Metric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AI
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
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 62%Into the Omniverse: Three Workflows for Improving Vision AI Agent Accuracy With Synthetic Data and Fine-Tuning →
- PossiblePossibly related (embedding) · 57%DeepTrackAI/DeepTrack2 →
- PossiblePossibly related (embedding) · 51%From virtual experiments to biomedical insight with synthetic data →
- PossiblePossibly related (embedding) · 50%ghbalf/freecad-ai →
- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “Metric-Guided Synthetic Image Data Rendering for Deep Learni” ≈ “Tongyi-MAI/Z-Image-Turbo””
- PossiblePossibly related (embedding) · 26%voxel51/fiftyone →
“Possibly related via embedding similarity 0.57 (not asserted). Timestamp check: artifact slightly before paper (-12d).”
- LinkedLinked via arxiv author · 85%Martina Radoynova →
“Metric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AI”
- LinkedLinked via arxiv author · 85%Samuel Pantze →
“Metric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AI”