Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Panos Achlioptas"'
Publikováno v:
ACM Transactions on Graphics. 41:1-12
We present a novel method to acquire object representations from online image collections, capturing high-quality geometry and material properties of arbitrary objects from photographs with varying cameras, illumination, and backgrounds. This enables
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f732869d1a49f4439e209ac2eb23fef
https://doi.org/10.1145/3528223.3530177
https://doi.org/10.1145/3528223.3530177
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031198359
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::24d0eb54d674156d235560145bf0f82c
https://doi.org/10.1007/978-3-031-19836-6_11
https://doi.org/10.1007/978-3-031-19836-6_11
Publikováno v:
CVPR
We present a novel large-scale dataset and accompanying machine learning models aimed at providing a detailed understanding of the interplay between visual content, its emotional effect, and explanations for the latter in language. In contrast to mos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::94fbad79feb3a5964956315249448664
Shape deformation is an important component in any geometry processing toolbox. The goal is to enable intuitive deformations of single or multiple shapes or to transfer example deformations to new shapes while preserving the plausibility of the defor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d57400c368ba67530458fae623c97d1
http://arxiv.org/abs/2009.01456
http://arxiv.org/abs/2009.01456
Publikováno v:
Computer Vision – ECCV 2020 ISBN: 9783030584511
ECCV (1)
ECCV (1)
In this work we study the problem of using referential language to identify common objects in real-world 3D scenes. We focus on a challenging setup where the referred object belongs to a fine-grained object class and the underlying scene contains mul
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::82517ea469db8a12d5b2d0d13af22999
https://doi.org/10.1007/978-3-030-58452-8_25
https://doi.org/10.1007/978-3-030-58452-8_25
Publikováno v:
ICCV
In this work we explore how fine-grained differences between the shapes of common objects are expressed in language, grounded on 2D and/or 3D object representations. We first build a large scale, carefully controlled dataset of human utterances each
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f011be4882c8a61b0804e14cc94b8f0a
http://arxiv.org/abs/1905.02925
http://arxiv.org/abs/1905.02925
Autor:
Raphael Groscot, Leonidas J. Guibas, Panos Achlioptas, Anastasia Dubrovina, Mira Shalah, Fei Xia
Publikováno v:
ICCV
We present a novel neural network architecture, termed Decomposer-Composer, for semantic structure-aware 3D shape modeling. Our method utilizes an auto-encoder-based pipeline, and produces a novel factorized shape embedding space, where the semantic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c56ac56641445545de322cf5c0e60c34
http://arxiv.org/abs/1901.02968
http://arxiv.org/abs/1901.02968
Publikováno v:
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
ICCV
ICCV
This paper proposes a learning-based framework for reconstructing 3D shapes from functional operators, compactly encoded as small-sized matrices. To this end we introduce a novel neural architecture, called OperatorNet, which takes as input a set of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bbfcfe425e379b3fc12e017d4fef7559
Publikováno v:
Symposium of Geometry Processing 2019
Symposium of Geometry Processing 2019, Jul 2019, Milan, Italy. pp.187-202, ⟨10.1111/cgf.13799⟩
Computer Graphics Forum
Symposium of Geometry Processing 2019, Jul 2019, Milan, Italy. pp.187-202, ⟨10.1111/cgf.13799⟩
Computer Graphics Forum
International audience; We propose a novel construction for extracting a central or limit shape in a shape collection, connected via a functional map network. Our approach is based on enriching the latent space induced by a functional map network wit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d1ba9a298762b4e89bb53c39323f9927