It provides an extensible registry of annotators and writers to address custom requirements around type of annotations and output formats needed to train AI models. Omniverse Replicator is built on the highly extensible OmniGraph architecture that allows users to easily extend the built-in functionalities to create datasets for their own needs. It allows users to easily import simulation-ready assets to build contextually aware 3D scenes to unleash a data-centric approach by creating new types of datasets and annotations previously not available.īuilt on open-source standards like Universal Scene Description (USD), PhysX, Material Definition Language (MDL), Omniverse Replicator can be easily integrated or connected to existing pipelines via extensible Python APIs. Omniverse Replicator provides deep learning engineers and researchers with a set of tools and workflows to bootstrapping model training, improve the performance of existing models or develop a new type of models that were not possible due to the lack of datasets or required annotations. Omniverse Replicator is a highly extensible framework built on a scalable Omniverse platform that enables physically accurate 3D synthetic data generation to accelerate training and performance of AI perception networks. Replicator YAML Manual and Syntax Guide.Randomizing appearance, placement and orientation of existing 3D assets with a built-in writer.Using Replicator with a fully developed scene.Using existing 3D assets with Replicator.Visualizing output folder with annotated data programmatically.Adding semantics with Semantics Schema Editor and programmatically. Core functionalities - "Hello World" of Replicator.Theory behind training with synthetic data.
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