Position within the page tree

Institute for Visualization and Interactive Systems
Institute
Team
Lukas Mehl

Lukas Mehl

M. Sc.

Doctoral Researcher
Institute for Visualization and Interactive Systems (VIS)
Research Group Computer Vision

Contact

+49 711 685 88325
Email

Universitätsstraße 38
70569 Stuttgart
Germany
Room: 1.465

Publications

2022
1. Schmalfuss, J., Mehl, L., & Bruhn, A. (2022). Attacking Motion Estimation with Adversarial Snow. ECCV 2022 Workshop on Adversarial Robustness in the Real World (AROW).
  - BibTeX
  BibTeX
  @conference{schmalfuss2022attacking, author = {Schmalfuss, Jenny and Mehl, Lukas and Bruhn, Andrés}, booktitle = {ECCV 2022 Workshop on Adversarial Robustness in the Real World (AROW)}, title = {Attacking Motion Estimation with Adversarial Snow}, year = 2022 }
2. Jahedi, A., Mehl, L., Rivinius, M., & Bruhn, A. (2022). Multi-Scale RAFT: combining hierarchical concepts for learning-based optical flow estimation. IEEE International Conference on Image Processing (ICIP). https://doi.org/10.48550/arXiv.2207.12163
  - BibTeX
  - Link
  BibTeX
  @inproceedings{Jahedi2022_ICIP, abstract = {Many classical and learning-based optical flow methods rely on hierarchical concepts to improve both accuracy and robustness. However, one of the currently most successful approaches -- RAFT -- hardly exploits such concepts. In this work, we show that multi-scale ideas are still valuable. More precisely, using RAFT as a baseline, we propose a novel multi-scale neural network that combines several hierarchical concepts within a single estimation framework. These concepts include (i) a partially shared coarse-to-fine architecture, (ii) multi-scale features, (iii) a hierarchical cost volume and (iv) a multi-scale multi-iteration loss. Experiments on MPI Sintel and KITTI clearly demonstrate the benefits of our approach. They show not only substantial improvements compared to RAFT, but also state-of-the-art results -- in particular in non-occluded regions.}, author = {Jahedi, Azin and Mehl, Lukas and Rivinius, Marc and Bruhn, Andrés}, booktitle = {IEEE International Conference on Image Processing (ICIP)}, doi = {10.48550/arXiv.2207.12163}, title = {Multi-Scale {RAFT}: combining hierarchical concepts for learning-based optical flow estimation}, url = {/brokenurl# https://doi.org/10.48550/arXiv.2207.12163}, year = 2022 }
  Link
  /brokenurl# https://doi.org/10.48550/arXiv.2207.12163
2021
1. Mehl, L., Beschle, C., Barth, A., & Bruhn, A. (2021). An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation. Proceedings of the International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), 140--152. https://doi.org/10.1007/978-3-030-75549-2_12
  - BibTeX
  - Link
  BibTeX
  @inproceedings{10.1007/978-3-030-75549-2_12, abstract = {Approaches based on order-adaptive regularisation belong to the most accurate variational methods for computing the optical flow. By locally deciding between first- and second-order regularisation, they are applicable to scenes with both fronto-parallel and ego-motion. So far, however, existing order-adaptive methods have a decisive drawback. While the involved first- and second-order smoothness terms already make use of anisotropic concepts, the underlying selection process itself is still isotropic in that sense that it locally chooses the same regularisation order for all directions. In our paper, we address this shortcoming. We propose a generalised order-adaptive approach that allows to select the local regularisation order for each direction individually. To this end, we split the order-adaptive regularisation across and along the locally dominant direction and perform an energy competition for each direction separately. This in turn offers another advantage. Since the parameters can be chosen differently for both directions, the approach allows for a better adaption to the underlying scene. Experiments for MPI Sintel and KITTI 2015 demonstrate the usefulness of our approach. They not only show improvements compared to an isotropic selection scheme. They also make explicit that our approach is able to improve the results from state-of-the-art learning-based approaches, if applied as a final refinement step – thereby achieving top results in both benchmarks.}, author = {Mehl, Lukas and Beschle, Cedric and Barth, Andrea and Bruhn, Andrés}, booktitle = {Proceedings of the International Conference on Scale Space and Variational Methods in Computer Vision (SSVM)}, doi = {10.1007/978-3-030-75549-2_12}, pages = {140--152}, publisher = {Springer}, title = {An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation}, url = {https://link.springer.com/chapter/10.1007%2F978-3-030-75549-2_12}, year = 2021 }
  Link
  https://link.springer.com/chapter/10.1007%2F978-3-030-75549-2_12

Teaching

Summer term 2022

Tutorial Correspondence Problems in Computer Vision (Coordination)
Seminar Recent Advances in Computer Vision (Supervision)

Winter term 2021/2022

Lecture Computer Vision (Coordination)

Summer term 2021

Tutorial Correspondence Problems in Computer Vision (Tutor)

Winter term 2020/2021

Tutorial Correspondence Problems in Computer Vision (Tutor)
Seminar Bildverarbeitung und Computer Vision (Supervision)

Summer term 2020

Tutorial Correspondence Problems in Computer Vision (Tutor)
Seminar Recent Advances in Computer Vision (Supervision)

Supervised Theses and Projects

Multi-Frame Approaches for Learning Optical Flow Predictions: Master's Thesis, 2020. (co-supervision)
Zwischenbildinterpolation mit optischem Fluss: Bachelor's Thesis, 2020. (co-supervision)
Occlusion-Aware Variational Optical Flow Refinement: Master's Thesis, 2021.
Motion-compensated Frame Interpolation using Multiple Frames: Bachelor's Thesis, 2021.
Diffusion-Based Refinement of Optical Flow: Bachelor's Thesis, 2021.
Pre-training ProFlow: Research Project, 2022.

Team page

Lukas Mehl

Contact

Publications

2022

BibTeX

BibTeX

Link

2021

BibTeX

Link

Teaching

Supervised Theses and Projects

Audience

Formalities

Services

Organization

Lukas Mehl

Contact

Publications

2022

BibTeX

BibTeX

Link

2021

BibTeX

Link

Teaching

Supervised Theses and Projects

Here you can reach us

Audience

Formalities

Services

Organization