Introduction

Hey there! I am a fourth year PhD student supervised by Bodo Rosenhahn at the Institut für Informationsverarbeitung. My main research focus lies on Machine Learning and its applications in Computer Vision, specifically 3D Scene Understanding and Geometric Feature Extraction.
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

Publications

Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images

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

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

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

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

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.

For a complete list of publications, please have a look at my Google Scholar page.