🤖 AI Summary
To address the challenge of efficiently and realistically rendering high-frequency microstructure glints on specular surfaces under strong illumination using high-resolution normal maps, this paper proposes a position-normal manifold-based glint NDF modeling approach. We introduce the first analytically tractable manifold-parameterized glint NDF, enabling NDF integration to be reformulated as mesh intersection computation. To accelerate evaluation for large footprints, we incorporate hierarchical mesh clustering. Furthermore, we derive, for the first time, an analytical shadowing-masking model for normal-mapped diffuse surfaces. Our method achieves glint visual fidelity comparable to state-of-the-art baselines while accelerating computation by an order of magnitude, enabling real-time rendering with high-resolution normal maps. Crucially, it unifies glint rendering and diffuse shadowing-masking within a single coherent framework.
📝 Abstract
Detailed microstructures on specular objects often exhibit intriguing glinty patterns under high-frequency lighting, which is challenging to render using a conventional normal-mapped BRDF. In this paper, we present a manifold-based formulation of the glint normal distribution functions (NDF) that precisely captures the surface normal distributions over queried footprints. The manifold-based formulation transfers the integration for the glint NDF construction to a problem of mesh intersections. Compared to previous works that rely on complex numerical approximations, our integral solution is exact and much simpler to compute, which also allows an easy adaptation of a mesh clustering hierarchy to accelerate the NDF evaluation of large footprints. Our performance and quality analysis shows that our NDF formulation achieves similar glinty appearance compared to the baselines but is an order of magnitude faster. Within this framework, we further present a novel derivation of analytical shadow-masking for normal-mapped diffuse surfaces -- a component that is often ignored in previous works.