2025W

• Bachelor Thesis / 183.582 / PR
• Doctoral Seminar / 183.619 / SE
• Fundamentals of Computer Vision / 193.125 / VU
• Methods for Data Generation and Analytics in Medicine and Life Sciences / 193.026 / VU
• Project in Computer Science 1 / 193.117 / PR
• Project in Computer Science 2 / 193.118 / PR
• Project in Medical Informatics / 193.025 / PR
• Project in Visual Computing 1 / 193.023 / PR
• Project in Visual Computing 2 / 193.024 / PR
• Project Media and Human-Centered Computing 1 / 193.021 / PR
• Project Media and Human-Centered Computing 2 / 193.022 / PR
• Scientific Research and Writing / 193.052 / SE

2026S

• Bachelor Thesis / 183.582 / PR
• Deep Learning for Visual Computing / 193.200 / VU
• Doctoral Seminar / 183.619 / SE
• Project in Computer Science 1 / 193.117 / PR
• Project in Computer Science 2 / 193.118 / PR
• Project in Medical Informatics / 193.025 / PR
• Project in Visual Computing 1 / 193.023 / PR
• Project in Visual Computing 2 / 193.024 / PR
• Project Media and Human-Centered Computing 1 / 193.021 / PR
• Project Media and Human-Centered Computing 2 / 193.022 / PR
• Scientific Research and Writing / 193.052 / SE

• Konzipierung einer vollautomatisierten Ent- und Verladung mittels FTS für Außenbereich und Kommissionierung
  2025 – 2028 / Austrian Research Promotion Agency (FFG)
  Publication: 223720
• 2D-to-3D lifting of foundation models for few-shot 3D object detection
  2024 – 2026 / cirqular pointcloud analytics GmbH

