AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A diffusion-based shadow caching framework that compresses a vast set of pre-calculated shadow INRs into a single diffusion model that effectively memorizes and reconstructs a dense set of pre-trained lighting conditions on the fly, bypassing the massive storage bloat of independent INRs.
This work encodes scalar fields as anisotropic Gaussian primitives under a fixed budget that serves every subsequent visualization state - supporting post-hoc transfer-function, colormap, lighting, and viewpoint changes without re-encoding.
Across structured, unstructured, and particle data, the sample-based formulation improves reconstruction accuracy with measurably fewer primitives in comparison to prior formulations, achieving up to 4.8 dB higher PSNR with an approximate 44x reduction in primitive count.
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