Proprietary pipeline brings together AI and CGI
Pixel-perfect labels for complex tasks
Since the image data is generated, the attributes of every pixel are known. Complex images are automatically labeled, enabling you to efficiently and cost-effectively train complex models.
Complicated Semantic Segmentation
Detailed Sub-Segmentation
More robust models through more diverse data.
Programmatic control lets you create a high degree of inter- and intra- class variability. Modify geometries, textures, environments, lighting, imaging modality & camera location to generate vast amounts of diverse training data to power higher performing and more generalized computer vision models.
More robust models through more diverse data.
Programmatic control lets you create a high degree of inter- and intra- class variability. Modify geometries, textures, environments, lighting, imaging modality & camera location to generate vast amounts of diverse training data to power higher performing and more generalized computer vision models.
More robust models through more diverse data.
Programmatic control lets you create a high degree of inter- and intra- class variability. Modify geometries, textures, environments, lighting, imaging modality & camera location to generate vast amounts of diverse training data to power higher performing and more generalized computer vision models.
New labels to create new models
Human labelers are unable to provide a wide-range of necessary labels for emerging applications. Synthesis’ platform can provide an expanded set of pixel-accurate labels (e.g. depth, 3D segmentation, complex pose, surface normals, velocity, etc) to enable you to build new and more capable models.
Build better products more efficiently with virtual prototyping
Model camera placement, image modality, resolution and many more attributes to inform the overall design of your computer vision systems. Using synthetic data enables you to make smarter choices and better understand model performance before building and deploying hardware.
