Pedro Hermosilla

Assistant Professor

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I am Assistant Professor in AI for Visual Computing at the Computer Vision Lab, at TU Wien, Austria. Before joining TU Wien, I did my PostDoc at the Viscom group headed by Prof. Timo Ropinski at Ulm University while working in close collaboration with Prof. Tobias Ritschel from University College London. I obtained my PhD at the Polytechnical University of Catalonia, under the supervision of Prof. Pere-Pau Vazquez and Prof. Alvar Vinacua.

My research is focused on developing machine learning technologies for 3D and unstructured data, with a special interest in point clouds, graphs, and implicit representations. During my research, I applied these technologies to solve different problems in the fields of Computer Vision, Computer Graphics, and Bioinformatics.

News

Jun 27, 2025 We have a paper accepted at ICCV. Congratulations to Leon Sick!
Mar 18, 2025 We have two papers accepted at CVPR. Congratulations to Sebastian Koch and Leon Sick!
Nov 28, 2024 We have two papers accepted at 3DV and one paper accepted at WACV. Congratulations to Lisa Weijler and Leon Sick!
Feb 28, 2024 We have two papers accepted at CVPR. Congratulations to Sebastian Koch and Leon Sick!
Feb 20, 2024 Congratulations to Aron S. Kovacs for getting his work accepted at Eurographics 2024.

Selected publications

  1. Masked Scene Modeling: Narrowing the Gap Between Supervised and Self-Supervised Learning in 3D Scene Understanding
    P. Hermosilla, C. Stippel, and L. Sick
    Conference on Computer Vision and Pattern Recognition (CVPR) 2025
  2. RelationField: Relate Anything in Radiance Fields
    S. Koch, J. Wald, M. Colosi, N. Vaskevicius,  P. Hermosilla, F. Tombari, and T. Ropinski
    Conference on Computer Vision and Pattern Recognition (CVPR) 2025
  3. CutS3D: Cutting Semantics in 3D for 2D Unsupervised Instance Segmentation
    L. Sick, D. Engel, S. Hartwig,  P. Hermosilla, and T. Ropinski
    International Conference on Computer Vision (ICCV) 2025
  4. TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models
    L. Weijler, M. J. Mirza, L. Sick, C. Ekkazan, and P. Hermosilla
    International Conference on 3D Vision (3DV) 2025
  5. 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
  6. Attention-Guided Masked Autoencoders For Learning Image Representations
    L. Sick, D. Engel,  P. Hermosilla, and T. Ropinski
    Winter Conference on Applications of Computer Vision (WACV) 2025 (Oral)
  7. Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships
    S. Koch, N. Vaskevicius, M. Colosi,  P. Hermosilla, and T. Ropinski
    Conference on Computer Vision and Pattern Recognition (CVPR) 2024
  8. Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling
    L. Sick, D. Engel,  P. Hermosilla, and T. Ropinski
    Conference on Computer Vision and Pattern Recognition (CVPR) 2024
  9. Weakly Supervised Virus Capsid Detection with Image-Level Annotations in Electron Microscopy Images
    H. Kniesel, L. Sick, T. Payer, T. Bergner, K. S. Devan, C. Read, P. Walther, T. Ropinski, and P. Hermosilla
    International Conference on Learning Representations (ICLR) 2024
  10. Lang3DSG: Language-based contrastive pre-training for 3D scene graph prediction
    S. Koch,  P. Hermosilla, N. Vaskevicius, M. Colosi, and T. Ropinski
    International Conference on 3D Vision (3DV) 2024
  11. G-Style: Stylized Gaussian Splatting
    A. S. Kovacs,  P. Hermosilla, and R. G. Raidou
    Computer Graphics Forum (Proc. Pacific Graphics) 2024
  12. Surface-aware Mesh Texture Synthesis with Pre-trained 2D CNNs
    A. S. Kovacs,  P. Hermosilla, and R. G. Raidou
    Computer Graphics Forum (Proc. Eurographics) 2024
  13. Variance-Aware Weight Initialization for Point Convolutional Neural Networks
    P. Hermosilla, M. Schelling, T. Ritschel, and T. Ropinski
    European Conference on Computer Vision (ECCV) 2022
  14. Clean Implicit 3D Structure from Noisy 2D STEM Images
    H. Kniesel, T. Ropinski, T. Bergner, K. Shaga Devan, C. Read, P. Walther, T. Ritschel, and P. Hermosilla
    Conference on Computer Vision and Pattern Recognition (CVPR) 2022
  15. RADU: Ray-Aligned Depth Update Convolutions for ToF Data Denoising
    M. Schelling,  P. Hermosilla, and T. Ropinski
    Conference on Computer Vision and Pattern Recognition (CVPR) 2022
  16. Gaussian Mixture Convolution Networks
    A. Celarek,  P. Hermosilla, B. Kerbl, T. Ropinski, and M. Wimmer
    International Conference on Learning Representations (ICLR) 2022
  17. Contrastive Representation Learning for 3D Protein Structures
    P. Hermosilla, and T. Ropinski
    Pre-print 2022
  18. Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures
    P. Hermosilla, M. Schaefer, M. Lang, G. Fackelmann, P.-P. Vazquez, B. Kozlikova, M. Krone, T. Ritschel, and T. Ropinski
    International Conference on Learning Representations (ICLR) 2021
  19. Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning
    P. Hermosilla, T. Ritschel, and T. Ropinski
    International Conference on Computer Vision (ICCV) 2019
  20. Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds
    P. Hermosilla, T. Ritschel, P.-P. Vazquez, A. Vinacua, and T. Ropinski
    ACM Transactions on Graphics (Proc. SIGGRAPH Asia) 2018