Project description DEENESFRITPL Novel image analysis techniques advance modelling of 3D world Computer vision algorithms hold great potential for modelling and understanding the visual world. The ERC-funded 3D Reloaded project aims to develop novel image analysis techniques, focusing on reconstructing and analysing the 3D structure of the world. Research will be geared towards three areas: developing real-time 3D reconstruction algorithms using standard colour cameras and RGB-D cameras; creating (near-)optimal algorithms for 3D shape analysis; and designing shape priors (models describing object geometry) for 3D reconstruction, either learned from shape samples or acquired during the process. Advances in geometric reconstruction and analysis will have significant implications beyond the field of computer vision. Show the project objective Hide the project objective 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. Fields of science engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehiclesengineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensorsoptical sensorsnatural sciencescomputer and information sciencesartificial intelligencecomputer visionnatural sciencesmathematicspure mathematicsgeometrynatural sciencescomputer and information sciencessoftwaresoftware applicationssimulation software Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Topic(s) ERC-CoG-2014 - ERC Consolidator Grant Call for proposal ERC-2014-CoG See other projects for this call Funding Scheme ERC-COG - Consolidator Grant Host institution TECHNISCHE UNIVERSITAET MUENCHEN Net EU contribution € 2 000 000,00 Address Arcisstrasse 21 80333 Muenchen Germany See on map Region Bayern Oberbayern München, Kreisfreie Stadt Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 2 000 000,00 Beneficiaries (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all TECHNISCHE UNIVERSITAET MUENCHEN Germany Net EU contribution € 2 000 000,00 Address Arcisstrasse 21 80333 Muenchen See on map Region Bayern Oberbayern München, Kreisfreie Stadt Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 2 000 000,00
