In one recent project, moving from a small set of highly realistic assets to a broad spectrum of 3D models and textures from the Unity Asset Store substantially increased model performance – a bigger jump than any other experiment we tried.Īssets from SynthDet were created using a Flatbed scanner and 3D modeling. To achieve the variety required to help the model to generalize, you need to source variations on every relevant axis, including shape, material, color, and pose.Įven nonrealistic assets like cartoonish or odd textures and colors can lead to better generalization in the model. Online asset sources are great sources of data when the task requires detecting broad classes of objects like “chair” or “cat,” where a wide variety of content is required. Sourcing from different artists increases the breadth of data even further, reducing bias and overfitting in the model. These can all be randomized during dataset generation to create nearly infinite variations on each object.
![unity free 3d models unity free 3d models](https://styly.cc/wp-content/uploads/2018/11/free3D-01.png)
This post will introduce the best ways for sourcing 3D content, each lending itself to a different type of computer vision application.Īssets often come in “packs,” including various 3D models, texture variations, materials, and animations. 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. Luckily, the film and video game industries have faced the same content challenges for more than forty years. While synthetic data reduces the content requirements, the 3D nature of these assets requires getting creative about acquiring them.
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Instead of sourcing thousands of images and annotations, you need tens or hundreds of 3D assets such as meshes, textures, and animations. It is fast, so you can experiment with many dataset variations at once to find ones that improve your model.īut data sourcing is still a challenge. 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. Read on to learn about sources and techniques for acquiring 3D content for common computer vision problems.īuilding computer vision systems that use synthetic data is a transformative shift from ones that use real data. Synthetic data is powered by your library of 3D assets.