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LEGO-3D SIGNED

Learning Generative 3D Scene Models for Training and Validating Intelligent Systems

Total Cost €

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EC-Contrib. €

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Partnership

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 LEGO-3D project word cloud

Explore the words cloud of the LEGO-3D project. It provides you a very rough idea of what is the project "LEGO-3D" about.

representation    ways    primitives    recently    generative    artist    photo    training    probabilistic    relationships    significantly    environments    neural    away    deep    temporal    labeled    viewpoints    synthesis    networks    rely    full    viewpoint    material    differentiable    scenes    transformation    yielding    tackle    simulation    content    decomposition    jointly    renderings    algorithms    fidelity    primitive    lego    techniques    image    safety    expert    costly    3d    physical    data    conditioned    invariances    industry    spatio    captured    synthesize    unconditional    synthesizing    capturing    cars    simulators    2d    hard    manipulation    examples    representations    annotated    motion    realistic    entire    occlusion    generation    computer    devise    synthetic    extracted    pipeline    validation    augmentation    autonomous    ambiguities    vision    critical    shallow    automatic    designed    becomes    automate    models    amounts    scene    collecting    projections    latent    easier    light    variety    arbitrary    construction    rendering    entertainment    learning    efficient    combine    geometry    creation    considerably   

Project "LEGO-3D" data sheet

The following table provides information about the project.

Coordinator
EBERHARD KARLS UNIVERSITAET TUEBINGEN 

Organization address
address: GESCHWISTER-SCHOLL-PLATZ
city: TUEBINGEN
postcode: 72074
website: www.uni-tuebingen.de

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 1˙467˙500 €
 EC max contribution 1˙467˙500 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2019-STG
 Funding Scheme ERC-STG
 Starting year 2020
 Duration (year-month-day) from 2020-10-01   to  2025-09-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    EBERHARD KARLS UNIVERSITAET TUEBINGEN DE (TUEBINGEN) coordinator 1˙467˙500.00

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 Project objective

Recently, the field of computer vision has witnessed a major transformation away from expert designed shallow models towards more generic deep representation learning. However, collecting labeled data for training deep models is costly and existing simulators with artist-designed scenes do not provide the required variety and fidelity. Project LEGO-3D will tackle this problem by developing probabilistic models capable of synthesizing 3D scenes jointly with photo-realistic 2D projections from arbitrary viewpoints and with full control over the scene elements. Our key insight is that data augmentation, while hard in 2D, becomes considerably easier in 3D as physical properties such as viewpoint invariances and occlusion relationships are captured by construction. Thus, our goal is to learn the entire 3D-to-2D simulation pipeline. In particular, we will focus on the following problems:

(A) We will devise algorithms for automatic decomposition of real and synthetic scenes into latent 3D primitive representations capturing geometry, material, light and motion.

(B) We will develop novel probabilistic generative models which are able to synthesize large-scale 3D environments based on the primitives extracted in project (A). In particular, we will develop unconditional, conditioned and spatio-temporal scene generation networks.

(C) We will combine differentiable and neural rendering techniques with deep learning based image synthesis, yielding high-fidelity 2D renderings of the 3D representations generated in project (B) while capturing ambiguities and uncertainties.

Project LEGO-3D will significantly impact a large number of application areas. Examples include vision systems which require access to large amounts of annotated data, safety-critical applications such as autonomous cars that rely on efficient ways for training and validation, as well as the entertainment industry which seeks to automate the creation and manipulation of 3D content.

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

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