AI Verse provides a self-service platform that generates high-quality, fully labeled synthetic image datasets using procedural technology for training computer vision applications. This solution addresses the challenges of acquiring real-world data by enabling users to customize scene parameters and produce diverse datasets quickly and efficiently.
Funding
$3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
TCFounders
Product
Problem
Acquiring and labeling real-world image data for training computer vision models is often expensive, time-consuming, and raises privacy concerns. The limited availability of diverse and accurately labeled datasets can hinder the development and performance of AI applications.
Solution
AI Verse offers a self-service platform that leverages procedural technology to generate high-quality, fully labeled synthetic image datasets for training computer vision applications. The platform allows users to customize various scene parameters, including environments, objects, lighting, and activities, to produce diverse and unbiased datasets on demand. By eliminating the need for real-world data capture, AI Verse addresses privacy concerns and significantly reduces the cost and time associated with data acquisition and labeling. The generated images contain pixel-perfect labels, ensuring high accuracy and improving the performance of AI models.
Target Audience
The primary target audience includes AI/ML engineers, computer vision researchers, and organizations developing AI models for applications such as human detection, military vehicle detection, weapon detection, drone detection, and fall detection.
Features
- Procedural engine for generating diverse synthetic images with customizable scene parameters
- Support for both indoor (HELIOS) and outdoor (GAIA) applications
- On-demand image generation, eliminating the need for pre-existing datasets
- Full control over configurations, including scenes, sensors, lighting, activities, and labels
- Eight pixel-perfect labels included: classes, instances, depth, normals, 2D/3D bounding boxes, 2D/3D keypoints, skeletons, and color
- Automated system ensures high accuracy and reduces the risk of inaccuracies
- 3D scene procedural generation using stochastic decomposition trees
- Standardized 3D asset and materials databases