Synthetic Data Generation is useful in aiding research and engineering by providing ground truth known information otherwise is challenging to extract from a real camera sensor. Often times annotation is applied after data is collected and can be time consuming. Even though AI is improving in these areas, the extraction methods and understanding of the networks are hidden in a "black box," making it challenging with less flexibility. We use traditional and modern artist and engineering tools that are currently evolving along with next generation game engines to get you the most out of your data. In many cases hardware, camera systems, asset generation can provide great insight into a design before wasting money on prototypes and years of iterating in the dark only to throw away poorly executed systems or approaches.
Data Generation for Machine Learning, Asset Creation, Ground Truth Data
