Name: Peter Kocsis
Position: Ph.D Candidate
Phone: +49 (89) 289 - 18164
Room No: 02.07.038


I am Peter. I finished my Bachelor studies as a Mechatronics engineer in Hungary, then I moved to Munich and did the Master of Robotics, Cognition, Intelligence at TUM. I previously worked on Active Learning and Reinforcement Learning for control and autonomous driving.

Research Interest

Dynamic 3D Reconstruction, Reinforcement Learning and Information/Sensor Fusion techniques using learning based methodologies.



Intrinsic Image Diffusion for Single-view Material Estimation
Peter Kocsis, Vincent Sitzmann, Matthias Nießner
CVPR 2024
Appearance decomposition is an ambiguous task and collecting real data is challenging. We utilize a pre-trained diffusion model and formulate the problem probabilistically. We fine-tune a pre-trained diffusion model conditioned on a single input image to adapt its image prior to the prediction of albedo, roughness and metallic maps. With our sharp material predictions, we optimize for spatially-varying lighting to enable photo-realistic material editing and relighting.
[video][bibtex][project page]


The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes
Peter Kocsis, Peter Sukenik, Guillem Braso, Matthias Nießner, Laura Leal-Taixé, Ismail Elezi
NeurIPS 2022
We show that adding fully-connected layers is beneficial for the generalization of convolutional networks in the tasks working in the low-data regime. Furthermore, we present a novel online joint knowledge distillation method (OJKD), which allows us to utilize additional final fully-connected layers during training but drop them during inference without a noticeable loss in performance. Doing so, we keep the same number of weights during test time.
[code][bibtex][project page]