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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.

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

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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