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Hendrik Schilling - IEEE Xplore Author Profile
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Hendrik Schilling

Publications
12
Citations
194
Publications by Year
20122020
Co-Authors:
Show All Co-Authors (46)
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Hendrik Schilling

Affiliation

rabbitAI, Heidelberg

Publication Topics

Biography

Hendrik Schilling was born on February 6, 1985, in Duderstadt, Germany. He received the diploma degree in physics from the Karlsruhe Institute of Technology, Karlsruhe, Germany, in 2011, and is currently working on his Ph.D. in geodesy and geoinformatics at the Insitute for Photogrammetry, University of Stuttgart, Stuttgart, Germany.,Since 2011, he was a Researcher with the Department of Scene Analysis, Fraunhofer Institute of Optronics, System Technologies, and Image Exploitation, Karlsruhe, Germany. His main areas of research include object detection, data fusion, and hyperspectral data analysis.(Based on document published on 24 May 2018).
Publications
12
Citations
194
Publications by Year
20122020
Co-Authors:
Show All Co-Authors (46)
Author's Published Works

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Mind the Gap - A Benchmark for Dense Depth Prediction Beyond Lidar

Hendrik Schilling;Marcel Gutsche;Alexander Brock;Dane Späth;Carsten Rother;Karsten Krispin

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
The large interest in autonomous vehicles is a significant driver for computer vision research. Current deep learning approaches are capable of impressive feats, like dense full frame depth prediction from a single image. While impressive results have been achieved, it is not yet clear if they are sufficient for autonomous driving. The problem remains that existing evaluation benchmarks and metric...Show More

Mind the Gap - A Benchmark for Dense Depth Prediction Beyond Lidar

Hendrik Schilling;Marcel Gutsche;Alexander Brock;Dane Späth;Carsten Rother;Karsten Krispin

Year: 2020 | Conference Paper |

Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift

Titus Leistner;Hendrik Schilling;Radek Mackowiak;Stefan Gumhold;Carsten Rother

2019 International Conference on 3D Vision (3DV)
Year: 2019 | Conference Paper |
Cited by: Papers (29)
We propose a method for depth estimation from light field data, based on a fully convolutional neural network architecture. Our goal is to design a pipeline which achieves highly accurate results for small-and wide-baseline light fields. Since light field training data is scarce, all learning-based approaches use a small receptive field and operate on small disparity ranges. In order to work with ...Show More

Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift

Titus Leistner;Hendrik Schilling;Radek Mackowiak;Stefan Gumhold;Carsten Rother

Year: 2019 | Conference Paper |

Trust your Model: Light Field Depth Estimation with Inline Occlusion Handling

Hendrik Schilling;Maximilian Diebold;Carsten Rother;Bernd Jähne

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Year: 2018 | Conference Paper |
Cited by: Papers (47)
We address the problem of depth estimation from light-field images. Our main contribution is a new way to handle occlusions which improves general accuracy and quality of object borders. In contrast to all prior work we work with a model which directly incorporates both depth and occlusion, using a local optimization scheme based on the PatchMatch algorithm. The key benefit of this joint approach ...Show More

Trust your Model: Light Field Depth Estimation with Inline Occlusion Handling

Hendrik Schilling;Maximilian Diebold;Carsten Rother;Bernd Jähne

Year: 2018 | Conference Paper |

Detection of Vehicles in Multisensor Data via Multibranch Convolutional Neural Networks

Hendrik Schilling;Dimitri Bulatov;Robin Niessner;Wolfgang Middelmann;Uwe Soergel

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year: 2018 | Volume: 11, Issue: 11 | Journal Article |
Cited by: Papers (28)
Convolutional neural networks, or CNNs, raised the bar for most computer vision problems and have an increasing impact in remote sensing. However, since they usually contain multiple pooling layers, detection of exact borders of small objects at their original resolution remains yet a challenging topic. Additionally, efforts are being made to reduce the amount of training data. In this paper, we i...Show More

