MODELING DENDRITIC SHAPES

Using Path Planning

Ling Xu and David Mould

Department of Computer Science, University of Saskatchewan, Saskatoon, Canada

Keywords:

Dendrites, procedural modeling, natural phenomena, path planning.

Abstract:

We present a method for creating geometric models of dendritic forms. Dendritic shapes are commonplace in

the natural world; some examples of objects exhibiting dendritic shape include lichens, coral, trees, lightning,

rivers, crystals, and venation patterns. Our method ﬁrst generates a regular lattice with randomly weighted

edges, then ﬁnds least-cost paths through the lattice. Multiple paths from a single starting location (or gen-

erator) are connected into a single dendritic shape. Alternatively, path costs can be used to segment volumes

into irregular shapes. The pathﬁnding process is inexpensive, and admits control handles including endpoint

placement, distribution of generators, and arrangement of nodes in the graph.

1 INTRODUCTION

Dendritic forms are common in nature, and the phe-

nomena manifesting dendritic shapes are exceedingly

varied, from the humble to the spectacular: lichens,

coral, river systems, and lightning are all examples

of naturally-occurring dendritic shapes. The key ele-

ments of dendritic forms are the branching structures

and the erratic winding travels of individual branches;

both these characteristics can be obtained with least-

cost paths in randomly weighted graphs, branching

because paths to different destinations will share the

early part of their route, and winding because the op-

timal path will have to travel around random expen-

sive obstacles. In this paper, we propose explicit path

planning as a modeling primitive for dendritic shapes.

Path planning is the problem of ﬁnding the least-

cost path between two nodes in a weighted graph.

Algorithms for ﬁnding the least-cost path (Winston,

1992) are well known, since the problem appears so

often in different contexts in computer science. Mod-

eling dendrites using path planning is straightforward:

by ﬁnding multiple paths from the same starting point

to different endpoints, we create a dendrite. By using

an entire dendrite as the destination for a new set of

paths, we can create explicitly fractal dendrites.

One of the most popular algorithmic methods

for creating dendrites is diffusion-limited aggregation

(DLA), ﬁrst proposed in the physics literature by Wit-

ten and Sander (Witten and Sander, 1981). DLA

has been exploited for dendrite creation in computer

graphics. However, methods for simulating DLA are

slow. Employing an iterative path planning tech-

nique, we are able to produce dendrites compara-

ble in appearance to dendritic shapes produced by a

costly DLA simulation, but orders of magnitude more

quickly. Some 2D dendrites created by our method

appear in Figure 1.

Our basic method operates over a ﬁxed lattice, and

lattice artifacts can sometimes be seen in the resulting

models, just as in lattice DLA. In this paper, we also

present a formulation for reﬁning the lattice and com-

puting a higher-resolution version of the dendrites.

We demonstrate the utility of our framework by

applying it to synthesizing coral. In particular, we

model staghorn coral, one of the most obviously den-

dritic types of coral. We believe that our method is

suitable for other types of sessile marine life, as well

as for other natural objects that are well represented

by dendrites, including lichens, trees, and lightning.

The paper is organized as follows. Following the

introduction, we review some previously proposed

methods for generating dendritic shapes, concentrat-

ing on DLA and L-systems. We give details of our

path-planning algorithm in section 3. Results, in the

form of images of dendrites and dendrite-based geom-

etry, are shown in section 4; this section also contains

timing ﬁgures. Finally, section 5 concludes our paper

Xu L. and Mould D. (2007).

MODELING DENDRITIC SHAPES - Using Path Planning.

In Proceedings of the Second Inter national Conference on Computer Graphics Theory and Applications - GM/R, pages 29-36

DOI: 10.5220/0002076400290036

 SciTePress

Figure 1: Above: simple dendrites, with few or many paths;

below: fractal dendrites, where new paths are repeatedly

placed in the vicinity of previously chosen paths.

with a summary of the contributions and pointers to

possible future work.

2 PREVIOUS WORK

Algorithms for procedural geometry have been de-

vised by computer graphics practitioners. Two algo-

rithms in particular, L-systems and diffusion-limited

aggregation, have seen considerable attention because

of their versatility and the quality of their results.

