AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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RDGSplat is proposed, which decodes a geometry dedicated to rendering from a frozen 3D foundation model while keeping its metric predictions exact, and improves novel view synthesis for three feed-forward models of two architectures on four benchmarks, with every pretrained weight frozen.
The key insight is that DA3's cross-view features, though trained only for depth consistency, implicitly encode motion-discriminative signals when combined with best-match feature search across frames, which could enable much denser temporal sampling that prior methods cannot support.
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