High-Fidelity Cross-Scale Neural Rendering of Real-World Large-Scale Scenes with Hash Featurized Manifold

We propose a novel hash featurized manifold representation that fully unleashes the expressivity of volumetric hash encoding by rasterizing the surface manifold to explicitly prioritize multi-view consistency, enabling high-fidelity cross-scale neural rendering of real-world large-scale scenes.