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of 19
pro vyhledávání: '"Hurych, David"'
Automating visual inspection in industrial production lines is essential for increasing product quality across various industries. Anomaly detection (AD) methods serve as robust tools for this purpose. However, existing public datasets primarily cons
Externí odkaz:
http://arxiv.org/abs/2405.04953
We study the problem of self-supervised 3D scene flow estimation from real large-scale raw point cloud sequences, which is crucial to various tasks like trajectory prediction or instance segmentation. In the absence of ground truth scene flow labels,
Externí odkaz:
http://arxiv.org/abs/2404.08363
Learning without supervision how to predict 3D scene flows from point clouds is essential to many perception systems. We propose a novel learning framework for this task which improves the necessary regularization. Relying on the assumption that scen
Externí odkaz:
http://arxiv.org/abs/2312.08879
Autor:
Gebrehiwot, Awet Haileslassie, Hurych, David, Zimmermann, Karel, Pérez, Patrick, Svoboda, Tomáš
Deep perception models have to reliably cope with an open-world setting of domain shifts induced by different geographic regions, sensor properties, mounting positions, and several other reasons. Since covering all domains with annotated data is tech
Externí odkaz:
http://arxiv.org/abs/2309.08302
Autor:
Gebrehiwot, Awet Haileslassie, Vacek, Patrik, Hurych, David, Zimmermann, Karel, Perez, Patrick, Svoboda, Tomáš
Publikováno v:
in IEEE Robotics and Automation Letters, vol. 8, no. 2, pp. 536-543, Feb. 2023
Automatic pseudo-labeling is a powerful tool to tap into large amounts of sequential unlabeled data. It is specially appealing in safety-critical applications of autonomous driving, where performance requirements are extreme, datasets are large, and
Externí odkaz:
http://arxiv.org/abs/2207.06079
Autor:
Vobecky, Antonin, Hurych, David, Siméoni, Oriane, Gidaris, Spyros, Bursuc, Andrei, Pérez, Patrick, Sivic, Josef
This work investigates learning pixel-wise semantic image segmentation in urban scenes without any manual annotation, just from the raw non-curated data collected by cars which, equipped with cameras and LiDAR sensors, drive around a city. Our contri
Externí odkaz:
http://arxiv.org/abs/2203.11160
Existing datasets for training pedestrian detectors in images suffer from limited appearance and pose variation. The most challenging scenarios are rarely included because they are too difficult to capture due to safety reasons, or they are very unli
Externí odkaz:
http://arxiv.org/abs/2012.08274
Autor:
Uricar, Michal, Krizek, Pavel, Hurych, David, Sobh, Ibrahim, Yogamani, Senthil, Denny, Patrick
Generative Adversarial Networks (GAN) have gained a lot of popularity from their introduction in 2014 till present. Research on GAN is rapidly growing and there are many variants of the original GAN focusing on various aspects of deep learning. GAN a
Externí odkaz:
http://arxiv.org/abs/1902.03442
Autonomous driving is getting a lot of attention in the last decade and will be the hot topic at least until the first successful certification of a car with Level 5 autonomy. There are many public datasets in the academic community. However, they ar
Externí odkaz:
http://arxiv.org/abs/1901.09270
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