L-systems (Lindenmayer and Prusinkiewicz, 1990)

uses a replacement grammar to create strings which

can be interpreted as a variety of botanical forms,

particularly (though not exclusively) branching struc-

tures. Diffusion-limited aggregation (DLA) is an

algorithmic process capable of generating dendritic

forms akin to those seen in a number of natural ob-

jects, including lichens, crystals, neurons, and light-

ning (Bunde and Havlin, 1996). We are particu-

larly interested in DLA, owing both to the rich set of

phenomena which can be described by this method,

and to the irregularities in the branching structures;

we aspire to create dendrites with the same natu-

ral appearance. Other models for dendritic growth,

including viscous ﬁngering (Ball, 2004), the Eden

model (Bunde and Havlin, 1996), and ad-hoc greedy

models (Gastner and Newman, 2006), have appeared

in the physics and biology literature.

Diffusion-limited aggregation has recently been

used in graphics to model lichens (Desbenoit et al.,

2004) and ice crystals (Kim and Lin, 2003; Kim et al.,

2004). Lightning (Kim and Lin, 2004) has been mod-

eled using the related dielectric breakdown model,

which describes another form of Laplacian growth.

These results are of high visual quality, although the

modeling process is time-consuming.

The brute-force algorithm for diffusion-limited

aggregation (Witten and Sander, 1981) is as follows.

Some initial sites in a lattice are set to “occupied”; the

remainder of the lattice nodes are unoccupied. A par-

ticle is released, at a great distance from any occupied

site, and undertakes a random walk until it reaches

some location adjacent to an occupied site. At that

point, the node where the particle is located becomes

occupied, and a new walker is released. The above

process is repeated, hundreds, thousands, or even mil-

lions of times. When a sufﬁcient number of particles

have been placed, the resulting aggregation has a frac-

tal dendritic shape; the dendrites arise owing to the

greater likelihood of a particle encountering the tip of

a branch than a point along a branch.

L-systems is a parallel rewriting grammar that

takes an initial string (“axiom”) and repeatedly per-

forms applicable transformations on it. The ﬁnal

string is an encoding of some object, often fractal; the

string is interpreted into geometry by mapping each

symbol in the string to some geometric primitive or

action. (A popular mapping is to have the symbols

represent “turtle movement”: move forward, move

backward, turn left, turn right).

Basic L-systems do not consider information

about the surroundings, since the interpretation into

geometry happens at the end of the process. While ba-

sic DLA alters the contents of the grid, other environ-

ment variables are not used in DLA simulation either.

For modeling interactions with the environment, open

L-systems (Mech and Prusinkiewicz, 1996) were de-

vised. While the previous environmentally-sensitive

L-systems (Prusinkiewicz et al., 1994) introduced

query symbols allowing information about the envi-

ronment to inﬂuence development, open L-systems

have a symbol in the grammar for two-way commu-

nication between the L-system and the environment.

The similar notion of open DLA has also been em-

ployed for lichen simulation (Desbenoit et al., 2004).

The main drawback to L-systems lies in the difﬁculty

of devising the system of replacement rules; the con-

nection between the rules and the resulting shapes can

be profoundly obscure.

3 ALGORITHM

Our algorithm involves ﬁnding a collection of paths

through a weighted graph. The graph is a regular lat-

GRAPP 2007 - International Conference on Computer Graphics Theory and Applications

tice ﬁlling the 2D or 3D space where the modeled ob-

ject is to exist; weights on the edges are chosen at

random. To create a dendritic form, we connect to-

gether multiple paths which share one endpoint, the

root of the dendrite. Our implementation performs a

breadth-ﬁrst computation of path costs from the root

to all nodes in the lattice. The dendritic shape can ei-

ther be converted to geometry, directly (as lines) or by

taking an isosurface from a scalar ﬁeld; or, the shape

can be visualized without the intermediate geometry,

in the case of 2D dendrites.

