Lisa Weijler

I am a PhD student in 3D Vision advised by Pedro Hermosilla at the Computer Vision Lab, at TU Wien, Austria. My research focuses on 3D scene understanding using point cloud representation and geometric deep learning. I get particularly excited about solutions that blend mathematical concepts with modern deep learning in creative ways. Previously, I worked in the biomedical domain, applying deep learning to cancer cell detection in pediatric leukemia patients, in close collaboration with CCRI.

If I’m not deep diving into 3D Vision, I’m diving into the ocean — surfboard included 🏄🏼.

news

Jul 12, 2025	I won 3rd place in the Best-Poster-Awards @ICVSS 2025!
Apr 11, 2025	I have been accepted to attend ICVSS 2025 on “Computer Vision for Spatial Intelligence” in Sicily!
Nov 07, 2024	Our works TTTKD and SE3Conv3D were accepted at 3DV 2025!
Mar 21, 2024	Grateful for the opportunity to co-organize the WiML Social event at ICLR 2024!
Oct 23, 2023	Our work FATE was accepted at WACV 2024!

selected publications

TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models

L. Weijler, M. J. Mirza, L. Sick, and 2 more authors

International Conference on 3D Vision (3DV), 2025

arXiv Bib HTML

@article{weijler2025tttkd,
  title = {TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models},
  author = {Weijler, L. and Mirza, M. J. and Sick, L. and Ekkazan, C. and Hermosilla, P.},
  journal = {International Conference on 3D Vision (3DV)},
  year = {2025},
}

Efficient Continuous Group Convolutions for Local SE(3) Equivariance in 3D Point Clouds

L. Weijler and P. Hermosilla

International Conference on 3D Vision (3DV), 2025

arXiv Bib HTML

@article{weijler2025roteq,
  title = {Efficient Continuous Group Convolutions for Local SE(3) Equivariance in 3D Point Clouds},
  author = {Weijler, L. and Hermosilla, P.},
  journal = {International Conference on 3D Vision (3DV)},
  year = {2025},
}

FATE: Feature-Agnostic Transformer-Based Encoder for Learning Generalized Embedding Spaces in Flow Cytometry Data

L. Weijler, F. Kowarsch, M. Reiter, and 3 more authors

Winter Conference on Applications of Computer Vision (WACV), 2024

arXiv Bib HTML

@article{weijler2024fate,
  author = {Weijler, L. and Kowarsch, F. and Reiter, M. and Hermosilla, P. and Maurer-Granofszky, M. and Dworzak, M.},
  title = {FATE: Feature-Agnostic Transformer-Based Encoder for Learning Generalized Embedding Spaces in Flow Cytometry Data},
  journal = {Winter Conference on Applications of Computer Vision (WACV)},
  year = {2024},
}

On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data

L. Weijler, M. Reiter, P. Hermosilla, and 2 more authors

International Conference on Pattern Recognition (ICPR), 2024

arXiv Bib HTML

@article{weijler2024gcn,
  author = {Weijler, L. and Reiter, M. and Hermosilla, P. and Maurer-Granofszky, M. and Dworzak, M.},
  title = {On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data},
  journal = {International Conference on Pattern Recognition (ICPR)},
  year = {2024},
}