The 3rd SHApe Recovery from Partial textured 3D scans (SHARP) Workshop and Challenge will be held in conjunction with CVPR on June 19, 2022 (TBC). Research on data-driven 3D reconstruction of shapes has been very active in the past years thanks to the availability of large datasets of 3D models. However, current methods did not focus enough on two important aspects: (1) reconstructing full 3D objects with shape and texture at the same time, (2) reconstructing sharp edges that tend to be smoothed by 3D scanning.  The goal of this workshop is to promote the development of methods to recover a complete 3D scan from its partial acquisition. This should in turn foster the development of 3D modelling and processing techniques that exploit both geometry and texture.

This workshop will host a competition focusing on the reconstruction of full high-resolution 3D meshes from partial or noisy 3D scans and includes 2 challenges and 3 datasets:

  • The first challenge consists of the recovery of textured 3D scans from partial acquisition. It involoves 2 tracks:
    • Track 1: Recovering textured human body scans from partial acquisitions. The dataset used in this scope is the 3DBodyTex.v2 dataset, containing 2500 textured 3D scans. It is an extended version of the original 3DBodyTex.v1 dataset, first published in the 2018 International Conference on 3D Computer Vision, 3DV 2018.
    • Track 2: Recovering textured object scans from partial acquisitions. It involves the recovery of generic object scans from the 3DObjTex.v1 dataset, which is a subset from the ViewShape online repository of 3D scans. This dataset contains over 2000 various generic objects with different levels of complexity in texture and geometry.
  • The second challenge focuses on the recovery of fine object details in the form of sharp edges from noisy sparse scans with smooth edges. The CC3D-PSE dataset which is a new version of the CC3D dataset, introduced at the 2020 IEEE International Conference on Image Processing (ICIP), will be used for this purpose. It contains over 50k pairs of CAD models and their corresponding 3D scans. Each pair of scan and CAD model is annotated with parametric sharp edges. Given a 3D scan with smooth edges, the goal is to reconstruct the corresponding CAD model as a triangular mesh, with sharp edges approximating the ground-truth sharp edges. The second challenge invovles 2 tracks:
    • Track 1: Recovering linear sharp edges. A subset of the CC3D-PSE dataset is considered in this track which includes only linear sharp edges.
    • Track 2:  Recovering sharp edges as linear, circular, and spline segments. The whole CC3D-PSE will be used in this track.

Sponsor

 

 

Call for Participation (Challenges)

We propose two challenges. The task of the challenges is to reconstruct a full 3D textured mesh from a partial 3D scan. The first challenge focuses on the recovery of textured 3D scans from partial acquisition, while the second consists of the recovery of fine object details in the form of sharp edges from noisy sparse scans with smooth edges.

🏆 An overall 8k€ will be awarded as cash prizes to the winners.

Challenge 1

Textured Partial Scan Completion

Given Partial Textured Scans, the goal is to recover scans with completed shape and texture. More information can be found here.

 

Challenge 2

Sharp Edge Recovery in Object Scans

Given an object scan, the goal is recover sharp edges and the geometry of the corresponding CAD model. More information can be found here.

Track 1

Recovery of Partial Textured Human Body Scans

Track 2

Recovery of Partial Textured Object Scans

Track 1

Recovery of Linear Sharp Edges

Track 2

Recovery of Generic Sharp Edges

Important Dates

Website opened: 15th January 2022

Release of training datasets: 20th January 2022

Registration deadline: 20th April 2022

Release of evaluation datasets: 1st May 2022

Submission of results: 20th May 2022

Organizers

DjamilaAouadaFeb20_v1
Djamila Aouada

Chair

SnT, University of Luxembourg

djamila.aouada@uni.lu

IMG_3274_ne
Kseniya Cherenkova

Co-Chair

Artec3D, SnT

kcherenkova@artec-group.com

Anis_Kacem2
Anis Kacem

 

SnT, University of Luxembourg

anis.kacem@uni.lu

azizal
Sk Aziz Ali

 

SnT, University of Luxembourg

skaziz.ali@uni.lu

IMG_20220611_0959166
Elona Dupont

 

SnT, University of Luxembourg

elona.dupont@uni.lu

gleb_
Gleb Gusev

 

Artec3D

gleb@artec-group.com

DA457009-9004-43AA-A72C-35D40E8FC8F7_1_105_c
David Fofi

 

University of Burgundy

david.fofi@u-bourgogne.fr

Ottersten2
Bjorn Ottersten

 

SnT, University of Luxembourg

bjorn.ottersten@uni.lu