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

Project Information

3D Reloaded

Grant agreement ID: 649323

DOI

10.3030/649323

Project closed

EC signature date 5 May 2015

Start date 1 September 2015

End date 31 August 2020

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 2 000 000,00

EU contribution

€ 2 000 000,00

Coordinated by

TECHNISCHE UNIVERSITAET MUENCHEN
Germany

Periodic Reporting for period 4 - 3D Reloaded (3D Reloaded: Novel Algorithms for 3D Shape Inference and Analysis)

Reporting period: 2020-03-01 to 2020-08-31

Summary of the context and overall objectives of the project

The isssue adddressed in the project "3D Reloaded" is the development of novel algorithms for 3D reconstruction from cameras and for 3D shape analysis. Both of these problems are of central importance for machines to understand the 3D world observed with a camera.

This is important for society as it affects a number of application domains: For the development of self-driving cars, the autonomous cars needs to understand what happens around (and in particular) in front of the car. For augmented reality, one is interested in recovering the 3D structure of our world fIn the development of medical image analysis, algorithms need to interpret and analyze changes of inner organs represented in form of 3D shapes.

The overall objectives of the project are:
1. the development of algorithms for reconstructing 3D structures from monocular and from RGB-D cameras with a particular focus on dense and realtime capable methods,
2. the development of algorithms for 3D shape analysis, including problems such as measuring shape similarity, matching of shapes and the semantic analysis of shapes.
3. For the next phase of the project, a central objective is the development of 3D shape priors for 3D reconstruction.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

We developed a number of algorithms for:
- reconstructing 3D scenes from monocular cameras with known pose,
- reconstrucing 3D scenes from RGB-D cameras with known pose,
- jointly reconstructing 3D scenes and camera trajectory from moving monocular cameras (visual odometry / simultaneous localization and mapping),
- jointly reconstructing 3D scenes and camera trajectory from moving RGB-D cameras,
- geometric shape priors for 3D reonstruction.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The algorithms we developed were in many ways beyond the state of the art - in terms of accuracy, in terms of robustness, in terms of scalability, in terms of runtime, etc.

The main emphasis and expected results until the end of the project is the development of shape priors for 3D reconstruction. This is one of the topics we are currently working on.

Fight ill-posedness with ill-posedness: Single-shot variational, Haefner et al., CVPR 2018.

Divergence-free Shape Interpolation and Correspondence, Eisenberger, Laehner, Cremers, CVPR 2019.

Direct Sparse Odometry, Engel, Koltun, Cremers, PAMI 2018.