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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.

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

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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