REAL-TIME WALKTHROUGH RENDERING

Speed Improvement of Photon Mapping Algorithm

Florian Graglia, Jean Sequeira and S´ebastien Mavromatis

Aix Marseille Universit´e, LSIS, 13009, Marseille, France

Keywords:

Real Time, Global Illumination, Rendering, Walkthrough, Photon Mapping, 3D Modeling.

Abstract:

The main purpose of this paper is to discuss the global illumination methods in the context of real-time walk-

through. Our work focuses on an accurate illumination of complex scenes; the intensity of the lights can also

be interactively modiﬁed. This feature is particularly relevant within the context of a production pipeline: Af-

ter the 3D modeling, photorealistic walkthrough is often used to detect inappropriate reﬂections or to measure

illumination rates. The known methods that usually work in real-time do not simulate all light paths for large

scenes. However, some methods provide a full global illumination with a computation time close to a few

seconds. This paper shows how this calculation time can be reduced to approach real-time animation thanks

to a speciﬁc method derived from the photon mapping.

1 INTRODUCTION

The goal of global illumination is to solve the ren-

dering equation (Kajiya, 1986) or to approach its so-

lution. This equation gives a complete description

of light transport. Global illumination methods aim

at resolving this rendering equation to generate pho-

torealistic images from the scene description. Dif-

fuse and glossy interreﬂections are important parts

of this illumination. However, these interreﬂections

are often limited to pure diffuse surfaces (radiosity

(Goral et al., 1984)) or approximated by non-realistic

methods (ambient occlusion (Zhukov et al., 1998)

(Ritschel et al., 2009), point-based color bleeding

(Bunnell, 2005)...). In this paper, we study consis-

tent rendering methods that simulate realistic environ-

ments according to a production pipeline. In this con-

text, the end-users must be able to detect inappropri-

ate reﬂections, measure illumination rates or compare

several lighting ambiances.

Most of the global illumination methods based on

ray tracing, like stochastic ray tracing (Cook et al.,

1984) (Lai et al., 2007), bidirectional path tracing

(Veach and Guibas, 1994) or metropolis light trans-

port match the quality requirements. However, de-

spite recent improvements (Pajot et al., 2011), the ex-

ecution time is too long to provide global illumination

in real-time (Granier et al., 2000). The radiosity algo-

rithm extended to glossy interreﬂections (Christensen

et al., 1996) strongly depends on the viewpoint posi-

tion. In walkthrough, this viewpoint changes continu-

ously. We must compute all the rendering process for

each frame. This is a serious issue to provide results

in real-time.

H.W. Jensen introduced the photon mapping

method in 1996 (Jensen, 1996). This high-quality

rendering technique computes diffuse and glossy in-

terreﬂections in two steps. First, many rays are sent

from the light sources towards the scene. After the

ﬁrst bounces, and for each diffuse or glossy surface,

we store the properties of the photons in a KD tree

structure (called photon map) (Jensen, 2001) (Wald

et al., 2007). We use this structure in the second step:

a modiﬁed ray tracing from the camera is processed to

render the scene. The algorithm searches for the near-

est neighbor photons of the point of the intersected

surface for each primary ray. These nearest neighbor

photons contribute to the illumination of the surface

point.

This paper is also based on the works of B.D.

Larsen and N.J. Christensen (Larsen and Christensen,

2004). With this approach, the geometry is subdi-

vided into several surfaces. A surface contains an ar-

bitrary number of polygons that share common edges

or corners and similar orientations. The photons sent

in the ﬁrst step of photon mapping are stored in a lo-

cal map belonging to the intersected surface. In the

rendering step, the nearest photons are searched for

in these smaller maps.

291

Graglia F., Sequeira J. and Mavromatis S..

REAL-TIME WALKTHROUGH RENDERING - Speed Improvement of Photon Mapping Algorithm.

