Introduction
Previously, I studied Electrical Engineering and Information Technology (BSc and MSc) at Leibniz University Hannover, Germany, spent a semester abroad at Yonsei University in Seoul, and visited Sony R&D in Tokyo for an internship.
News
- 01 March 2021
Paper accepted at CVPR 2021
Our paper Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images was accepted for publication at CVPR 2021!
- 14 June 2020
Meet me (virtually) at CVPR 2020
I am attending this year’s virtual CVPR conference this week. You can meet me at the Q&A sessions for our CONSAC paper: Tuesday, June 16, 2020, 16:00-18:00 and 04:00-06:00 (PDT), Session: Poster 1.4, Poster No. 94
- 24 February 2020
Paper accepted at CVPR 2020
Our paper CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus was accepted for publication at CVPR 2020!
- 22 January 2020
Paper accepted at ICRA 2020
Our paper Temporally Consistent Horizon Lines was accepted for publication at ICRA 2020!
Publications
Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images
Florian Kluger, Hanno Ackermann, Eric Brachmann, Michael Ying Yang, Bodo Rosenhahn
CVPR 2021
Paper , Code , Poster , Video
3D primitive fitting to RGB images based on a neural-guided end-to-end trainable multi-model RANSAC estimator.
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus
Florian Kluger, Eric Brachmann, Hanno Ackermann, Carsten Rother, Michael Ying Yang, Bodo Rosenhahn
CVPR 2020
Paper , Code , Poster , Video
We present the first learning-based method for robust multi-model fitting and achieve state-of-the-art for homography and vanishing point estimation.
Temporally Consistent Horizon Lines
Florian Kluger, Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn
ICRA 2020
Paper , Code
We present a novel CNN architecture with an improved residual convolutional LSTM for temporally consistent horizon line estimation, which consistently achieves superior performance compared with existing methods.
Region-based Cycle-Consistent Data Augmentation for Object Detection
Florian Kluger, Christoph Reinders, Kevin Raetz, Philipp Schelske, Bastian Wandt, Hanno Ackermann, Bodo Rosenhahn
IEEE Big Data Workshops 2018
Paper , Code
Winner of the Special Price for proposing innovative ideas and contributing to the Road Damage Detection and Classification Workshop at IEEE Big Data 2018.
Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection
Florian Kluger, Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn
GCPR 2017
Paper , Code
We present a novel CNN-based approach for vanishing point detection from uncalibrated monocular images, which achieves competitive performance on three horizon estimation benchmark datasets.