The method for creating dendrites operates as fol-

lows. A regular lattice is created, and the edges

of the lattice given weights from some distribu-

tion. We use four-connected lattices (six-connected in

3D) to conserve memory, but eight-connectivity (26-

connectivity) could be used to reduce lattice artifacts;

in this case non-orthogonal edges’ weights would

need to be scaled appropriately. We have found that a

uniform distribution of weights, say W = 1 + rhRi,

works well. In the preceding, we denote by W a

weight, and let hRi be a value chosen randomly from

the interval (0, 1); r is a parameter determining the

amount of ﬂuctuation permitted in the weights. We

found a value of r around 10 to work well. Note that

with small r the resulting paths are close to Manhattan

paths (since the constant term dominates), while with

larger r the paths are more erratic (since the random

component is relatively more important).

We often perform breadth-ﬁrst computation from

a more elaborate set of nodes than just a single root

node, and we need some terminology to refer to the

base nodes (those at distance zero); borrowing from

the implicit surfaces terminology, we call this set of

nodes the generators. The next stage in our algorithm

is to choose the generators for the dendrite. If the

generators are single disjoint nodes, each one will be-

come the root of a separate dendritic shape, but we

commonly choose connected sets of points, as de-

scribed shortly. Using breadth-ﬁrst search, we pop-

ulate all nodes in the lattice with the costs of their

least-cost paths from the generators.

Next, we select a set of endpoints in the lattice.

The endpoints can be chosen randomly, determined

procedurally, or placed manually. In the examples

shown in this paper we placed endpoints almost ran-

domly; we used rejection sampling to prevent two

endpoints from appearing too near to one another.

With the endpoints chosen and the graph popu-

lated with distance values, we use a greedy algorithm

to ﬁnd the least-cost path from each endpoint to the

nearest generator. The union of the paths thus ob-

tained is the dendrite. The overall construction pro-

cess is shown in Figure 2.

Figure 2: Plain dendrite construction process. Left: iso-

contours of distance values from central generator. Middle:

randomly placed endpoints. Right: dendrite arising from

planning paths from endpoints to central generator.

Figure 3: Left: a coarse path; the reﬁned lattice will be

generated around it. Right: a new path computed inside the

reﬁned lattice.

3.1 Path Reﬁnement

Our method as described so far produces shapes with

resolution limited by the ﬁxed resolution of the mesh.

However, there is a natural extension to an iterative

reﬁnement approach: once the skeleton of the den-

drite has been created, or the shell of the object in the

case of a mesh from a segmentation, a new higher-

resolution lattice can be constructed in the region of

interest. This reﬁnement process can be repeated if

desired. The basic idea is that a new sublattice is

built for each node in the dendrite; the sublattices are

hooked together to form a connected graph, where a

new pathﬁnding process can take place. Figure 3 il-

lustrates the process.

Pseudocode describing the reﬁnement process for

a single path is shown in Figure 4; the process is re-

peated for each path in a dendrite. One advantage

of doing the reﬁnement on a per-path basis is that

the high-resolution graphs are individually small, and

they are temporary, and hence memory usage is not

overly onerous. A side-by-side comparison between

a coarse dendrite and a reﬁned dendrite is shown in

Figure 5. The reﬁnement can also be applied to 3D

lattices to create a high-resolution 3D model.

MODELING DENDRITIC SHAPES - Using Path Planning

Input: a coarse path D consisting of m nodes.

Output: a reﬁned path D

′

1. For each node in D, say N

, create a regular lat-

tice L

of size n × n. Assign positions to nodes in

relative to N

2. For i = 0 to m−2, stitch the lattices together by

adding edges between nodes in L

and L

i+1

. Call

the resulting graph G.

3. Perform a path planning task within G and re-

turn the result.

Figure 4: Pseudocode for reﬁning a path.

Figure 5: Left: a dendrite generated on a coarse graph.

Right: a reﬁned version of the coarse dendrite.

3.2 Fractal Site Placement

A fractal dendrite can be created through an iterative

process involving repeatedly adding new endpoints,

and the corresponding paths, to an existing structure.

In the ﬁrst iteration, the structure is a single root node.

In later iterations, we compute paths to the entire

structure obtained at the previous iteration.

