OSOG: A Differentiable, Physics-Informed Synthetic Data Engine for Micro-Optical Environments
Deep learning in computational microscopy is severely constrained by the scarcity of densely annotated datasets. While synthetic data generation has bridged this gap in macroscopic computer vision, traditional graphics engines rely on geometric ray-tracing, failing to capture the micro-optical phenomena required for microscopy. Conversely, while wave-optics formulations exist, rendering them computationally tractable at the scale required for deep learning remains a massive systems challenge. To address this, we introduce the Optical Synthetic Object Generator (OSOG), a high-performance, fully differentiable forward-modeling engine. Drawing on established physical models of diffraction an...
| Authors | Caio Silva |
|---|---|
| Primary category | cs.CV |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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