Detection of Vehicles in Multisensor Data via Multibranch Convolutional Neural Networks

Hendrik Schilling;Dimitri Bulatov;Robin Niessner;Wolfgang Middelmann;Uwe Soergel

Year: 2018 | Volume: 11, Issue: 11 | Journal Article |

Surface Normal Reconstruction from Specular Information in Light Field Data

Marcel Gutsche;Hendrik Schilling;Maximilian Diebold;Christoph Garbe

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2017 | Conference Paper |
Cited by: Papers (2)
Specular highlights provide information about the shape of an object. Its characteristics are mostly unwanted in computer vision due to violation of the Lambertian assumption, which most algorithms require. Instead of neglecting this ubiquitous phenomenon we harvest it to extract surface normals with very high accuracy. Compared to photometric stereo our method works with multiple views and a fixe...Show More

Surface Normal Reconstruction from Specular Information in Light Field Data

Marcel Gutsche;Hendrik Schilling;Maximilian Diebold;Christoph Garbe

Year: 2017 | Conference Paper |

A Taxonomy and Evaluation of Dense Light Field Depth Estimation Algorithms

Ole Johannsen;Katrin Honauer;Bastian Goldluecke;Anna Alperovich;Federica Battisti;Yunsu Bok;Michele Brizzi;Marco Carli;Gyeongmin Choe;Maximilian Diebold;Marcel Gutsche;Hae-Gon Jeon;In So Kweon;Jaesik Park;Jinsun Park;Hendrik Schilling;Hao Sheng;Lipeng Si;Michael Strecke;Antonin Sulc;Yu-Wing Tai;Qing Wang;Ting-Chun Wang;Sven Wanner;Zhang Xiong;Jingyi Yu;Shuo Zhang;Hao Zhu

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2017 | Conference Paper |
Cited by: Papers (62)
This paper presents the results of the depth estimation challenge for dense light fields, which took place at the second workshop on Light Fields for Computer Vision (LF4CV) in conjunction with CVPR 2017. The challenge consisted of submission to a recent benchmark [7], which allows a thorough performance analysis. While individual results are readily available on the benchmark web page http://www....Show More

A Taxonomy and Evaluation of Dense Light Field Depth Estimation Algorithms

Ole Johannsen;Katrin Honauer;Bastian Goldluecke;Anna Alperovich;Federica Battisti;Yunsu Bok;Michele Brizzi;Marco Carli;Gyeongmin Choe;Maximilian Diebold;Marcel Gutsche;Hae-Gon Jeon;In So Kweon;Jaesik Park;Jinsun Park;Hendrik Schilling;Hao Sheng;Lipeng Si;Michael Strecke;Antonin Sulc;Yu-Wing Tai;Qing Wang;Ting-Chun Wang;Sven Wanner;Zhang Xiong;Jingyi Yu;Shuo Zhang;Hao Zhu

Year: 2017 | Conference Paper |

Segmentation methods for detection of stationary vehicles in combined elevation and optical data

Dimitri Bulatov;Hendrik Schilling

2016 23rd International Conference on Pattern Recognition (ICPR)
Year: 2016 | Conference Paper |
Cited by: Papers (1)
Detection of vehicles in remote sensing data represents a captivating and challenging task that has been studied during many years. The state-of-the-art detection tools can be subdivided into implicit and explicit methods; the latter ones provide detection results by means of some explicitly characterizing features. Mostly, these methods rely on optical aerial images in which vehicles appear disto...Show More

Segmentation methods for detection of stationary vehicles in combined elevation and optical data

Dimitri Bulatov;Hendrik Schilling

Year: 2016 | Conference Paper |

Improving semantic orthophotos by a fast method based on harmonic inpainting

Benedikt Kottler;Dimitri Bulatov;Hendrik Schilling

2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS)
Year: 2016 | Conference Paper |
Cited by: Papers (2)
Context-based modeling of 3D urban terrain has become increasingly popular in the last two decades. Typically, orthophotos are used for texturing ground. In order to increase locational awareness, it is useful to eliminate from the orthophotos those object instances which frequently appear and disappear in the terrain. Vehicles are good examples of such instances. Assuming that vehicles were detec...Show More

