## Comparison of two Methods of Global Illumination Analysis

Andrei Khodulev

 Contents  Introduction  Overview of Deterministic algorithm  Overview of Monte Carlo algorithm  Scenes used for comparison  Accuracy analysis  Results obtained  Conclusion  Acknowledgments

# 4. SCENES USED FOR COMPARISON

We used scenes of two kinds: simple and complex. There is only one simple scene for which we can find accurate theoretical values of illuminance distribution. All other scenes are realistic ones that are presented by several companies (listed below). The scenes represent different kinds of interior.

## 4.1 Simple scene: empty cube

The first scene used for the comparison is simply an interior of a cube (10x10x10 m) with one point light source at its center (Figure 2). Luminous intensity of the light source is equal to 50000 cd that create illumination level 2000 lux at the nearest point on the wall. The wall material is white with the diffuse reflectivity 2/3 so that an indirect component constitutes a greater part in the full illumination.

Figure 2. Empty cube.

Because of symmetry all cube faces are equivalent. We've chosen several points on a cube face where the theoretical results are compared with the results produced by the two algorithms being analyzed. Although the scene is very simple, the exact analytical solution for illuminance distribution in it seems to be impossible. We used a combination of the Monte Carlo simulation and the direct numerical integration of the rendering equation to get accurate estimates of illuminance in the points selected.

## 4.2 Complex scenes: interiors

A series of interior scenes prepared mainly by Japanese companies is used. They are listed below. Scene size is characterized by the total number of triangles used. When a triangle subdivision was applied (to increase a simulation accuracy by reducing the discretization error) the number of triangles after subdivision is presented.

List of scenes:

 Scene name Created by Triangles Description APART2 Integra 5,000 An apartment DREAM0 Korea National Housing 14,000 A living room FLOWER Integra 17,000 A flower in a room CG Matsushita 32,000 A bathroom HONSHA CTC 68,000 An office

Simulations run on Convex SPP 1000 supercomputer (4x128M).

 Contents  Introduction  Overview of Deterministic algorithm  Overview of Monte Carlo algorithm  Scenes used for comparison  Accuracy analysis  Results obtained  Conclusion  Acknowledgments