Your Custom AI
Edge AI, Accelerated by Synthetic Data
Take your computer vision project from concept to the edge faster than ever before. We specialize in developing custom, fine-tuned AI models by leveraging our advanced synthetic data pre-training pipelines.
We generate photorealistic training scenarios to teach the AI rare edge cases and specific behaviors before it ever sees the real world. The result? A robust, hardware-accelerated device that is ready to deploy and pre-configured for your unique use case.
DeepRendering
A multi-control-layer sensor-realistic image generation platform
The idea: Work with easy to create control input images and let the DeepRendering Model transform them to realistic annotated images.
DeepRendering transformed images are not only visually better!
Even with extensive shader tuning, 3D renderings typically exhibit significant statistical deviations from real images. This is because rendering involves an immense number of interacting factors that are extremely difficult to approximate accurately. In contrast, our DeepRendering model learns to map rendered input data to real-world examples, effectively capturing true image statistics. This ability to reproduce realistic statistical properties makes a critical difference when training models with such synthetic images. When examining just the histograms, the statistical distribution of pixel values, it becomes clear how significant the differences between real and rendered images can be. In contrast, DeepRendering generates statistically plausible images, ensuring that they contribute far more meaningful information during training than computer-graphics generated data.
Real Image
DeepRendering
Computer Graphics
Ultra-realistic synthetic data in seconds
Train faster, deploy smarter, scale efficiently
With DeepRendering you can generate super realistic synthetic images from your use case that can be used for your Computer Vision task and model trainings. As an example, with DeepRendering you can quickly generate synthetic defects. With just a small number of real-world samples, it enables the creation of large and representative datasets. This not only accelerates model deployment but also results in more robust and significantly more cost-effective AI models.
What makes DeepRendering different from conventional data augmentation methods?
Traditional techniques for augmenting small datasets work by artificially increasing variation- images are flipped, shifted, rotated, or scaled. These proven methods have their place and can be helpful to a certain extent. But what is really happening here? The same information is reused over and over again – variations are simply geometric transformations. Essentially, the model only learns that it should not rely on a specific position or orientation. That’s it – nothing more, nothing less.
DeepRendering, by contrast, introduces genuine diversity into your datasets. Instead of merely transforming existing patterns, it learns the visual characteristics of defects and reproduces them – either randomly or in a controlled, targeted manner. This allows you to generate truly new and varied defect instances, resulting in more realistic training data and more powerful models.
