Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Schinagl, David"'
Autor:
Schinagl, David, Krispel, Georg, Fruhwirth-Reisinger, Christian, Possegger, Horst, Bischof, Horst
Widely-used LiDAR-based 3D object detectors often neglect fundamental geometric information readily available from the object proposals in their confidence estimation. This is mostly due to architectural design choices, which were often adopted from
Externí odkaz:
http://arxiv.org/abs/2310.20319
Autor:
Krispel, Georg, Schinagl, David, Fruhwirth-Reisinger, Christian, Possegger, Horst, Bischof, Horst
The sensing process of large-scale LiDAR point clouds inevitably causes large blind spots, i.e. regions not visible to the sensor. We demonstrate how these inherent sampling properties can be effectively utilized for self-supervised representation le
Externí odkaz:
http://arxiv.org/abs/2212.07207
While 3D object detection in LiDAR point clouds is well-established in academia and industry, the explainability of these models is a largely unexplored field. In this paper, we propose a method to generate attribution maps for the detected objects i
Externí odkaz:
http://arxiv.org/abs/2204.06577
Data labeling for learning 3D hand pose estimation models is a huge effort. Readily available, accurately labeled synthetic data has the potential to reduce the effort. However, to successfully exploit synthetic data, current state-of-the-art methods
Externí odkaz:
http://arxiv.org/abs/1811.09497
The labeled data required to learn pose estimation for articulated objects is difficult to provide in the desired quantity, realism, density, and accuracy. To address this issue, we develop a method to learn representations, which are very specific f
Externí odkaz:
http://arxiv.org/abs/1804.03390
Autor:
Krispel, Georg, Schinagl, David, Fruhwirth-Reisinger, Christian, Possegger, Horst, Bischof, Horst
We demonstrate how the often overlooked inherent properties of large-scale LiDAR point clouds can be effectively utilized for self-supervised representation learning. In pursuit of this goal, we design a highly data-efficient feature pre-training bac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d1c7252b55643b517d882eb22ac09913
http://arxiv.org/abs/2212.07207
http://arxiv.org/abs/2212.07207
Proceedings of the OAGM Workshop 2018
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c79ffab05ea6ea3c960b3bd25b192cd1