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of 80 228
pro vyhledávání: '"A. Cremers"'
5.4b. Pseudolycopodiella meridionalis (Underw. & F.E.Lloyd) Holub var. mesetarum (B.Øllg.) B.Øllg., Boudrie & Cremers, comb. nov. —Basionym: Lycopodiella caroliniana (L.) Pic.Serm. var. mesetarum B.Øllg., Nord. J. Bot. 23: 39–40. 2004).—Syno
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0634d54013e8adc13674ded8b2c4dc25
Autor:
Yang, Linyan, Hoyer, Lukas, Weber, Mark, Fischer, Tobias, Dai, Dengxin, Leal-Taixé, Laura, Pollefeys, Marc, Cremers, Daniel, Van Gool, Luc
Unsupervised Domain Adaptation (UDA) is the task of bridging the domain gap between a labeled source domain, e.g., synthetic data, and an unlabeled target domain. We observe that current UDA methods show inferior results on fine structures and tend t
Externí odkaz:
http://arxiv.org/abs/2408.16478
Autor:
Zhou, Tianfei, Zhang, Fei, Chang, Boyu, Wang, Wenguan, Yuan, Ye, Konukoglu, Ender, Cremers, Daniel
Image segmentation is a long-standing challenge in computer vision, studied continuously over several decades, as evidenced by seminal algorithms such as N-Cut, FCN, and MaskFormer. With the advent of foundation models (FMs), contemporary segmentatio
Externí odkaz:
http://arxiv.org/abs/2408.12957
Autor:
Meier, Johannes, Scalerandi, Luca, Dhaouadi, Oussema, Kaiser, Jacques, Araslanov, Nikita, Cremers, Daniel
Existing techniques for monocular 3D detection have a serious restriction. They tend to perform well only on a limited set of benchmarks, faring well either on ego-centric car views or on traffic camera views, but rarely on both. To encourage progres
Externí odkaz:
http://arxiv.org/abs/2408.11958
We propose LiFCal, a novel geometric online calibration pipeline for MLA-based light field cameras. LiFCal accurately determines model parameters from a moving camera sequence without precise calibration targets, integrating arbitrary metric scaling
Externí odkaz:
http://arxiv.org/abs/2408.11682
Autor:
Bongratz, Fabian, Golkov, Vladimir, Mautner, Lukas, Della Libera, Luca, Heetmeyer, Frederik, Czaja, Felix, Rodemann, Julian, Cremers, Daniel
The field of reinforcement learning offers a large variety of concepts and methods to tackle sequential decision-making problems. This variety has become so large that choosing an algorithm for a task at hand can be challenging. In this work, we stre
Externí odkaz:
http://arxiv.org/abs/2407.20917
Neural implicit surfaces can be used to recover accurate 3D geometry from imperfect point clouds. In this work, we show that state-of-the-art techniques work by minimizing an approximation of a one-sided Chamfer distance. This shape metric is not sym
Externí odkaz:
http://arxiv.org/abs/2407.17058
Parametric feature grid encodings have gained significant attention as an encoding approach for neural fields since they allow for much smaller MLPs, which significantly decreases the inference time of the models. In this work, we propose MeshFeat, a
Externí odkaz:
http://arxiv.org/abs/2407.13592
Plane adjustment (PA) is crucial for many 3D applications, involving simultaneous pose estimation and plane recovery. Despite recent advancements, it remains a challenging problem in the realm of multi-view point cloud registration. Current state-of-
Externí odkaz:
http://arxiv.org/abs/2407.13537
Autor:
Xia, Yan, Ding, Ran, Qin, Ziyuan, Zhan, Guanqi, Zhou, Kaichen, Yang, Long, Dong, Hao, Cremers, Daniel
Recent advances in predicting 6D grasp poses from a single depth image have led to promising performance in robotic grasping. However, previous grasping models face challenges in cluttered environments where nearby objects impact the target object's
Externí odkaz:
http://arxiv.org/abs/2407.06168