Global illumination (GI) plays a crucial role in rendering realistic results for virtual exhibitions, such as virtual car exhibitions. These scenarios usually include all-frequency bidirectional reflectance distribution functions (BRDFs), although their geometries and light configurations may be static. Rendering all-frequency BRDFs in real time remains challenging due to the complex light transport. Existing approaches, including precomputed radiance transfer, light probes, and the most recent path-tracing-based approaches (ReSTIR PT), cannot satisfy both quality and performance requirements simultaneously. Herein, we propose a practical hybrid global illumination approach that combines ray tracing and cached GI by caching the incoming radiance with wavelets. Our approach can produce results close to those of offline renderers at the cost of only approximately 17 ms at runtime and is robust over all-frequency BRDFs. Our approach is designed for applications involving static lighting and geometries, such as virtual exhibitions.

The glinty details from complex microstructures significantly enhance rendering realism. However, the previous methods use high-resolution normal maps to define each micro-geometry, which requires huge memory overhead. This paper observes that many self-similarity materials have independent structural characteristics, which we define as tiny example microstructures. We propose a procedural model to represent microstructures implicitly by performing spatial transformations and spatial distribution on tiny examples. Furthermore, we precompute normal distribution functions (NDFs) by 4D Gaussians for tiny examples and store them in multi-scale NDF maps. Combined with a tiny example based NDF evaluation method, complex glinty surfaces can be rendered simply by texture sampling. The experimental results show that our tiny example based the microstructure rendering method is GPU-friendly, successfully reproducing high-frequency reflection features of different microstructures in real time with low memory and computational overhead.