Synthetic images are computer generated images which represent the real world
By simulating the data in a virtual environment, it is possible to influence every parameter that has an impact on the images. All possible light scenarios, as well as camera positions, environments and actions can be displayed.
Synthetic images are created natively with perfect pixel-wise annotated ground truth labels. Synthetic datasets can therefore be used for every computer vision task from image classification over instance segmentation or anomaly detection.
Real Images vs Syntehtic Images
Synthetic images offer the best trade off between quality and price for training object detection and segmentation models. Since images are created digitally on computers in a real world environment, the whole process of creating images and labels is highly scalable due to the help of cloud computing. By rendering the images in the cloud the number of images per time frame is only limited to the budget. It doesn’t matter if you need 1 or 1.000.000 images, you will receive both within a short period of time. While creating the dataset synthetically we assure that the dataset will be unbiased due to quality control steps during the creation process and hand this report with every dataset that is sent to you. Since variance in light settings is the most crucial quality indicator for a dataset besides the objects, we assure that there will be no image with the same light setting.
Making every image in the dataset unique which will cause a big impact in model performance when rolled out into production. Because our images are created by computers we not only get perfect labels on each object in the image over all images but also have pixel wise annotations and depth maps.