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3D Reloaded SIGNED

3D Reloaded: Novel Algorithms for 3D Shape Inference and Analysis

Total Cost €


EC-Contrib. €






Project "3D Reloaded" data sheet

The following table provides information about the project.


Organization address
address: Arcisstrasse 21
postcode: 80333

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Total cost 2˙000˙000 €
 EC max contribution 2˙000˙000 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2014-CoG
 Funding Scheme ERC-COG
 Starting year 2015
 Duration (year-month-day) from 2015-09-01   to  2020-08-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

Despite their amazing success, we believe that computer vision algorithms have only scratched the surface of what can be done in terms of modeling and understanding our world from images. We believe that novel image analysis techniques will be a major enabler and driving force behind next-generation technologies, enhancing everyday life and opening up radically new possibilities. And we believe that the key to achieving this is to develop algorithms for reconstructing and analyzing the 3D structure of our world.

In this project, we will focus on three lines of research:

A) We will develop algorithms for 3D reconstruction from standard color cameras and from RGB-D cameras. In particular, we will promote real-time-capable direct and dense methods. In contrast to the classical two-stage approach of sparse feature-point based motion estimation and subsequent dense reconstruction, these methods optimally exploit all color information to jointly estimate dense geometry and camera motion.

B) We will develop algorithms for 3D shape analysis, including rigid and non-rigid matching, decomposition and interpretation of 3D shapes. We will focus on algorithms which are optimal or near-optimal. One of the major computational challenges lies in generalizing existing 2D shape analysis techniques to shapes in 3D and 4D (temporal evolutions of 3D shape).

C) We will develop shape priors for 3D reconstruction. These can be learned from sample shapes or acquired during the reconstruction process. For example, when reconstructing a larger office algorithms may exploit the geometric self-similarity of the scene, storing a model of a chair and its multiple instances only once rather than multiple times.

Advancing the state of the art in geometric reconstruction and geometric analysis will have a profound impact well beyond computer vision. We strongly believe that we have the necessary competence to pursue this project. Preliminary results have been well received by the community.


year authors and title journal last update
List of publications.
2018 Yvain Quéau, Bastien Durix, Tao Wu, Daniel Cremers, François Lauze, Jean-Denis Durou
LED-Based Photometric Stereo: Modeling, Calibration and Numerical Solution
published pages: 313-340, ISSN: 0924-9907, DOI: 10.1007/s10851-017-0761-1
Journal of Mathematical Imaging and Vision 60/3 2019-06-07
2016 O. Litany, E. Rodolà, A. M. Bronstein, M. M. Bronstein, D. Cremers
Non-Rigid Puzzles
published pages: 135-143, ISSN: 0167-7055, DOI: 10.1111/cgf.12970
Computer Graphics Forum 35/5 2019-06-07

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The information about "3D RELOADED" are provided by the European Opendata Portal: CORDIS opendata.

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