Reference : SHARP 2020: The 1st Shape Recovery from Partial Textured 3D Scans Challenge Results

	Document type :	Scientific congresses, symposiums and conference proceedings : Unpublished conference
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/44711

Title :	SHARP 2020: The 1st Shape Recovery from Partial Textured 3D Scans Challenge Results
Language :	English
Author, co-author :	Saint, Alexandre Fabian A [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
	Kacem, Anis [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) > >]
Publication date :	23-Aug-2020
Peer reviewed :	Yes
Event name :	SHARP workshop, ECCV 2020
Event date :	23-08-2020
Abstract :	[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/
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Computer Vision Imaging & Machine Intelligence (CVI²)
Funders :	Fonds National de la Recherche - FnR ; Fonds National de la Recherche - FnR
Permalink :	http://hdl.handle.net/10993/44711
FnR project :	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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