SYNTHETIC DATA: IMPROVING TIME TO MARKET AND COST EFFICIENCY IN COMPUTER VISION

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. 

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, not pretty marketing pictures. 

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

The winning formula for 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 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 data using models trained on synthetic datasets on the Rendered.ai platform — enhancing the value of 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: 

4. Autonomous systems 

5. Physical AI and robotics  

          6. Drones and counter-UAS defense systems 

          7. Satellite and aerial imagery 

          8. Manufacturing inspection 

          9. Maritime, transportation, and logistics 

         10. 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 specializes in advanced modalities, that 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. 

There are 3 things most synthetic data vendors don’t do well—Rendered.ai does by default:

1. Physics-based accuracy 

Images generated abide automatically by the laws of physics with respect to the interaction of lighting, materials, sensor physics, and geometry in each scene.

2. Sensor-specific simulation, specializing in difficult-to-work-with sensor modalities 

Not just “an image,” — the accurate view of what your sensor would see. While this may seem trivial for RGB cameras, it requires rich domain expertise and proven data generation technology to render physically accurate synthetic images for complex sensor types, such as radar (e.g., SAR), infrared/thermal, remote sensing (e.g., multispectral and hyperspectral), and x-ray.

3. A data generation platform with engineering-first workflows 

Synthetic data generation + model training + validation on one platform. The full synthetic data generation factory — instead of just a toolkit or engine you still need to build out. Rendered.ai’s PaaS provides open framework to plug in preferred tools, best-in-class simulator integrations (e.g., DIRSIG™, NVIDIA Omniverse, QSIM RT x-ray simulator), easily configured automated generation workflows, and direct access to customization tools, asset management, model training, and validation  designed for streamlined computer vision engineering collaboration. 

Minutes to days—not months. 

CV engineering teams use Rendered.ai to: 

1. Spin up fully labeled, training-ready datasets at lightning speed
2. Iterate training imagery with trackable history for future reuse 
3. Regenerate data quickly when new requirements arise 
4. Test models before hardware or sensors are deployed

The speed and quality that Rendered.ai provides is the competitive advantage you need to get your CV systems to market faster, minus extensive rounds of trial and error and the need to add expertise to your team. 

Exponentially so. 

Rapid synthetic data generation with Rendered.ai: 

• Overcomes the need for on-team domain expertise 

• Reduces reliance on hard-to-acquire real-world data collection 

• Cuts labeling costs and time 

• Shrinks the likelihood of late-stage model failures 

• Significantly shortens time-to-deployment 

Most teams don’t realize how much budget they’re burning on data until the end of the project and inevitably choose to stop doing it the hard way. 

100%! No computer vision model today performs perfectly across every use case. Without tailoring both the model and the data it’s trained on, specialized AI applications simply don’t work at scale.

Rendered.ai allows teams to customize: 

• Environments 

• Objects of interest 

• Sensor specifications 

• Distractors 

• Viewing geometry 

• Weather, lighting, occlusion 

• Edge cases and rare conditions 

You don’t adapt your model to the dataset—the dataset adapts to your model. 

Yes. Sometimes simulated data is the only option you have.

Because data is generated—not collected from the real world—it avoids: 

1. Non-compliance with privacy regulations (e.g., patients, children, consumers, etc.)
2. 2. Restrictions tied to moving real imagery between systems 
3. Risky or dangerous real-world capture (e.g., imagery from conflict zones, battlefronts, and other unsafe environments)

When access to sensitive real-world test scenarios is challenging, synthetic data is often the only option in early-stage development of AI applications in fields like healthcare, defense, workplace safety, consumer retail, transportation, and more. 

It doesn't have to be. 

In fact, smaller teams benefit even more from using tools like Rendered.ai’s Synthetic Data Platform or Synthetic Data as a Service training data creation because they: 

• Have fewer resources for data collection 

• Need faster iteration 

• Can’t afford long model retraining cycles

Rendered.ai’s solutions are designed to be accessible and easy-to-use for small engineering teams with the ability to quickly scale for enterprise deployments without changing their tech stack. 

Rendered.ai offers both subscription-based pricing for its Synthetic Data Platform as a Service and custom, project-based pricing for fully managed services like Synthetic Data as a Service, Model Development, and Auto-Data Labeling. This flexible structure allows teams to choose between hands-on platform use or expert-led delivery with minimal internal lift. 

Please refer to our Pricing page or contact us to get a quote based on your computer vision needs.