, Hanno Ackermann, Carsten Rother, Michael Ying Yang, Bodo Rosenhahn
We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements.
no code implementations • 26 Aug 2019 • Maren Awiszus, Hanno Ackermann, Bodo Rosenhahn
We use face images as our example of choice.
1 code implementation • 23 Jul 2019 • Florian Kluger, Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn
The horizon line is an important geometric feature for many image processing and scene understanding tasks in computer vision.
Ranked #1 on Horizon Line Estimation on KITTI Horizon
no code implementations • 30 Apr 2019 • Sami S. Brandt, Hanno Ackermann
The right singular vectors are affinely back-projected into the 3D space, and the affine back-projections will also be solved as part of the factorisation.
no code implementations • 22 Nov 2018 • Sami Sebastian Brandt, Hanno Ackermann, Stella Grasshof
The word general refers to an approach that recovers the non-rigid affine structure and motion from 2D point correspondences by assuming that (1) the non-rigid shapes are generated by a linear combination of rigid 3D basis shapes, (2) that the non-rigid shapes are affine in nature, i. e., they can be modelled as deviations from the mean, rigid shape, (3) and that the basis shapes are statistically independent.
no code implementations • 18 Sep 2017 • Christoph Reinders, Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn
These algorithms are usually trained on large datasets consisting of thousands or millions of labeled training examples.
2 code implementations • 8 Jul 2017 • Florian Kluger, Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn
We present a novel approach for vanishing point detection from uncalibrated monocular images.
Ranked #3 on Horizon Line Estimation on York Urban Dataset
no code implementations • 1 Feb 2017 • Bastian Wandt, Hanno Ackermann, Bodo Rosenhahn
This paper deals with motion capture of kinematic chains (e. g. human skeletons) from monocular image sequences taken by uncalibrated cameras.
no code implementations • 24 Jan 2017 • Michael Ying Yang, Hanno Ackermann, Weiyao Lin, Sitong Feng, Bodo Rosenhahn
In this paper, we propose a new framework for segmenting feature-based moving objects under affine subspace model.
no code implementations • IEEE Transactions on Pattern Analysis and Machine Intelligence 2016 • Pulak Purkait, Tat-Jun Chin, Hanno Ackermann, David Suter
The extension of conventional clustering to hypergraph clustering, which involves higher order similarities instead of pairwise similarities, is increasingly gaining attention in computer vision.
no code implementations • 19 Sep 2016 • Michael Ying Yang, Wentong Liao, Hanno Ackermann, Bodo Rosenhahn
In contrast to previous methods for extracting support relations, the proposed approach generates more accurate results, and does not require a pixel-wise semantic labeling of the scene.
no code implementations • 16 Sep 2016 • Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn
If these unknown subspaces are well-separated this algorithm is guaranteed to succeed.