Synthetic Data

What Are Synthetic Data?

Synthetic data is algorithmically generated information that replicates real-world patterns without using sensitive or proprietary data. Unlike traditional datasets that require manual collection, synthetic data is created through procedural generation, 3D modeling, or neural networks. This method helps organizations overcome challenges like data scarcity, privacy regulations, and high annotation costs while preserving statistical validity for machine learning.

How Synthetic Data Works

NYRIS's synthetic data generation process combines computer-aided design (CAD) expertise with advanced neural rendering techniques:

1. CAD Model Ingestion: Industrial partners provide 3D CAD files of machinery components, products, or retail items. NYRIS's pipeline automatically parses geometric data, material properties, and assembly hierarchies into a unified digital twin format compatible with rendering engines.
2. Scene Parameterization: Engineers define environmental variables like lighting conditions (studio, factory floor, outdoor), camera angles, occlusions, and surface wear patterns. For manufacturing use cases, this includes simulating oil stains, rust, or partial visibility in tight machinery spaces.
3. Domain-Randomized Rendering: Using Unreal Engine-based systems, NYRIS generates millions of unique image variations by randomly combining materials, lighting, angles, and environmental effects. A single CAD model can produce over 10,000 synthetic training images covering edge cases rarely seen in real-world photos.

This synthetic data trains convolutional neural networks (CNNs) to recognize parts under diverse conditions, achieving 99.7% accuracy in NYRIS's visual search benchmarks.

Applications

Manufacturing

NYRIS's synthetic data enables manufacturers like DMG Mori and Trumpf to automate spare parts identification from blurry maintenance photos. By training AI on CAD-derived images showing components under corrosion, partial obstruction, or low-light conditions, clients reduce equipment downtime by 85% compared to manual lookup processes.

E-commerce

IKEA uses NYRIS-generated synthetic imagery to train visual search models for identifying furniture across 500 million SKUs. Synthetic data simulates varying home environments, allowing accurate product recognition despite cluttered backgrounds or unusual camera angles.

Automotive

Daimler integrates synthetic data into quality control systems, using rendered images of car parts to detect microscopic defects during assembly. This synthetic training approach reduced false positives by 40% compared to models trained solely on factory camera feeds.

Benefits

1. Cost Efficiency: Generating synthetic data costs 90% less than manual photo collection and annotation for industrial use cases. NYRIS clients eliminate expenses related to photography equipment, site visits, and GDPR-compliant data handling.
2. Edge Case Coverage: Synthetic pipelines create rare scenarios like damaged components or extreme lighting that real-world datasets lack. NYRIS's manufacturing partners achieved 30% higher fault detection rates after switching to synthetic-trained models.
3. Speed to Deployment: NYRIS can generate 1 million labeled training images from CAD files within 72 hours, accelerating AI deployment timelines by 6-8 months compared to traditional data collection.