Improved diffuse reflection models for computer vision

There are many computational vision techniques that fundamentally rely upon assumptions about the nature of diffuse reflection from object surfaces co nsisting of commonly occurring nonmetallic materials. Probably the most pre valent assumption made about diffuse reflection by computer vision research ers is that its reflected radiance distribution is described by the Lambert ian model, whether the surface is rough or smooth. While computationally an d mathematically a relatively simple model, in physical reality the Lambert ian model is deficient in accurately describing the reflected radiance dist ribution for both rough and smooth nonmetallic surfaces. Recently, in compu ter vision diffuse reflectance models have been proposed separately for rou gh, and, smooth nonconducting dielectric surfaces each of these models accu rately predicting salient non-Lambertian phenomena that have important bear ing on computer vision methods relying upon assumptions about diffuse refle ction. Together these reflectance models are complementary in their respect ive applicability to rough and smooth surfaces. A unified treatment is pres ented here detailing important deviations from Lambertian behavior for both rough and smooth surfaces. Some speculation is given as to how these separ ate diffuse reflectance models may be combined.