DOI: 10.5220/0003817702910294

In Proceedings of the International Conference on Computer Graphics Theory and Applications (GRAPP-2012), pages 291-294

ISBN: 978-989-8565-02-0

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

2 PHOTON MAPPING

IMPROVEMENTS FOR

WALKTHROUGH

We propose a new geometry-based approach of this

method, to decrease the execution time of the original

method of photon mapping. The goal is to provide a

high-quality result for a real-time walkthrough.

2.1 Triangle-based Photon Mapping

The approach introduced in this paper signiﬁcantly

decreases the number of photons in the photon map.

The main idea is to gather the photons by triangle.

For each triangle, a distinctive map is used to store

the photons that hit the triangle. During the ﬁrst step

of photon map construction, the photons are stored in

the map linked to the intersected triangle. With this

method, the knn algorithm only searches for the near-

est photon in a small map during the rendering step.

We choose a triangle as a building block for our al-

gorithm because it is the smallest entity in geometry.

For the polygons with 4 or more sides, a subdivision

into separate triangles is processed.

Figure 1: Na¨ıve rendering with the triangle-based photon

mapping method.

However, an additional issue stems from this ap-

proach. Figure 1 shows illumination with photon

mapping when using the subdivision of photon maps.

We see dark edge effects that are related to an incom-

plete integration of the photons close to the edges. At

these points, the knn algorithm searches for photons

in truncated photon maps, resulting in a diminution

of the illumination (see illustration in ﬁgure 2). To

avoid this issue, during the ﬁrst step of the triangle-

based photon mapping, we integrate the photons into

photon maps related to the triangles located nearby.

Two conditions are required to integrate a photon into

a non-intersected triangle: the photon shall be sepa-

rated by a distance not greater than the search radius

and shall have the same space orientation as the trian-

gle. This last requirement prevents the illuminations

of the triangle and of the photon from being too dif-

ferent, which could result in an undesirable bleeding

on the corners.

Figure 2: KNN algorithm only searches nearest neighbors

on the photon map (in green) of the intersected triangle.

A na¨ıve method is to test each photon with all tri-

angles and to add it to the appropriate photon maps.

Despite the fact that this algorithm is carried out in a

precomputation step, the processing time is too long

for a large number of photons and triangles. To over-

come this difﬁculty, a list of neighboring triangles that

have a similar orientation, is created for each triangle.

Figure 3: In yellow: a group of neighboring triangles with

a similar orientation for a given triangle (in red).

Figure 3 shows the group of neighboring triangles

for the red triangle, in yellow. The blue boundary rep-

resents the triangles close to the red triangle, but too

different in orientation. In our implementation, the

method introduced by D.H. Eberly (Eberly, 2007) is

used to calculate the distance between two triangles.

To compare the orientations between the triangles, we

set a parameter a that deﬁnes the maximum value of

the difference between the triangle normal vectors.

Empirically, a value around 60 degrees avoids dark

edge effects and undesirable bleeding on the corners.

When a photon hits a triangle, we add it to the tri-

GRAPP 2012 - International Conference on Computer Graphics Theory and Applications

292

angles listed in the group. During this step, the pho-

tons are not duplicated; only references are stored in

the structure of the maps. With this approach, we de-

crease the number of photons in the maps from sev-

eral million to a few dozen. The quality of the result

remains unchanged (see resulting image on ﬁgure 4).

Figure 4: Resulting image with triangle-based photon map-

ping method.

2.2 Two Dimension Structure

In section 2.1, we have shown that the photon maps

are generated for each triangle, and not globally for

the scene. An interesting result of this subdivision

is that the maps contain uniform 2D point clouds in-

stead of a non uniform 3D point cloud with the global

photon map. In the triangle-based photon mapping,

almost all the photons are located on the triangle,

thus they can be represented in triangular coordinates.

However, some photons are not on the plane of the

triangle. In section 2.1, we add photons located on

triangles close to the intersected one. Their position

can not be represented in triangular coordinates for all

the photon maps. In order to use this representation,

we project the considered points on the plane of the

triangle (see ﬁgure 5). We use these new coordinates

to store the photons in the data structure. To avoid

wrong results when integrating the illumination, we

use the initial coordinates for the KNN algorithm.

