Explore our products

Discover the possibilities of our Digital Twin Platform. From custom digital twins to advanced generation of synthetic data designed to accelerate innovation and enhance efficiency.

Generative AI

makes everyone an expert

Generative AI has reduced the effort for image generation in the creative sector by orders of magnitude, allowing amateurs to create high-end professional images!

Is something similar possible for industrial applications?

Prompt: Show me the man from @IMG_2 framed through the windshield of a car like the reference @IMG_1.

DeepRendering

A controllable sensor-realistic image generation model

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!

Learn from real samples – Create realistic annotated synthetic variants

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.

Possible application areas

  • Quality control / Defect Detection
  • Component and part verification
  • Object detection / Pick-and-place
  • Simulation of manufacturing processes
  • Training and assistance systems
  • Traceability & documentation