Synthetic data is powered by your library of 3D assets. Read on to learn about sources and techniques for acquiring 3D content for common computer vision problems.

Building computer vision systems that use synthetic data is a transformative shift from ones that use real data. While real-world data requires painstaking collection and annotation, a synthetic dataset can be constructed in a matter of minutes by a single machine learning (ML) engineer, enabling a rapid, data-centric development cycle. It is fast, so you can experiment with many dataset variations at once to find ones that improve your model.

But data sourcing is still a challenge. Instead of sourcing thousands of images and annotations, you need tens or hundreds of 3D assets such as meshes, textures, and animations. While synthetic data reduces the content requirements, the 3D nature of these assets requires getting creative about acquiring them.

Luckily, the film and video game industries have faced the same content challenges for more than forty years. In that time they have developed new techniques for content creation as well as vast repositories of content, much of which is already perfect for synthetic data.

This post will introduce the best ways for sourcing 3D content, each lending itself to a different type of computer vision application.

Source: Unity Technologies Blog

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