• Exploring Modality Guidance to Enhance VFM-Based Feature Fusion for UDA in 3D Semantic Segmentation / Johannes Spöcklberger, Lin, W., Hermosilla, P., Doveh, S., Possegger, H., & Mirza, J. M. (2025). Exploring Modality Guidance to Enhance VFM-Based Feature Fusion for UDA in 3D Semantic Segmentation. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp. 4789–4798). IEEE. https://doi.org/10.1109/CVPRW67362.2025.00465
• DeepEM Playground: Bringing deep learning to electron microscopy labs / Kniesel, H., Poonam, P., Payer, T., Bergner, T., Hermosilla, P., & Ropinski, T. (2025). DeepEM Playground: Bringing deep learning to electron microscopy labs. Journal of Microscopy, 299(3), 287–300. https://doi.org/10.1111/jmi.70005
• TTT-KD: Test-Time Training for 3D Semantic Segmentation Through Knowledge Distillation From Foundation Models / Weijler, L. M., Mirza, J. M., Sick, L., Ekkazan, C., & Hermosilla, P. (2025). TTT-KD: Test-Time Training for 3D Semantic Segmentation Through Knowledge Distillation From Foundation Models. In 2025 International Conference on 3D Vision (3DV) (pp. 1264–1274). IEEE. https://doi.org/10.1109/3DV66043.2025.00120
• RelationField: Relate Anything in Radiance Fields / Koch, S., Wald, J., Colosi, M., Vaskevicius, N., Hermosilla, P., Tombari, F., & Ropinski, T. (2025). RelationField: Relate Anything in Radiance Fields. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 21706–21716). IEEE. https://doi.org/10.1109/CVPR52734.2025.02022
• Attention-Guided Masked Autoencoders for Learning Image Representations / Sick, L., Engel, D., Hermosilla, P., & Ropinski, T. (2025). Attention-Guided Masked Autoencoders for Learning Image Representations. In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (pp. 836–846). IEEE. https://doi.org/10.1109/WACV61041.2025.00091
• From Natural to Nanoscale: Training ControlNet on Scarce FIB-SEM Data for Augmenting Semantic Segmentation Data / Kniesel, H., Rapp, P., Hermosilla, P., & Ropinski, T. (2025). From Natural to Nanoscale: Training ControlNet on Scarce FIB-SEM Data for Augmenting Semantic Segmentation Data. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops 2025 (pp. 5731–5740).
• Masked Scene Modeling: Narrowing the Gap Between Supervised and Self-Supervised Learning in 3D Scene Understanding / Hermosilla, P., Stippel, C., & Sick, L. (2025). Masked Scene Modeling: Narrowing the Gap Between Supervised and Self-Supervised Learning in 3D Scene Understanding. In Proceedings of the IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) (pp. 14835–14844). https://doi.org/10.1109/CVPR52734.2025.01382
• Efficient Continuous Group Convolutions for Local SE(3) Equivariance in 3D Point Clouds / Weijler, L. M., & Hermosilla Casajus, P. (2025). Efficient Continuous Group Convolutions for Local SE(3) Equivariance in 3D Point Clouds. In 2025 International Conference on 3D Vision (3DV) (pp. 1372–1381). IEEE. https://doi.org/10.1109/3DV66043.2025.00130
• Marching Neurons: Accurate Surface Extraction for Neural Implicit Shapes / Stippel, C., Mujkanovic, F., Leimkühler, T., & Hermosilla Casajus, P. (2025). Marching Neurons: Accurate Surface Extraction for Neural Implicit Shapes. ACM Transactions on Graphics, 44(6), Article 222. https://doi.org/10.1145/3763328
• HPSCAN: Human Perception‐Based Scattered Data Clustering / Hartwig, S., Onzenoodt, C. v., Engel, D., Hermosilla Casajus, P., & Ropinski, T. (2025). HPSCAN: Human Perception‐Based Scattered Data Clustering. Computer Graphics Forum, 44(1), Article e15275. https://doi.org/10.1111/cgf.15275
• On the Importance of Local and Global Feature Learning for Automated Measurable Residual Disease Detection in Flow Cytometry Data / Weijler, L., Reiter, M., Hermosilla, P., Maurer-Granofszky, M., & Dworzak, M. (2025). On the Importance of Local and Global Feature Learning for Automated Measurable Residual Disease Detection in Flow Cytometry Data. In A. Antonacopoulos, S. Chaudhuri, R. Chellappa, Cl. Liu, S. Bhattacharya, & U. Pal (Eds.), Pattern Recognition (pp. 316–331). https://doi.org/10.1007/978-3-031-78198-8_21
  Project: MyeFLOW (2020–2025)
• Surface-aware Mesh Texture Synthesis with Pre-trained 2D CNNs / Kovacs, A. S., Hermosilla Casajus, P., & Raidou, R. G. (2024). Surface-aware Mesh Texture Synthesis with Pre-trained 2D CNNs. Computer Graphics Forum, 43(2), Article e15016. https://doi.org/10.1111/cgf.15016
• Efficient continuous group convolutions for local SO(3) equivariance in 3D point clouds / Weijler, L. M., & Hermosilla, P. (2024, February 15). Efficient continuous group convolutions for local SO(3) equivariance in 3D point clouds [Conference Presentation]. COMPUTER VISION WINTER WORKSHOP 2024, Slovenia.
• PPSurf: combining patches and point convolutions for detailed surface reconstruction / Erler, P., Fuentes‐Perez, L., Hermosilla, P., Guerrero, P., Pajarola, R., & Wimmer, M. (2024). PPSurf: combining patches and point convolutions for detailed surface reconstruction. Computer Graphics Forum, Article e15000. https://doi.org/10.1111/cgf.15000
  Download: PDF (2.53 MB)
  Projects: Echtzeit-Formenakquisition mit sensorspezifischer Präzision (2015–2020) / EVOCATION (2018–2022) / Modeling the World at Scale (2020–2026) / Superhumans (2019–2022)
• SGRec3D: Self-Supervised 3D Scene Graph Learning via Object-Level Scene Reconstruction / Koch, S., Hermosilla, P., Vaskevicius, N., Colosi, M., & Ropinski, T. (2024). SGRec3D: Self-Supervised 3D Scene Graph Learning via Object-Level Scene Reconstruction. In 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (pp. 3392–3402). https://doi.org/10.1109/WACV57701.2024.00337
• Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships / Koch, S., Vaskevicius, N., Colosi, M., Hermosilla, P., & Ropinski, T. (2024). Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 14183–14193). https://doi.org/10.1109/CVPR52733.2024.01345
• Lang3DSG: Language-based contrastive pre-training for 3D Scene Graph prediction / Koch, S., Hermosilla, P., Vaskevicius, N., Colosi, M., & Ropinski, T. (2024). Lang3DSG: Language-based contrastive pre-training for 3D Scene Graph prediction. In 2024 International Conference on 3D Vision (3DV) (pp. 1037–1047). https://doi.org/10.1109/3DV62453.2024.00076
• Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling / Sick, L., Engel, D., Hermosilla, P., & Ropinski, T. (2024). Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3637–3646). https://doi.org/10.1109/CVPR52733.2024.00349
• Weakly Supervised Virus Capsid Detection with Image-Level Annotations in Electron Microscopy Images / Kniesel, H., Sick, L., Payer, T., Bergner, T., Shaga Devan, K., Read, C., Walther, P., Ropinski, T., & Hermosilla Casajus, P. (2024). Weakly Supervised Virus Capsid Detection with Image-Level Annotations in Electron Microscopy Images. In The Twelfth International Conference on Learning Representations. The Twelfth International Conference on Learning Representations (ICLR 2024), Wien, Austria. http://hdl.handle.net/20.500.12708/203934
• FATE: Feature-Agnostic Transformer-based Encoder for learning generalized embedding spaces in flow cytometry data / Weijler, L., Kowarsch, F., Reiter, M., Hermosilla, P., Maurer-Granofszky, M., & Dworzak, M. (2024). FATE: Feature-Agnostic Transformer-based Encoder for learning generalized embedding spaces in flow cytometry data. In 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (pp. 7941–7949). https://doi.org/10.1109/WACV57701.2024.00777
  Project: MyeFLOW (2020–2025)
• G-Style: Stylized Gaussian Splatting / Kovács, Á. S., Hermosilla, P., & Raidou, R. G. (2024). G-Style: Stylized Gaussian Splatting. Computer Graphics Forum, 43(7), Article e15259. https://doi.org/10.1111/cgf.15259

• Alpine terrain relighting : deep-learning based single image shadow-removal with digital elevation models / Staats, M. (2024). Alpine terrain relighting : deep-learning based single image shadow-removal with digital elevation models [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2024.112641
  Download: PDF (4.85 MB)