Bidirectional importance sampling for direct illumination

Image-based representations for illumination can capture complex real-world lighting that is difficult to represent in other forms. Current importance sampling strategies for image-based illumination have difficulties in cases where both the illumination and the surface BRDF contain important high-frequency detail – for example, when a specular surface is illuminated by an environment map containing small light sources. We introduce the notion of bidirectional importance sampling, in which samples are drawn from the product distribution of both the surface reflectance and the light source energy. While this approach makes the sample selection process more expensive, we drastically reduce the number of visibility tests required to obtain good image quality. As a consequence, we achieve significant quality improvements over previous sampling strategies for the same compute time.