LEGO-3D: Learning Generative 3D Scene Models for Training and Validating Intelligent Systems
ERC Starting Grant #850533
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. ERC Starting Grant project LEGO-3D (project number #850533) tackles 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 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 process. In particular, we focus on the following problems:
- We devise algorithms for automatic decomposition of real and synthetic scenes into latent 3D primitive representations capturing geometry, material, light and motion.
- We develop probabilistic generative models to synthesize 3D environments. In particular, we develop unconditional, conditioned and spatio-temporal scene generation networks.
- We combine differentiable and neural rendering techniques with image synthesis, yielding high-fidelity 2D renderings of the generated 3D representations while capturing ambiguities and uncertainties.
We believe that 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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