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pro vyhledávání: '"Haitman, Yuval"'
In this paper, we adopt the Universal Manifold Embedding (UME) framework for the estimation of rigid transformations and extend it, so that it can accommodate scenarios involving partial overlap and differently sampled point clouds. UME is a methodol
Externí odkaz:
http://arxiv.org/abs/2408.12380
Autor:
Bialer, Oded, Haitman, Yuval
Object detection in radar imagery with neural networks shows great potential for improving autonomous driving. However, obtaining annotated datasets from real radar images, crucial for training these networks, is challenging, especially in scenarios
Externí odkaz:
http://arxiv.org/abs/2404.18150
Autor:
Haitman, Yuval, Bialer, Oded
Publikováno v:
2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2024, pp. 1627-1636
Automotive radars have an important role in autonomous driving systems. The main challenge in automotive radar detection is the radar's wide point spread function (PSF) in the angular domain that causes blurriness and clutter in the radar image. Nume
Externí odkaz:
http://arxiv.org/abs/2404.17861
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