The IEEE Croatia Section and the IFRoS project hosted lectures on:
"Neural Radiance Fields (NeRF): basics, variations, applications" which was presented by Nelli Nyisztor, a Ph.D student and "Transferability in Robotics: transfer, curriculum, and reinforcement learning" which were presented by Dániel Horváth, a Ph.D student on Thursday, January 18.
Nelli Nyisztor
Bio:
Nelli Nyisztor is a Ph.D student at Eötvös Loránd University, Budapest and a researcher at Bosch Hungary – specializing in visual sensors and sensor fusion. She works on an aerial image-based pose estimation method assisting ADAS development. Her interests lie in computer vision, machine learning and 3D reconstruction.
She earned her M.Sc. in autonomous systems via a double degree program split between Politech Nice, France and Eötvös Loránd University, Hungary. Her master thesis covers 6-DoF pose estimation using Neural Radiance Fields.
Abstract:
Explore the fundamentals, variations, and applications of Neural Radiance Fields (NeRF) in this introductory presentation. Revealing the potential of NeRF as we navigate through its basics and showcase how this innovative approach is shaping the future of 3D scene and object representation.
Dániel Horváth
Bio:
Dániel Horváth is a research associate, pursuing his Ph.D. at the Eötvös Loránd University, Budapest, Hungary in collaboration with the Institute for Computer Science and Control, Budapest Hungary, and, as a Campus France scholar, with the École Nationale Supérieure des Mines de Paris in France. His research focuses on robotics in the fields of reinforcement, curriculum, and transfer learning, and computer vision.
He received the M.Sc. degree with the highest honours in mechatronics from the Budapest University of Technology and Economics, Budapest, Hungary, in 2019, spending one semester each at the Technical University of Denmark, Copenhagen, Denmark, and the Otto von Guericke University, Magdeburg, Germany.
Abstract:
Attaining sufficient scale and robustness in robotics is essential for the seamless integration of robots into both industrial settings and our daily lives. Transferability, covering a wide range of topics, plays a pivotal role in achieving this objective. This lecture addresses three key components—transfer, curriculum, and reinforcement learning—illustrated through research projects conducted in these domains.
More information is available here: www.danielhorvath.eu.