COMPUTER VISION ACCELERATED WITH SYNTHETIC DATA

Real data is: 

1. Time-consuming and expensive to acquire and label 
2. Often limited to common scenarios 
3. Ineffective in modeling for edge cases and rare objects

Synthetic data generation empowers engineering teams to design the training data they actually need, including rare events, foundational cases for experimentation, and diverse variations—before deployment or to update AI systems quickly. 

Yes, when done correctly.

Low-fidelity synthetic data can actually hurt models. Well-labeled, physics-based synthetic data accelerates training, improves model performance, and fills data gaps left by real imagery.

Rendered.ai focuses on training-ready realism for complex systems, not marketing art. 

Sometimes—but typically it is used to augment real data. 

The winning formula for using synthetic imagery in computer vision (CV) engineering: 

1. Generate customized synthetic data to bootstrap models quickly.
2. Extend training data to cover rare events synthetically. 
3. Auto-label real data to effectively merge real and synthetic into robust training datasets. 
4. Train CV models and infer performance on real-world test scenarios to inform data improvements. 
5. Iterate synthetic data generation to optimize model performance with the right mix of real-to-synthetic training data. 

Synthetic data acts as a force multiplier, reducing engineering headaches, lost time, and dollars to insufficient training information for computer vision systems. 

Every synthetic image generated on the Rendered.a platform and by our team of experts on behalf of our customers is fully labeled at creation. 

That means: 

1. Consistent, custom annotations mapped to the desired format 
2. No tedious, time-consuming manual annotation 
3. Immediate ground truth for computer vision model training and evaluation 

Rendered.ai also offers auto-annotation services for real datasets using models trained on synthetic data on the Rendered.ai platform — enhancing the value of the existing datasets you’ve been waiting to use. 

Synthetic data generated with Rendered.ai shines when: 

1. Training AI for rare events is important. 
2. Sensors are complex (e.g., synthetic aperture radar, infrared, hyperspectral, multispectral, and x-ray). 
3. Cost, access, privacy constraints, or risk limits real data collection. 

This comes up most often when engineering vision-based AI for: 

• Autonomous systems 

• Physical AI and robotics  

• Drones and counter-UAS defense systems 

• Satellite and aerial imagery 

• Manufacturing inspection 

• Maritime, transportation, and logistics 

• Security and surveillance 

If you're having trouble training models for all the test scenarios and edge cases needed, working with complex sensor types, or filling a massive data gap, synthetic data probably belongs in your AI pipeline. 

Rendered.ai supports RGB cameras and all advanced CV sensor modalities, which can be difficult to simulate and acquire viable real-world training data for, including: 

1. Synthetic Aperture Radar (SAR) 
2. Infrared (IR) 
3. Thermal 
4. Multispectral & hyperspectral 
5. X-ray 
6. Custom and emerging sensors 

This is where more generic synthetic data vendors quietly tap out and Rendered.ai excels.