expertise

machine learning & 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

all expertises.

electrical

mechanical

mechatronics

optics & vision

software architecture & design

system engineering

thermal management