Abstract Recent work in interactive global illumination addresses diffuse and moderately glossy indirect lighting effects, but high‐frequency effects such as multi‐bounce reflections on highly glossy surfaces are often ignored. Accurately simulating such effects is important to convey the realistic appearance of materials such as chrome and shiny metal. In this paper, we present an efficient method for visualizing multi‐bounce glossy reflections at interactive rates under environment lighting. Our main contribution is a pre‐computation–based method which efficiently gathers subsequent highly glossy reflection passes modelled with a non‐linear transfer function representation based on the von Mises–Fisher distribution. We show that our gathering method is superior to scattered sampling. To exploit the sparsity of the pre‐computed data, we apply perfect spatial hashing. As a result, we are able to visualize multi‐bounce glossy reflections at interactive rates at a low pre‐computation cost.

Abstract This paper proposes a method for efficiently rendering indirect highlights. Indirect highlights are caused by the primary light source reflecting off two or more glossy surfaces. Accurately simulating such highlights is important to convey the realistic appearance of materials such as chrome and shiny metal. Our method models the glossy BRDF at a surface point as a directional distribution, using a spherical von Mises‐Fisher (vMF) distribution. As our main contribution, we merge multiple vMFs into a combined multimodal distribution. This effectively creates a filtered radiance response function, allowing us to efficiently estimate indirect highlights. We demonstrate our method in a near‐interactive application for rendering scenes with highly glossy objects. Our results produce realistic reflections under both local and environment lighting.

Visual observation is our principal source of information in determining the nature of objects, including shape, material or roughness. The physiological and cognitive processes that resolve visual input into an estimate of the material of an object are influenced by the illumination and the shape of the object. This affects our ability to select materials by observing them on a point-lit sphere, as is common in current 3D modeling applications. In this paper we present an exploratory psychophysical experiment to study various influences on material discrimination in a realistic setting. The resulting data set is analyzed using a wide range of statistical techniques. Analysis of variance is used to estimate the magnitude of the influence of geometry, and fitted psychometric functions produce significantly diverse material discrimination thresholds across different shapes and materials. Suggested improvements to traditional material pickers include direct visualization on the target object, environment illumination, and the use of discrimination thresholds as a step size for parameter adjustments.