Zobrazeno 1 - 10
of 847
pro vyhledávání: '"Heß, Georg"'
Ensuring the safety of autonomous robots, such as self-driving vehicles, requires extensive testing across diverse driving scenarios. Simulation is a key ingredient for conducting such testing in a cost-effective and scalable way. Neural rendering me
Externí odkaz:
http://arxiv.org/abs/2411.16816
Autor:
Lindström, Carl, Hess, Georg, Lilja, Adam, Fatemi, Maryam, Hammarstrand, Lars, Petersson, Christoffer, Svensson, Lennart
Neural Radiance Fields (NeRFs) have emerged as promising tools for advancing autonomous driving (AD) research, offering scalable closed-loop simulation and data augmentation capabilities. However, to trust the results achieved in simulation, one need
Externí odkaz:
http://arxiv.org/abs/2403.16092
Multi-object tracking (MOT) is the task of estimating the state trajectories of an unknown and time-varying number of objects over a certain time window. Several algorithms have been proposed to tackle the multi-object smoothing task, where object de
Externí odkaz:
http://arxiv.org/abs/2312.17261
Autor:
Tonderski, Adam, Lindström, Carl, Hess, Georg, Ljungbergh, William, Svensson, Lennart, Petersson, Christoffer
Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community. Recent methods show NeRFs' potential for closed-loop simulation, enabling testing of AD systems, and as an advanced training data augmentation technique.
Externí odkaz:
http://arxiv.org/abs/2311.15260
Autor:
Alibeigi, Mina, Ljungbergh, William, Tonderski, Adam, Hess, Georg, Lilja, Adam, Lindstrom, Carl, Motorniuk, Daria, Fu, Junsheng, Widahl, Jenny, Petersson, Christoffer
Existing datasets for autonomous driving (AD) often lack diversity and long-range capabilities, focusing instead on 360{\deg} perception and temporal reasoning. To address this gap, we introduce Zenseact Open Dataset (ZOD), a large-scale and diverse
Externí odkaz:
http://arxiv.org/abs/2305.02008
Research connecting text and images has recently seen several breakthroughs, with models like CLIP, DALL-E 2, and Stable Diffusion. However, the connection between text and other visual modalities, such as lidar data, has received less attention, pro
Externí odkaz:
http://arxiv.org/abs/2212.06858
Autor:
Hess, Georg, Jaxing, Johan, Svensson, Elias, Hagerman, David, Petersson, Christoffer, Svensson, Lennart
Masked autoencoding has become a successful pretraining paradigm for Transformer models for text, images, and, recently, point clouds. Raw automotive datasets are suitable candidates for self-supervised pre-training as they generally are cheap to col
Externí odkaz:
http://arxiv.org/abs/2207.00531
Publikováno v:
European Conference on Computer Vision (ECCV). (2022) 550-566
Accurate uncertainty estimates are essential for deploying deep object detectors in safety-critical systems. The development and evaluation of probabilistic object detectors have been hindered by shortcomings in existing performance measures, which t
Externí odkaz:
http://arxiv.org/abs/2203.07980
Autor:
Pinto, Juliano, Hess, Georg, Ljungbergh, William, Xia, Yuxuan, Wymeersch, Henk, Svensson, Lennart
Multi-object tracking (MOT) is the problem of tracking the state of an unknown and time-varying number of objects using noisy measurements, with important applications such as autonomous driving, tracking animal behavior, defense systems, and others.
Externí odkaz:
http://arxiv.org/abs/2202.07909
Autor:
Malinverni, Chiara, Bernardelli, Andrea, Glimelius, Ingrid, Mirandola, Massimo, Smedby, Karin E., Tisi, Maria Chiara, Giné, Eva, Albertsson-Lindblad, Alexandra, Marin-Niebla, Ana, Di Rocco, Alice, Moita, Filipa, Sciarra, Roberta, Bašić-Kinda, Sandra, Hess, Georg, Ohler, Anke, Eskelund, Christian W., Re, Alessandro, Ferrarini, Isacco, Kolstad, Arne, Räty, Riikka, Quaglia, Francesca Maria, Eyre, Toby A., Scapinello, Greta, Stefani, Piero Maria, Morello, Lucia, Nassi, Luca, Hohaus, Stefan, Ragaini, Simone, Zilioli, Vittorio Ruggero, Bruna, Riccardo, Cocito, Federica, Arcari, Annalisa, Jerkeman, Mats, Visco, Carlo
Publikováno v:
In Blood 29 August 2024 144(9):1001-1009
