no code implementations • 18 Oct 2021 • Christian Fruhwirth-Reisinger, Michael Opitz, Horst Possegger, Horst Bischof
In the field of autonomous driving, self-training is widely applied to mitigate distribution shifts in LiDAR-based 3D object detectors.
no code implementations • 16 Jun 2021 • Muhammad Jehanzeb Mirza, Cornelius Buerkle, Julio Jarquin, Michael Opitz, Fabian Oboril, Kay-Ulrich Scholl, Horst Bischof
State-of-the-art object detection systems for autonomous driving achieve promising results in clear weather conditions.
no code implementations • 18 Dec 2019 • Georg Krispel, Michael Opitz, Georg Waltner, Horst Possegger, Horst Bischof
We introduce a simple yet effective fusion method of LiDAR and RGB data to segment LiDAR point clouds.
1 code implementation • 23 Nov 2018 • Georg Poier, Michael Opitz, David Schinagl, Horst Bischof
In this work, we remove this requirement by learning to map from the features of real data to the features of synthetic data mainly using a large amount of synthetic and unlabeled real data.
no code implementations • 9 May 2018 • Georg Waltner, Michael Maurer, Thomas Holzmann, Patrick Ruprecht, Michael Opitz, Horst Possegger, Friedrich Fraundorfer, Horst Bischof
Furthermore due to the design of the network, at test time only the 2D camera images are required for classification which enables the usage in portable computer vision systems.
1 code implementation • 15 Jan 2018 • Michael Opitz, Georg Waltner, Horst Possegger, Horst Bischof
To this end, we divide the last embedding layer of a deep network into an embedding ensemble and formulate training this ensemble as an online gradient boosting problem.
Ranked #13 on Image Retrieval on SOP
no code implementations • ICCV 2017 • Michael Opitz, Georg Waltner, Horst Possegger, Horst Bischof
Learning similarity functions between image pairs with deep neural networks yields highly correlated activations of large embeddings.
no code implementations • 1 Sep 2016 • Michael Opitz, Georg Waltner, Georg Poier, Horst Possegger, Horst Bischof
Detection of partially occluded objects is a challenging computer vision problem.