Model Releases
Metric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AI
arXiv:2607.12874v1 Announce Type: new Abstract: Deep learning computer vision for scientific applications requires collecting and annotating large datasets in a laborious, expensive and error-prone pr
arXiv:2607.12874v1 Announce Type: new Abstract: Deep learning computer vision for scientific applications requires collecting and annotating large datasets in a laborious, expensive and error-prone process. Synthetic data generation through 3D modelling and rendering may simplify this process and increase the accuracy of annotations by generating them programmatically. However, minimising the domain gap between real and synthetic images visually is subjective and lacks systematic quantitative guidance. We present GraNatPy, a Python package with metrics to guide improvement of the rendered scene. We show that quantifiable increase in realism, diversity and size of rendered dataset correlates with improved visual perception of the scene and higher zero-shot performance of an object detection model. Furthermore, we demonstrated using photographs of virological plaque assays that gradient similarity affects performance on small object detection, which can be improved by mixing real and synthetic data. Finally, we turn procedural data rendering into an agentic skill (SynthClaw) to automate the procedural parameter optimisation.
Related
- SADGE: Structure and Appearance Domain Gap Estimation of Synthetic and Real Data
- Grounding Synthetic Data Generation With Vision and Language Models
- The Power of Light: Improving Synthetic-to-Real Domain Adaptation through Physically-Based Indirect Illumination
- What Makes Synthetic Data Effective in Image Segmentation
Source: arXiv cs.CV | 2026-07-15