Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Noam Aigerman"'
Autor:
Xingyi Du, Danny M. Kaufman, Qingnan Zhou, Shahar Z. Kovalsky, Yajie Yan, Noam Aigerman, Tao Ju
Publikováno v:
ACM Transactions on Graphics. 40:1-18
Injective parameterizations of triangulated meshes are critical across applications but remain challenging to compute. Existing algorithms to find injectivity either require initialization from an injective starting state, which is currently only pos
Publikováno v:
SIGGRAPH Asia 2022 Conference Papers.
Publikováno v:
ACM Transactions on Graphics. 40:1-11
Given a solid 3D shape and a trajectory of it over time, we compute its swept volume - the union of all points contained within the shape at some moment in time. We consider the representation of the input and output as implicit functions, and lift t
This work is concerned with a representation of shapes that disentangles fine, local and possibly repeating geometry, from global, coarse structures. Achieving such disentanglement leads to two unrelated advantages: i) a significant compression in th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::033eb61390800b751b5e7a1c82ee0800
http://arxiv.org/abs/2204.02289
http://arxiv.org/abs/2204.02289
Möbius transformations play an important role in both geometry and spherical image processing - they are the group of conformal automorphisms of 2D surfaces and the spherical equivalent of homographies. Here we present a novel, Möbius-equivariant s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e3b0a50f34a2bf75f74a960c668f702c
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031200618
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::91ee5a5fe4caebd61077f889e38734bb
https://doi.org/10.1007/978-3-031-20062-5_29
https://doi.org/10.1007/978-3-031-20062-5_29
Publikováno v:
CVPR
We introduce a deep generative network for 3D shape detailization, akin to stylization with the style being geometric details. We address the challenge of creating large varieties of high-resolution and detailed 3D geometry from a small set of exempl
Autor:
Noam Aigerman, Mikaela Angelina Uy, Leonidas J. Guibas, Vladimir G. Kim, Siddhartha Chaudhuri, Minhyuk Sung
Publikováno v:
CVPR
We propose a novel technique for producing high-quality 3D models that match a given target object image or scan. Our method is based on retrieving an existing shape from a database of 3D models and then deforming its parts to match the target shape.
Publikováno v:
CVPR
Maps are arguably one of the most fundamental concepts used to define and operate on manifold surfaces in differentiable geometry. Accordingly, in geometry processing, maps are ubiquitous and are used in many core applications, such as paramterizatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be278c8a6d0826f2f21fff743d5090f9
http://arxiv.org/abs/2103.16942
http://arxiv.org/abs/2103.16942
Autor:
Sanjeev Muralikrishnan, Siddhartha Chaudhuri, Noam Aigerman, Vladimir G. Kim, Matthew Fisher, Niloy J. Mitra
We investigate the problem of training generative models on a very sparse collection of 3D models. We use geometrically motivated energies to augment and thus boost a sparse collection of example (training) models. We analyze the Hessian of the as-ri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::800e19de1cc919c08ef196bd87b7bcb0