Gaia Procedural Engine offers full control over scene parameters:
1.
Get Started
Create a project and add your first batch. You can add as many batches as you want to each project.
2.
Set the scene
Select the type of environment you need. Add specific objects of interest from a catalog with 3D assets. Your objects of interest are automatically added to each scene.
3.
Call the Action
Select the activities you are interested in. Set various parameters related to the characters you are adding such as age, gender, physical characteristics, ethnicity, etc.
4.
Lights
For each batch, select several lighting scenarios from a catalog including various artificial and natural lighting conditions. You can even simulate pictures taken with a flash if desired.
5.
Camera
Set your camera’s intrinsic and extrinsic parameters to match your use case. For example, simulate images from a fixed surveillance camera, a drone, satellite image.
6.
Generate
Select the labels you need among instance and semantic segmentation, depth image, 3D normal image, albedo image, Lambertian reflectance model, or skeleton key points. Next, choose the number of scenes and images per scene. Then, generate your fully labeled dataset.
Generate Images in days, not months with Procedural Engine GAIA!
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Computer vision engineers are at the forefront of teaching machines to “see” and understand the world. Their daily practices, and ultimately the pace of AI innovation, are shaped by the kind of data they use—either real-life imagery painstakingly collected from the physical world, or synthetic data generated by advanced simulation engines. Let’s examine how these […]
Developing autonomous drones that can perceive, navigate, and act in complex, unstructured environments relies on one critical asset: high-quality, labeled training data. In drone-based vision systems—whether for surveillance, object detection, terrain mapping, or BVLOS operations—the robustness of the model is directly correlated with the quality of the dataset. However, sourcing real-world aerial imagery poses challenges: […]
