no code implementations • 21 Mar 2024 • Francesco Di Felice, Alberto Remus, Stefano Gasperini, Benjamin Busam, Lionel Ott, Federico Tombari, Roland Siegwart, Carlo Alberto Avizzano
Estimating the pose of objects through vision is essential to make robotic platforms interact with the environment.
no code implementations • 4 Dec 2023 • Felix Tristram, Stefano Gasperini, Nassir Navab, Federico Tombari
With Re-Nerfing, we enhance the geometric consistency of novel views as follows: First, we train a NeRF with the available views.
no code implementations • 9 Nov 2023 • Sen Wang, Wei zhang, Stefano Gasperini, Shun-Cheng Wu, Nassir Navab
Creating high-quality view synthesis is essential for immersive applications but continues to be problematic, particularly in indoor environments and for real-time deployment.
no code implementations • 29 Aug 2023 • Alexander Lehner, Stefano Gasperini, Alvaro Marcos-Ramiro, Michael Schmidt, Nassir Navab, Benjamin Busam, Federico Tombari
We conduct extensive experiments across a variety of scenarios on data from KITTI, Waymo, and CrashD for 3D object detection, and on data from SemanticKITTI, Waymo, and nuScenes for 3D semantic segmentation.
no code implementations • ICCV 2023 • Stefano Gasperini, Nils Morbitzer, HyunJun Jung, Nassir Navab, Federico Tombari
While state-of-the-art monocular depth estimation approaches achieve impressive results in ideal settings, they are highly unreliable under challenging illumination and weather conditions, such as at nighttime or in the presence of rain.
no code implementations • ICCV 2023 • Stefano Gasperini, Alvaro Marcos-Ramiro, Michael Schmidt, Nassir Navab, Benjamin Busam, Federico Tombari
By doing so, for the first time in panoptic segmentation with unknown objects, our U3HS is trained without unknown categories, reducing assumptions and leaving the settings as unconstrained as in real-life scenarios.
no code implementations • CVPR 2022 • Alexander Lehner, Stefano Gasperini, Alvaro Marcos-Ramiro, Michael Schmidt, Mohammad-Ali Nikouei Mahani, Nassir Navab, Benjamin Busam, Federico Tombari
Despite training only on a standard dataset, such as KITTI, augmenting with our vector fields significantly improves the generalization to differently shaped objects and scenes.
no code implementations • 4 Oct 2021 • Stefano Gasperini, Jan Haug, Mohammad-Ali Nikouei Mahani, Alvaro Marcos-Ramiro, Nassir Navab, Benjamin Busam, Federico Tombari
Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings.
no code implementations • 10 Aug 2021 • Stefano Gasperini, Patrick Koch, Vinzenz Dallabetta, Nassir Navab, Benjamin Busam, Federico Tombari
While self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches, violations of the static world assumption can still lead to erroneous depth predictions of traffic participants, posing a potential safety issue.
no code implementations • 28 Oct 2020 • Stefano Gasperini, Mohammad-Ali Nikouei Mahani, Alvaro Marcos-Ramiro, Nassir Navab, Federico Tombari
Panoptic segmentation has recently unified semantic and instance segmentation, previously addressed separately, thus taking a step further towards creating more comprehensive and efficient perception systems.
no code implementations • 18 Nov 2019 • Stefano Gasperini, Magdalini Paschali, Carsten Hopke, David Wittmann, Nassir Navab
Radar signals have been dramatically increasing in complexity, limiting the source separation ability of traditional approaches.
no code implementations • 5 Apr 2019 • Magdalini Paschali, Stefano Gasperini, Abhijit Guha Roy, Michael Y. -S. Fang, Nassir Navab
Model architectures have been dramatically increasing in size, improving performance at the cost of resource requirements.