Figure 5: Projection of photons from another triangle (in

red) on two dimensions.

A Photon mapping algorithm usually stores the

photons with a KD tree. Indeed, this structure is the

most effective in the case of a non uniform 3D point

cloud. The new approach of triangle-based photon

mapping, places us in a new case of uniform 2D point

cloud. Speciﬁc solutions must be provided for this

new distribution. We use here a 2D uniform grid as

data structure and store the photons in cells. During

the rendering step, the search begins from the closest

cells to the furthest ones until all the nearest photons

are found.

5000 50 100 150 200 250 300 350 400 450

0,5

1,5

2,5

Figure 6: Comparison of access time of the knn algorithm

between 3D KD Tree (red), 2D KD Tree (yellow) and 2D

uniform grid (green). X-axis represents the number of pho-

tons.

Figure 6 shows a benchmark for the knn algorithm

between a KD Tree (3D and 2D) and a uniform grid

(2D). With a uniform grid structure, the execution

time is divided by 2 compared to a 2D KD tree, and is

divided by a factor of between 3 and 5 compared to a

3D KD tree.

2.3 Speciﬁc Feature for Production

Pipeline

Our works focus on two problems. The ﬁrst is to de-

crease the execution time of the illumination method

and has been addressed in the previous two sections.

The other problem is related to a speciﬁc feature usu-

ally used in a production pipeline after the 3D mod-

eling: the interactive modiﬁcation of the power of the

light source. In this section, we propose a modiﬁca-

tion of the photon mapping pipeline to provide this

requirement.

H.W. Jensen (Jensen, 2001) considers that the

photon map contain all the information about the pho-

tons, including their energy. Our implementation dif-

fers from that: the starting energy of the sources is

normalized to a value of 1W. The precomputation

time is done with normalized powers for each light

source. These power are included only when the illu-

mination is computed during the rendering step. By

this means, it is possible to interactively modify the

lighting conﬁguration.

2.4 Results

The wide variety of methods and optimizations for

the photon mapping makes it difﬁcult to carry out an

REAL-TIME WALKTHROUGH RENDERING - Speed Improvement of Photon Mapping Algorithm

293

accurate benchmark. Thus, we compare the execu-

tion time obtained by a basic implementation of the

photon mapping with and without our improvements

in some preliminary tests. Both implementations use

GPU and the NVIDIA CUDA framework. Table 1

shows the execution time of the two main steps of the

photon mapping: the KNN algorithm and the integra-

tion of the illumination.

Table 1: Execution time in milliseconds for a scene with

100.000 triangles and 4.000.000 photons. Resolution 500 x

500.

Method KNN Integration Total

Photon mapping 150 140 230

Triangle-based pho-

ton mapping

14 140 154

As shown in ﬁgure 1, our method signiﬁcantly de-

creases the execution time of the KNN algorithm. For

all the process, the improvement is estimated to be

around 40%. There are several reasons for this. First,

the photon maps are smaller with our method: an av-

erage of just 40 photons, with a maximum of 300 for

one map. Another reason is the use of a 2D uniform

grid instead of a 3D KD tree. With our method, the

execution time is not directly dependent on the num-

ber of photons, but on the ratio of the photons to the

number of triangles in the scene.

3 CONCLUSIONS

We have shown in this paper that a subdivision of the

photon map decreases signiﬁcantly the execution time

of the rendering step. This approach modiﬁes the pho-

ton map structure, changing the mode into two dimen-

sions and decreasing the number of photons.

This new approach provides a result close to real

time rendering for walkthrough. This method ren-

ders images with good accuracy simulating a large

part of the light paths, including the last reﬂection

on a glossy surface. The end users of this simulation

can also interactively modify the power of the light

sources. These features allow lighting simulation for

production pipelines. We are currently implementing

the method of the triangle-based photon mapping in a

real industrial setting.

ACKNOWLEDGEMENTS

This work was carried out for the VIRTUART project

which is funded by the DGSIS and the PACA region

and has been labeled by the PEGASE cluster.

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