Understanding Synthetic Image Data

Data Generation

Synthetic images are pictures generated using computer graphics, simulation methods, and Artificial Intelligence (AI) to represent reality with high fidelity. This technology is revolutionizing the way we create datasets for machine vision and other AI applications.
Synthetic images provide the opportunity to produce vast amounts of varied, high-quality vision data that are optimal for specific situations and edge cases that are challenging to gather in the real-world environment.

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Real vs Synthetic Images

How We Generate Synthetic Image Data - Simulation

Making use of the vast possibilities offered by Computer Generated Imagery (CGI), and generative AI, we generate synthetic images that comprehensively represent 2D and 3D scenes. Our process, based on ‘Structured Domain Randomization’ and rendering, ensures that each variable, feature, and detail in a scene is represented with a specified multitude of values within relevant domain parameters.

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Example showing one variable (camera transformation) for the use case plate recognition. By adding different plate and car types, etc. the data generated would become more diverse.

How We Generate Synthetic Image Data - Enhancing Realism

To enhance realism, we employ generative AI approaches that incorporate pixel-level details from your real data. This includes camera characteristics such as sensor noise patterns, contrast, and features like dirt and blur. This combination of techniques ensures that our synthetic images closely mimic real-world conditions.

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Substrate with additive manufactured welding seam.

Example showing details and realism added with generative AI, to represent welding seams in an additive manufacturing process.

Avoiding AI hallucinations with structured 3D simulations as a basis

Using 3D simulation techniques in conjunction with generative AI is crucial because relying solely on generative AI can lead to unrealistic or "hallucinated" data that doesn't accurately represent real-world conditions. 3D simulations provide a structured and controlled foundation, ensuring that the generated data accurately reflects the physical properties and behaviours of the objects and environments being modelled. This foundation is then enhanced with generative AI to add realistic details and variations, resulting in high-fidelity synthetic images that are both accurate and reliable for training robust machine vision models.

Image showing 4 results of generative AI created images, 3 representing substrate with additive manufactured welding seam. The 4th one showing something weird. Hallucination of the generative AI model

When AI gets too creative

Advantages of Synthetic Image Data

In industrial machine vision projects, capturing and annotating real data is often resource-intensive and not always feasible. Synthetic image datasets provide an ideal solution, creating exhaustive training and validation datasets efficiently and effectively. While we do not intend to replace real image data, our experience shows that supplementing a small set of real images with a large set of synthetic images yields optimal results in training and validating machine vision models.

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Synthetic image data is annotated

Inconsistent labels and a resource intensive annotation process are a thing of the past.‍ With our method of generating synthetic images, you can completely skip the manual annotation process. We deliver images with pixel precise labels, based on your computer vision task.

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Real image with overlay of 3D bounding boxes of a bin on a conveyor belt with metal parts in it

Semantic Segmentation of a bin on a conveyor belt with metal parts in it

Instance Segmentation of a bin on a conveyor belt with metal parts in it

Depth Channel of a bin on a conveyor belt with metal parts in it

Normal Channel of a bin on a conveyor belt with metal parts in it

Additional Resources

Explore Key Concepts and Benefits of Synthetic Data and corresponding Annotations.

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Why Use Synthetic Data?

Understand the benefits of synthetic data, including enhanced model training, cost efficiency, and the ability to generate rare or hard-to-capture scenarios.

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How Real is Synthetic Data?

Explore the realism and accuracy of synthetic data compared to real-world data, and how it can be tailored to match specific use cases.

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Annotations

Explore the critical role of high-quality annotations in dataset preparation. Our synthetic image data comes fully annotated, as our generation process precisely tracks and identifies every element within each image, ensuring consistent and accurate labelling.

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3D Rendering

Our 3D rendering process leverages advanced computer graphics techniques to create highly realistic and detailed synthetic images. This approach allows us to simulate a wide range of scenarios and environments.

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Generative Approaches

Our generative data creation techniques use advanced AI models to enhance realism and add details to the 3D rendered synthetic image.

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