Csoportunk két kutatója, Bogár György Richárd és Szántó Mátyás egyaránt prezentálták kutatási eredményeinket a 2023. január 24-27. között, Gyulán tartott KÉPAF2023 konferencián. A szimpózium a Neumann János Számítástudományi Társaság (NJSZT) Képfeldolgozók és Alakfelismerők Társaságának (KÉPAF) szervezésében valósult meg. A publikációk a csoportunk több kutatási témájához is kapcsolódó, átfogó problémák megoldását mutatják be. Szántó Mátyás az Efficient …
At the International Symposium on Measurement, Control, and Robotics (ISMCR2022) organized by the IEEE Robotics and Automation Society, Matyas Szanto presented the latest results achieved in a three-dimensional simulation environment based on differentiable rendering of real occlusion phenomena. The work was done in collaboration with Dr. Marton Szemenyei. The publication, titled Self-Supervised Occlusion Detection and Avoidance using Differentiable Rendering, can be ...
Dr. Szemenyei Márton presented our research topics and achieved results at the 3rd meetup of the Spatial Information and Remote Sensing section of the Hungarian Association for Computer Science in June 27, 2022. The title of Szemenyei Márton's presentation was Sim2Real technologies in the service of urban databases.
Our MSc student, Solt Skribanek, gave a presentation at the ICAISC International Conference in Zakopane. The work presented was the result of a research project carried out under the PIA project with Dr. Márton Szemenyei and Moni Róbert from the Department of Telecommunications and Media Informatics. The publication, titled "Semantically consistent sim-to-real image translation with neural networks," is available at this link. Congratulations!
The group participated in the 10th Hungarian Conference on Computer Graphics and Geometry. At the symposium held on June 9, 2022 at the SZTAKI Kende Street building, we presented our deep learning-based occlusion removal methods, which were developed as a result of the work of our two leading researchers, Dr. Márton Szemenyei and Mátyás Szántó. The presented work is titled "Deep Learning-based Occlusion Detection in a Differentiable Simulation Environment" ...
