Synetic provides on‑demand, physics‑based procedural rendering to generate unlimited, photorealistic images with pixel‑perfect annotations for computer‑vision training. Users can control lighting, weather, camera angles, and actors to produce diverse data sets that include bounding boxes, masks, keypoints, depth, and segmentation maps, eliminating the need for manual labeling and reducing the sim‑to‑real gap.
Funding
Funding not disclosed
Founders
Product
Problem
Training computer‑vision models requires large amounts of labeled image data, but manual annotation is costly, time‑consuming, and often fails to cover rare edge cases, leading to a persistent sim‑to‑real performance gap.
Solution
Synetic offers a procedural rendering platform that generates unlimited, physically accurate synthetic images on demand. Users can programmatically control lighting, weather, camera parameters, object placement, and occlusions to produce photorealistic RGB, depth, and segmentation data. Each rendered frame includes pixel‑perfect ground truth annotations such as bounding boxes, masks, keypoints, and full camera metadata, eliminating the need for manual labeling. The physics‑based rendering minimizes domain shift, allowing models trained on synthetic data to perform directly on real‑world inputs. Synetic’s data can be used to train models for edge or cloud deployment, accelerating development cycles from weeks to hours.
Target Audience
Primary customers are computer‑vision teams in robotics, autonomous vehicles, surveillance, and agricultural technology that need high‑quality training data for edge or cloud‑deployed models.
Features
- Parametric scene controls for lighting, weather, camera angles, actor placement, and occlusion
- Unlimited variation generation to cover rare and edge‑case scenarios
- Pixel‑perfect annotations including bounding boxes, masks, keypoints, depth maps, and segmentation
- Physically accurate rendering with real‑world material properties and lighting for minimal domain gap
- Export of complete camera metadata and support for any model format (edge or cloud inference)
- On‑demand data generation via API, enabling rapid iteration and scaling of training pipelines