Improving semantic orthophotos by a fast method based on harmonic inpainting

Benedikt Kottler;Dimitri Bulatov;Hendrik Schilling

Year: 2016 | Conference Paper |

Evaluation and performance analysis of hydrocarbon detection methods using hyperspectral data

Andreas Lenz;Hendrik Schilling;Wolfgang Gross;Wolfgang Middelmann

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Year: 2015 | Conference Paper |
Cited by: Papers (7)
Different methods for the detection for hydrocarbons in aerial hyperspectral images are analyzed in this study. The scope is to find a practical method for airborne oil spill mapping on land. Examined are Hydrocarbon index and Hydrocarbon detection index. As well as spectral reidentification algorithms, like Spectral angle mapper, in comparison to the indices. The influence of different ground cov...Show More

Evaluation and performance analysis of hydrocarbon detection methods using hyperspectral data

Andreas Lenz;Hendrik Schilling;Wolfgang Gross;Wolfgang Middelmann

Year: 2015 | Conference Paper |

Automatic in-flight boresight calibration considering topography for hyperspectral pushbroom sensors

Andreas Lenz;Hendrik Schilling;Dominik Perpeet;Sebastian Wuttke;Wolfgang Gross;Wolfgang Middelmann

2014 IEEE Geoscience and Remote Sensing Symposium
Year: 2014 | Conference Paper |
Cited by: Papers (13)
This paper suggests a method for automatic in-flight boresight calibration of pushbroom scanner images, using an on-line system with broadband data downlink and near realtime georeferencing of the pushbroom image data. Georeferencing accuracy may decrease during long image acquisition flights due to instable atmospheric conditions, which may lead to geometric changes in the flight platform. Orthor...Show More

Automatic in-flight boresight calibration considering topography for hyperspectral pushbroom sensors

Andreas Lenz;Hendrik Schilling;Dominik Perpeet;Sebastian Wuttke;Wolfgang Gross;Wolfgang Middelmann

Year: 2014 | Conference Paper |

Automatic modeling of nonlinear signal source variations in hyperspectral data

Wolfgang Gross;Göksu Keskin;Hendrik Schilling;Andreas Lenz;Wolfgang Middelmann

2014 IEEE Geoscience and Remote Sensing Symposium
Year: 2014 | Conference Paper |
Nonlinear effects in hyperspectral data complicate classification and other data analysis procedures. Transforming the data onto manifolds can help to improve the results while simultaneously reducing the dimensionality due to the high correlation among the spectral bands. Methods like ISOMAP or Locally Linear Embedding are not ideal when the data is degraded by noise. In this paper, a method is i...Show More

Automatic modeling of nonlinear signal source variations in hyperspectral data

Wolfgang Gross;Göksu Keskin;Hendrik Schilling;Andreas Lenz;Wolfgang Middelmann

Year: 2014 | Conference Paper |

An approach to fully unsupervised hyperspectral unmixing

Wolfgang Gross;Hendrik Schilling;Wolfgang Middelmann

2012 IEEE International Geoscience and Remote Sensing Symposium
Year: 2012 | Conference Paper |
In the last few years, unmixing of hyperspectral data has become of major importance. The high spectral resolution results in a loss of spatial resolution. Thus, spectra of edges and small objects are composed of mixtures of their neighboring materials. Due to the fact that supervised unmixing is impossible for extensive data sets, the unsupervised Nonnegative Matrix Factorization (NMF) is used to...Show More

An approach to fully unsupervised hyperspectral unmixing

Wolfgang Gross;Hendrik Schilling;Wolfgang Middelmann

Year: 2012 | Conference Paper |

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