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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.

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

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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