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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.

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

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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