Figure 1: Our new importance sampling strategy allows easy inclusion of Marschner and related hair reflectance functions in physicallybased Monte Carlo renderers. Here we show hair volumes illuminated by environment maps and area lights with unbiased global illumination (computed using a forward path-tracer with multiple importance sampling). Our sampling strategy requires no precomputation, so it is easy to vary the absorption along the fiber (second image), and to add noise to the index of refraction, roughness, and scale tilt to create subtle heterogeneity along each fiber. Each image is 1024 samples/pixel.
AbstractWe present a new strategy for importance sampling hair reflectance models. To combine hair reflectance models with increasingly popular physically-based rendering algorithms, an efficient sampling scheme is required to select scattered rays that lead to lower variance and noise. Our new strategy, which is tied closely to the derivation of physically-based fiber functions, works well for both smooth and rough fibers based on the Marschner et al. model and also for Lambertian fibers. It should be directly usable with future hair reflectance models that allow for more general cross-sections and more complex surface properties, provided the lobes are derived in a similar, separable fashion. Our strategy includes lobe selection and can efficiently sample complex lobe shapes like the Marschner TRT function. The scheme is easy to implement and requires no precomputation, allowing fully heterogeneous variation of all fiber parameters.