Synthetic Data

Vision and perception models need a lot of labeled images to learn well. Collecting and labeling that data from the real world is slow and expensive. With a working simulation we can produce labeled images, depth maps and bounding boxes by the thousands, with very little extra effort.

What We Do

We use the simulation we have already set up for your project to produce training data. The same scene that runs your robot can also render images for a perception model. Every object has a known position and class, so labels come out automatically.

We can produce data for:

• Object detection models like YOLO
• Segmentation models
• Pose and grasp estimation models
• Depth and stereo models
• Custom models your team is training

How It Works

• We pick the camera placements that match your real sensor setup
• We vary lighting, color, texture and clutter to cover the cases your model will see
• We render large batches of frames in the background
• Labels are written next to every frame, in the format your training pipeline expects

For Isaac Sim users we can use NVIDIA Replicator to drive the runs. For Gazebo users we use a similar pipeline built around the world we already have.

When It Helps

Synthetic data is useful when:

• You do not have enough real images yet
• The objects are rare or hard to collect
• You need a balanced dataset across many cases
• You want to add edge cases that almost never show up in the wild

It is not a magic fix. A model trained only on simulation can struggle on real images. We usually mix synthetic and real data so the model learns both.

What You Get

• A script that produces fresh data on demand
• Sample datasets in the format your training code uses
• Notes on what settings we varied and why
• Help wiring the dataset into your training run

If you want to try synthetic data without buying into a full pipeline, we can start with a small batch and let your team test it against a real validation set.