demcon2020_ 5
Deep technical expertise

AI algorithms & synthetic data.

Imagine a pipe inspection robot. How do you train its vision algorithm when neither the robot nor the piping network has been built yet? In other use cases, some real-life data may be available, but the dataset is imbalanced, critical edge cases are missing or annotation is lacking. A large set of diverse, well-annotated and relevant data is key to developing a successful machine learning algorithm. Synthetic training data offers a solution where this data is not available or incomplete. Demcon develops project-specific simulations which generate near-infinite permutations of specific 3D environments. These environments are sampled by a range of virtual sensors to generate rich, well-balanced datasets.

highlights

  • Digital Twin of project-specific 3D environment generates rich datasets and annotation
  • procedural methods create near-infinite permutations of objects and environments
  • pixel perfect annotation
  • simulation of a range of sensors, e.g. RGB(D), infrared, Lidar, et cetera
  • rare edge cases can be generated at will to balance the dataset
  • enables Machine Learning where data or accurate annotation is not obtainable
  • develop in silico: digital twin of environment, device and sensors

a known ground thruth provides perfect annotation.

A simulation provides a known ground truth, enabling rich, pixel-perfect annotation. This includes annotation which is typically very hard or impossible to do, such as segmentation, depth, surface normals, optical flow, consistent quality indicators on a per-pixel or per-object basis and much more.

Content_synthetic data

Demcon data driven solutions

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