Reference : SHARP 2020: The 1st Shape Recovery from Partial Textured 3D Scans Challenge Results
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/44711
SHARP 2020: The 1st Shape Recovery from Partial Textured 3D Scans Challenge Results
English
Saint, Alexandre Fabian A [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
Kacem, Anis mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
Cherenkova, Kseniya [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Papadopoulos, Konstantinos [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
Chibane, Julian [Max-Planck-Institut für Informatik > Computer Vision and Machine Learning]
Pons-Moll, Geraerd [Max-Planck-Institut für Informatik > Computer Vision and Machine Learning]
Gusev, Gleb [ARTEC 3D]
Foffi, David [University of Burgundy]
Aouada, Djamila [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
Ottersten, Björn [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
23-Aug-2020
Yes
SHARP workshop, ECCV 2020
23-08-2020
[en] The SHApe Recovery from Partial textured 3D scans challenge, SHARP 2020, is the first edition of a challenge fostering and benchmarking methods for recovering complete textured 3D scans from raw incomplete data. SHARP 2020 is organized as a workshop in conjunction with ECCV 2020. There are two complementary challenges, the first one on 3D human scans, and the second one on generic objects. Challenge 1 is further split into two tracks, focusing, first, on large body and clothing regions, and, second, on fine body details. A novel evaluation metric is proposed to quantify jointly the shape reconstruction, the texture reconstruction, and the amount of completed data. Additionally, two unique datasets of 3D scans are proposed, to provide raw ground-truth data for the benchmarks. The datasets are released to the scientific community. Moreover, an accompanying custom library of software routines is also released to the scientific community. It allows for processing 3D scans, generating partial data and performing the evaluation. Results of the competition, analyzed in comparison to baselines, show the validity of the proposed evaluation metrics and highlight the challenging aspects of the task and of the datasets. Details on the SHARP 2020 challenge can be found at https://cvi2.uni.lu/sharp2020/
Fonds National de la Recherche - FnR ; Fonds National de la Recherche - FnR
http://hdl.handle.net/10993/44711
FnR ; FNR11806282 > Alexandre Saint > > Accurate 3D human body shape modelling and fitting under clothing > 01/09/2017 > 14/01/2021 > 2017

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