1 code implementation • 7 Feb 2024 • Peter Hönig, Stefan Thalhammer, Jean-Baptiste Weibel, Matthias Hirschmanner, Markus Vincze
To achieve a focus on learning shape features, the textures are randomized during the rendering of the training data.
no code implementations • 21 Sep 2023 • Philipp Ausserlechner, David Haberger, Stefan Thalhammer, Jean-Baptiste Weibel, Markus Vincze
The state-of-the-art 6D object pose estimation methods rely on object-specific training and therefore do not generalize to unseen objects.
no code implementations • 22 Jul 2023 • Stefan Thalhammer, Dominik Bauer, Peter Hönig, Jean-Baptiste Weibel, José García-Rodríguez, Markus Vincze
Object pose estimation is a core perception task that enables, for example, object grasping and scene understanding.
1 code implementation • 31 May 2023 • Stefan Thalhammer, Jean-Baptiste Weibel, Markus Vincze, Jose Garcia-Rodriguez
This work evaluates and demonstrates the differences between self-supervised CNNs and Vision Transformers for deep template matching.
no code implementations • 23 Feb 2023 • Stefan Thalhammer, Peter Hönig, Jean-Baptiste Weibel, Markus Vincze
Object pose estimation is a non-trivial task that enables robotic manipulation, bin picking, augmented reality, and scene understanding, to name a few use cases.
no code implementations • 10 Mar 2021 • Jean-Baptiste Weibel, Timothy Patten, Markus Vincze
While object semantic understanding is essential for most service robotic tasks, 3D object classification is still an open problem.
no code implementations • 28 Oct 2019 • Jean-Baptiste Weibel, Timothy Patten, Markus Vincze
In this work, we examine this gap in a robotic context by specifically addressing the problem of classification when transferring from artificial CAD models to real reconstructed objects.
no code implementations • CVPR 2016 • Hongyuan Zhu, Jean-Baptiste Weibel, Shijian Lu
RGBD scene recognition has attracted increasingly attention due to the rapid development of depth sensors and their wide application scenarios.