no code implementations • 31 Mar 2024 • Jacek Kałużny, Yannik Schreckenberg, Karol Cyganik, Peter Annighöfer, Sören Pirk, Dominik L. Michels, Mikolaj Cieslak, Farhah Assaad-Gerbert, Bedrich Benes, Wojciech Pałubicki
We introduce LAESI, a Synthetic Leaf Dataset of 100, 000 synthetic leaf images on millimeter paper, each with semantic masks and surface area labels.
no code implementations • 27 Mar 2024 • Mikolaj Cieslak, Umabharathi Govindarajan, Alejandro Garcia, Anuradha Chandrashekar, Torsten Hädrich, Aleksander Mendoza-Drosik, Dominik L. Michels, Sören Pirk, Chia-Chun Fu, Wojciech Pałubicki
The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data.
no code implementations • 5 Feb 2024 • Mulun Na, Jonathan Klein, Biao Zhang, Wojtek Pałubicki, Sören Pirk, Dominik L. Michels
We introduce the Lennard-Jones layer (LJL) for the equalization of the density of 2D and 3D point clouds through systematically rearranging points without destroying their overall structure (distribution normalization).
no code implementations • 4 Feb 2024 • Junchen Deng, Samhita Marri, Jonathan Klein, Wojtek Pałubicki, Sören Pirk, Girish Chowdhary, Dominik L. Michels
Robotic harvesting has the potential to positively impact agricultural productivity, reduce costs, improve food quality, enhance sustainability, and to address labor shortage.
no code implementations • 11 Oct 2023 • Andrei C. Aioanei, Regine Hunziker-Rodewald, Konstantin Klein, Dominik L. Michels
Our results validate the model's capabilities in handling diverse real-world scenarios, proving the viability of our synthetic data approach and avoiding the dependence on scarce training data that has constrained epigraphic analysis.
no code implementations • 10 Oct 2023 • Stefan Rhys Jeske, Jonathan Klein, Dominik L. Michels, Jan Bender
Overall, this can help reduce the computational overhead of training and evaluating neural distance fields, as well as enabling the application to difficult shapes.
no code implementations • 1 Jun 2022 • Azimkhon Ostonov, Peter Wonka, Dominik L. Michels
We present RLSS: a reinforcement learning algorithm for sequential scene generation.
1 code implementation • 26 Apr 2021 • Stanislava Fedorova, Alberto Tono, Meher Shashwat Nigam, Jiayao Zhang, Amirhossein Ahmadnia, Cecilia Bolognesi, Dominik L. Michels
The variety of annotations, the flexibility to customize the generated building and dataset parameters make this framework suitable for multiple deep learning tasks, including geometric deep learning that requires direct 3D supervision.
no code implementations • 16 Jun 2020 • Jonathan Klein, Sören Pirk, Dominik L. Michels
We present a novel domain adaptation framework that uses morphologic segmentation to translate images from arbitrary input domains (real and synthetic) into a uniform output domain.
no code implementations • 6 Jun 2020 • Han Shao, Tassilo Kugelstadt, Torsten Hädrich, Wojciech Pałubicki, Jan Bender, Sören Pirk, Dominik L. Michels
In this contribution, we introduce a novel method to accelerate iterative solvers for physical systems with graph networks (GNs) by predicting the initial guesses to reduce the number of iterations.
no code implementations • 18 Apr 2019 • Matthias Müller, Guohao Li, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem
A common approach is to learn an end-to-end policy that directly predicts controls from raw images by imitating an expert.
no code implementations • 3 Mar 2018 • Guohao Li, Matthias Müller, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem
Recent work has explored the problem of autonomous navigation by imitating a teacher and learning an end-to-end policy, which directly predicts controls from raw images.
no code implementations • 19 Aug 2017 • Matthias Müller, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem
Automating the navigation of unmanned aerial vehicles (UAVs) in diverse scenarios has gained much attention in recent years.