BIFROST

Generate Synthetic Data For Physical AI

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Description

BIFROST is an advanced AI tool designed to accelerate the development and validation of artificial intelligence for physical systems. It empowers teams to generate physically accurate synthetic datasets within detailed 3D simulations, thereby reducing the dependency on real-world data collection and manual labeling. This approach allows AI models to be trained and validated more rapidly and efficiently across a multitude of critical conditions.

The platform specializes in creating pixel-perfect labels and rich scenario metadata, moving beyond traditional 2D bounding boxes to offer precise segmentation for deeper AI understanding. Users can effortlessly create new scenarios and datasets in minutes, enabling AI to quickly adapt to changing environments, objects, and tasks. BIFROST also facilitates comprehensive stress testing of AI perception systems by simulating countless scenarios, helping to identify and mitigate potential failures before deployment.

Key Features

Synthetic Data Generation: Train and validate AI faster by generating physically accurate synthetic datasets in 3D simulation, no real-world data needed.
Pixel-Perfect Labels & Scenario Metadata: Move beyond basic 2D boxes with precise segmentation and rich metadata for deeper AI scenario understanding, no manual labeling required.
Rapid Scenario Adaptation: Create new scenarios and datasets in minutes to empower AI to quickly learn new skills and adapt to changing environments, objects, and tasks.
Comprehensive AI Stress Testing: Catch failures before they happen with full-spectrum testing across crucial conditions by simulating countless scenarios.
High Realism: Bridge the domain gap with highly realistic synthetic data, including customizable sensor emulation, dynamic global lighting simulations, and PBR materials.
Sensor Model Simulation: Customize pre-built sensor intrinsics to align with existing perception systems and sensor fusion specifications.
High-Fidelity 3D Assets: Access thousands of objects designed with physically accurate details and Physically Based Rendering (PBR) materials.
Lighting & Environmental Simulation: Create complex scenarios with complete control over lighting, visibility, and atmospheric effects to mirror real-world challenges.
Ease of Use: Built for fast-moving teams, easily configurable to specific use cases with templates and presets library for quick dataset generation.
Systematic AI Performance Improvement: Identify AI failures with guided analysis tools and programmatically generate data patches to fix problems before deployment.

Use Cases

Prototyping: Accelerate AI prototyping by removing hardware dependencies and data collection bottlenecks.
Bootstrapping: Jumpstart AI model development without waiting for time-consuming real-world data collection and labeling.
Class Balancing: Fix class imbalances in datasets by enriching training sets with more images of less common objects.
Performance Boosting: Expand training data variability with new levels of diversity and complexity to enhance AI model performance.
Automated Labeling Support: Use synthetically-trained AI models to automatically sort through unlabeled imagery.
Demo Optimization: Optimize AI models for flawless execution in live demonstrations by training on diverse simulated scenarios.