Zobrazeno 1 - 10
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pro vyhledávání: '"Theoharis, A."'
We propose Medial Atom Ray Fields (MARFs), a novel neural object representation that enables accurate differentiable surface rendering with a single network evaluation per camera ray. Existing neural ray fields struggle with multi-view consistency an
Externí odkaz:
http://arxiv.org/abs/2307.00037
Triangular meshes are the most popular representations of 3D objects, but many mesh surfaces contain topological singularities that represent a challenge for displaying or further processing them properly. One such singularity is the self-intersectio
Externí odkaz:
http://arxiv.org/abs/2206.09699
Autor:
Theoharides, Theoharis C.
Publikováno v:
In Allergy Medicine September 2024 1
Autor:
Kunze-Küllmer, Maximiliano, Goonewardene, Asthika, Kili, Sven, Theoharis, Stefanos, Rivers, Patrick
Publikováno v:
In Cytotherapy July 2024 26(7):672-680
Publikováno v:
In Computers & Graphics October 2024 123
A complete pipeline is presented for accurate and efficient partial 3D object retrieval based on Quick Intersection Count Change Image (QUICCI) binary local descriptors and a novel indexing tree. It is shown how a modification to the QUICCI query des
Externí odkaz:
http://arxiv.org/abs/2107.03368
Publikováno v:
In Annals of Allergy, Asthma & Immunology April 2024 132(4):440-454
In this paper, we address the speech denoising problem, where Gaussian, pink and blue additive noises are to be removed from a given speech signal. Our approach is based on a redundant, analysis-sparse representation of the original speech signal. We
Externí odkaz:
http://arxiv.org/abs/2104.14468
Publikováno v:
Computers & Graphics Volume 92, November 2020, Pages 55-66
A binary descriptor indexing scheme based on Hamming distance called the Hamming tree for local shape queries is presented. A new binary clutter resistant descriptor named Quick Intersection Count Change Image (QUICCI) is also introduced. This local
Externí odkaz:
http://arxiv.org/abs/2008.02916
Publikováno v:
Computers & Graphics, Volume 91, 2020, Pages 118-128
A novel shape descriptor for cluttered scenes is presented, the Radial Intersection Count Image (RICI), and is shown to significantly outperform the classic Spin Image (SI) and 3D Shape Context (3DSC) in both uncluttered and, more significantly, clut
Externí odkaz:
http://arxiv.org/abs/2007.02306