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!
In defense and security applications, where precision, reliability, and situational awareness are critical, the performance of computer vision models depends in 80% on the inputted labeled data. Annotation is the process of adding structured information to raw image or video data so that AI systems can learn to interpret the visual world. It enables models […]
Computer vision is the field of AI that enables machines to interpret and make decisions based on visual input. Tasks range from classifying images and detecting objects to understanding spatial context and tracking motion over time. But the success of a computer vision model hinges on its ability to generalize across varied, real-world scenarios. Model’s […]
Transitioning from real-world data to synthetic datasets isn’t always easy, especially for teams that have relied on conventional methods for years. The most common objections include: The Case for Synthetic Data 1. Faster, Cost-Effective Data Generation Real-world data collection is slow and costly, often requiring extensive fieldwork and manual annotation. Synthetic datasets, on the other […]
