Announced at GTC, technical artists, software developers, and ML engineers can now build custom, physically accurate, synthetic data generation pipelines in the cloud with NVIDIA Omniverse Replicator. Omniverse Replicator is a highly extensible framework built on the NVIDIA Omniverse platform that enables physically accurate 3D synthetic data generation to accelerate the training and accuracy…
]]>Synthetic data is an important tool in training machine learning models for computer vision applications. Researchers from NVIDIA have introduced a structured domain randomization system within Omniverse Replicator that can help you train and refine models using synthetic data. Omniverse Replicator is an SDK built on the NVIDIA Omniverse platform that enables you to build custom synthetic…
]]>Companies providing synthetic data generation tools and services, as well as developers, can now build custom physically accurate synthetic data generation pipelines with the Omniverse Replicator SDK. Built on the NVIDIA Omniverse platform, the Omniverse Replicator SDK is available in beta within Omniverse Code. Omniverse Replicator is a highly extensible SDK built on a scalable Omniverse…
]]>Deploying an autonomous robot to a new environment can be a tough proposition. How can you gain confidence that the robot’s perception capabilities are robust enough, so it performs safely and as planned? Trimble faced this challenge when it started building plans to deploy Boston Dynamics’ Spot in a variety of indoor settings and construction environments. Trimble needed to tune the machine…
]]>One of the main challenges and goals when creating an AI application is producing a robust model that is performant with high accuracy. Building such a deep learning model is time consuming. It can take weeks or months of retraining, fine-tuning, and optimizing until the model satisfies the necessary requirements. For many developers, building a deep learning AI pipeline from scratch is not a…
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