SHARP workshop in conjunction with CVPR Call for Papers
SHARP workshop in conjunction with CVPR 2021
Call for Participation (Challenges)
We propose three challenges. The task of the challenges is to
reconstruct a full 3D textured mesh from a partial 3D scan. The first
challenge is for human bodies, while the second and third challenges
are for a variety of generic objects. The third challenge launches a
new unique dataset.
?? An overall 9k€ will be awarded as cash prizes to the winners.
CHALLENGE 1 Recovery of Human Body Scans
The task of this challenge is to reconstruct a full 3D textured mesh
from a partial 3D human scan acquisition. 3DBodyTex.v2 is used, which
consists of about 2500 clothed scans with a large diversity in
clothing and in poses.
CHALLENGE 2 Recovery of Generic Object Scans
This challenge is focused on textured 3D scans of generic objects. It
uses 3DObjectTex.v1 dataset – a subset from the ViewShape
repository – containing 2000 textured 3D scans of very diverse
objects.
CHALLENGE 3 Recovery of Feature Edges in 3D Object Scans
This challenge is focused on recovering feature edges of 3D
scans. Here, the very recently introduced CC3D dataset is
considered. The CC3D dataset contains 50k+ pairs of CAD models and
their corresponding 3D scans.
In order to participate to SHARP challenges, two submission options are possible:
* Option1: Paper (required: a paper describing the method; optional:
working source code)
* Option2: Code (required: a working implementation of the method as
source code; optional: accompanying paper).
All challenges will be available with the same deadline for
registration. By choosing option1, participants will be required to
submit an accompanying paper. For option2, instead of submitting an
accompanying paper, participants will be required to submit a
code. Submitting an accompanying paper, to be included in the
proceedings of CVPR, is still highly encouraged.
Call for Papers (Paper Submission Track)
The main focus of SHARP is to encourage paper submissions on
high-resolution 3D shape and texture recovery from partial data,
especially as accompanying papers to the challenge submissions. In
addition, all topics that relate to and serve the goal of data-driven
shape and texture processing are of interest. This includes original
contributions at different levels of data processing; for different
industrial applications, as well as proposals for new evaluation
metrics and relevant original datasets. Topics of interest include,
but are not limited to:
Textured 3D data representation and evaluation
Textured 3D scan feature extraction
Generative modelling of textured 3D scans
Learning-based 3D reconstruction
Joint texture and shape matching
Joint texture and shape completion
Semantic 3D data reconstruction
Effective 3D and 2D data fusion
Textured 3D data refinement
3D feature edge detection and refinement
High-level representations of 3D data
CAD modeling from unstructured 3D data
Authors are encouraged to submit their contributions to the SHARP 2021
submission site. All accepted papers will be included in the CVPR 2021
conference proceedings. The papers will be peer-reviewed and they must
comply with the CVPR 2021 proceedings style and format.
Important dates
Paper submission track:
Paper submission deadline: 7th of March 2021
Final decisions to authors: 1st of April 2021
Camera-Ready submission deadline: 10th of April 2021
Challenges:
Registration deadline: 22nd of February 2021
Release of training datasets: 15th of March 2021
Submission of results: 18th of May 2021
Announcement of results: 20th of June 2021
Organizers
Djamila Aouada, SnT, University of Luxembourg
Kseniya Cherenkova, SnT, Artec3D
Alexandre Saint, SnT, University of Luxembourg
David Foffi, University of Burgundy
Gleb Gusev, Artec3D
Bjorn Ottersten, SnT, University of Luxembourg
Anis Kacem, SnT, University of Luxembourg
Konstantinos Papadopoulos, SnT, University of Luxembourg
More details about the workshop can be found in https://cvi2.uni.lu/sharp2021/
Contact: For any enquiries, please feel free to contact us at shapify3D@uni.lu.