Dieses Bild zeigt Lukas Mehl

Lukas Mehl

Herr M. Sc.

Wiss. Mitarbeiter
Institut für Visualisierung und Interaktive Systeme (VIS)
Abteilung Computer Vision

Kontakt

Universitätsstraße 38
70569 Stuttgart
Deutschland
Raum: 1.465

  1. 2023

    1. Mehl, L., Schmalfuss, J., Jahedi, A., Nalivayko, Y., & Bruhn, A. (2023). Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4981–4991. https://openaccess.thecvf.com/content/CVPR2023/html/Mehl_Spring_A_High-Resolution_High-Detail_Dataset_and_Benchmark_for_Scene_Flow_CVPR_2023_paper.html
    2. Schmalfuss, J., Mehl, L., & Bruhn, A. (2023). Distracting Downpour: Adversarial Weather Attacks for Motion Estimation. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 10106–10116. https://openaccess.thecvf.com/content/ICCV2023/html/Schmalfuss_Distracting_Downpour_Adversarial_Weather_Attacks_for_Motion_Estimation_ICCV_2023_paper.html
    3. Mehl, L., Jahedi, A., Schmalfuss, J., & Bruhn, A. (2023, Januar). M-FUSE: Multi-frame Fusion for Scene Flow Estimation. Proc. Winter Conference on Applications of Computer Vision (WACV). https://doi.org/10.48550/arXiv.2207.05704
    4. Mehl, L., Jahedi, A., Schmalfuß, J., & Bruhn, A. (2023, Januar). M-FUSE: Multi-frame Fusion for Scene Flow Estimation. Proc. Winter Conference on Applications of Computer Vision (WACV). https://doi.org/10.48550/arXiv.2207.05704
    5. Jahedi, A., Luz, M., Rivinius, M., Mehl, L., & Bruhn, A. (2023). MS-RAFT+: High Resolution Multi-Scale RAFT. International Journal of Computer Vision, 1573–1405. https://doi.org/10.1007/s11263-023-01930-7
  2. 2022

    1. Schmalfuß, J., Mehl, L., & Bruhn, A. (2022). Attacking Motion Estimation with Adversarial Snow. Proc. ECCV Workshop on Adversarial Robustness in the Real World (AROW). /brokenurl#ttps://arxiv.org/abs/2210.11242
    2. Schmalfuss, J., Mehl, L., & Bruhn, A. (2022). Attacking Motion Estimation with Adversarial Snow. Proc. ECCV Workshop on Adversarial Robustness in the Real World (AROW). https://doi.org/10.48550/arXiv.2210.11242
    3. 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
  3. 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

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)
  • 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.
  • Variationelle Verfeinerung zur Schätzung von Szenenfluss: Master's Thesis, 2022.
  • A Global Adversarial Attack on Scene Flow: Master's Thesis, 2023. (co-supervision)
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