Computer Vision Research Group

Members

arXiv Feed

  • Rußwurm, Marc; Lefèvre, Sébastien; Courty, Nicolas; Emonet, Rémi; Körner, Marco; Tavenard, Romain: End-to-end Learning for Early Classification of Time Series. arXiv preprint arXiv:1901.10681, 2019 more…
  • Liebel, Lukas; Körner, Marco: Auxiliary Tasks in Multi-task Learning. arXiv preprint arXiv:1805.06334, 2018 more…
  • Azimi, Seyed Majid; Vig, Eleonora; Bahmanyar, Reza; Körner, Marco; Reinartz, Peter: Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery. arXiv preprint arXiv:1807.02700, 2018 more… Full text ( DOI )

Projects

Single-Image Depth Estimation

The generation of depth maps is essential for numerous applications, such as autonomous driving or augmented reality. Typically these are generated from stereo image pairs or by making use of active sensors (e.g. LiDAR or RGB-D cameras). Based on the monocular depth perception of humans, this project investigates the estimation of depth maps from single images using artificial neural networks .

MAV 4 BIM

This project investigates the usability of image sequences from Micro Aerial Vehicles (MAVs) for generating complete and high resolutional 3D building information models (BIMs). Beside the modelling of the building exterior in a global reference frame, the interior should be reconstructed from independent indoor flights as well. An automatic alignment of the reconstructed indoor and outdoor building models offer the generation of LOD-4 building models.

CNN-based Large-Scale Land Use and Land Cover Classification

This project aims at adopting classification methods based on Convolutional Neural Networks for Land Use and Land Cover classification. Publicly available geodata (OpenStreetMap) and multi-spectral Sentinel-2 imagery is used as training data.

Prediction of Image Sequences from Driver Assistance Systems by implicit modelling of Activity Patterns

Safe autonomous driving systems rely on autonomous vehicles that are able to drive anticipatory. This project aims at simulating the human anticipation of future scenarios by predicting traffic events from generated video frames. We build up on state of the art methods in Computer Vision and Machine Learning to generate prospective frames of a video based on the latest observed video sequence. Features learned by the model are further used for modeling and analyzing traffic scenes and activity patterns of traffic participants.

Traffic Infrastructure Modeling

Modeling the traffic infrastructure based on aerial and ground images becomes ever more important, especially since autonomous driving systems seem to be a part of the near future. This project aims to develop algorithms to process images captured by dash cams mounted on top of a car in order to detect traffic relevant objects, such as traffic participants and all infrastructure elements. In this context, we also use aerial images to analyze group behavior and predict traffic actions.

Publications

  • Aigner, Sandra; Körner, Marco: FutureGAN: Anticipating the Future Frames of Video Sequences using Spatio-Temporal 3d Convolutions in Progressively Growing GANs. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS) (tbp), 2019 more…
  • Hödel, Max; Koch, Tobias; Hoegner, Ludwig; Stilla, Uwe: Monocular-Depth Assisted Semi-Global-Matching. International Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS) (tbp), 2019 more…
  • Koch, Tobias; Körner, Marco; Fraundorfer, Friedrich: Automatic and Semantically-Aware 3D UAV Flight Planning for Image-Based 3D Reconstruction. Remote Sensing 11 (13), 2019 more…
  • Koch, Tobias; Liebel, Lukas; Fraundorfer, Friedrich; Körner, Marco: Evaluation of CNN-Based Single-Image Depth Estimation Methods. Proceedings of the European Conference on Computer Vision Workshops (ECCV-WS), Springer International Publishing, 2019 more…
  • König, Peter; Aigner, Sandra; Körner, Marco: Enhancing Traffic Scene Predictions with Generative Adversarial Networks. Proceedings of the IEEE Intelligent Transportation Systems Conference (ITSC), 2019 more…
  • Rußwurm, Marc; Lefèvre, Sébastien; Courty, Nicolas; Emonet, Rémi; Körner, Marco; Tavenard, Romain: End-to-end Learning for Early Classification of Time Series. arXiv preprint arXiv:1901.10681, 2019 more…
  • Liebel, Lukas; Körner, Marco: Auxiliary Tasks in Multi-task Learning. arXiv preprint arXiv:1805.06334, 2018 more…
  • Azimi, Seyed Majid; Vig, Eleonora; Bahmanyar, Reza; Körner, Marco; Reinartz, Peter: Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery. arXiv preprint arXiv:1807.02700, 2018 more…
  • Azimi, Seyed Majid; Fischer, Peter; Körner, Marco; Reinartz, Peter: Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 2018 more…
  • Rußwurm, Marc; Körner, Marco: Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders. ISPRS International Journal of Geo-Information 7 (4), 2018, 129 more…