At each iteration, we increase the number of end-

points to be placed by a branching factor β. At the

same time, the maximum distance from the generators

that each new endpoint is placed is reduced – divided

by a factor α, the attenuation factor. The process con-

tinues until the maximum distance is less than some

small value, say 2 pixels. Notice that the number of

iterations therefore depends on the attenuation factor;

a larger factor means that the maximum distance de-

cays to a value beneath the threshold more rapidly,

resulting in a sparser dendrite. Figure 7 gives a vi-

sualization of this process; pseudocode describing the

process is given in Figure 6. Images showing different

fractal dendrites are shown in Figure 8.

3.3 Converting to Geometry

We have two options for converting the description

of path locations to geometry. One option is to use

the paths nearly directly, and render each node on the

Input: weighted graph G, containing nodes N and

edges E; an initial generator Z; parameters α and

β; intial number of centres m; initial distance d.

Output: a list of nodes P on the fractal dendrite.

1. Set P to null; append Z to P.

2. Repeat the following while d > ε.

2A. Find m centres, at distances ≈ d from Z.

2B. Find a path from each centre to Z. Append

each path to P.

2C. Set m to m∗ β.

2D. Set d to d/α.

2E. Set Z to P.

Figure 6: Pseudocode for creating fractal dendrites.

Figure 7: A fractal dendrite (four iterations). Initially, we

have only a few branches, but successively more endpoints

are placed at successively smaller distance from the struc-

ture.

path as a simple geometric object, e.g., a sphere. This

approach has the advantage of extreme simplicity.

Another option is to create a distance ﬁeld from

the structure and extract an isosurface from the ﬁeld.

The distance ﬁeld can be computed using the machin-

ery we already have in place: using the dendrite as the

generator, we make a pass of breadth-ﬁrst search over

the lattice. The breadth-ﬁrst search visits each node

in the lattice, populating it with the least-cost path

distance from the generators. The resulting distance

ﬁeld can then be converted to geometry using an exist-

ing isosurface extraction algorithm such as marching

cubes. A visualization of the 2D isosurfaces from a

dendritic generator are shown in Figure 9.

3.4 Creating Solid Objects with Region

Marking

The same workﬂow used to create dendritic shapes

can also be used to generate irregular solid objects.

Specifying multiple disjoint nodes as generators, and

GRAPP 2007 - International Conference on Computer Graphics Theory and Applications

Figure 8: A few fractal dendrites, with different parameters

governing the branching factor and distance limit at each

iteration. Right to left: α = 2,1.5,1.2; top to bottom, β =

2,3,4. All dendrites were built with three iterations.

Figure 9: Two-dimensional isocontours from a dendritic

generator. A surface can be generated by taking one of these

contours.

keeping track during the breadth-ﬁrst search process

of which site is nearest a given lattice node, has the ef-

fect of segmenting the volume. One region is created

for each generator node, consisting of all the points

nearest that node. A mesh marking the boundary of

one of these regions is shown in Figure 10.

The regions from the segmentation are locally ir-

regular but have a simple overall shape. We have re-

ferred to them as “rocks” because they resemble bro-

ken pieces of some hard and not necessarily homoge-

neous material.

Figure 10: “Rock” mesh from lattice segmentation.

4 RESULTS AND DISCUSSION

We next show some further results created by our

method, in the form of images and models. We have

already shown several examples of simple dendritic

forms, from Figure 1 onward. In this section we give

more elaborate models and provide some commen-

tary on the types of models that our approach can

generate. We give unadorned skeletal models and

meshes; more sophisticated rendering, including tex-

ture mapping, could improve the ﬁnal images, but in

this paper we are focusing on the models themselves.

We can readily create dendritic forms, i.e., branch-

ing structures. Branching comes about in our model

owing to the use of a common graph for all pathing

queries: paths to nearby destinations will often share

the early portion of their route, so that a single path

appears to emerge from the source, branching when

the two previously overlapping paths deviate. The

same reasoning, plus the fact that we use a common

set of source nodes for all paths, means that the paths

will never cross one another. Given a consistent tie-

breaking mechanism, there is a unique path from the

source nodes to any node in the graph; hence, two

paths that meet do not cross, but rather share the same

path the rest of the way to the generators.

We have set out to imitate the dendritic forms of

DLA, and the results of this imitation are shown in

Figure 11. The two models are visually extremely

similar, although a detailed investigation would reveal

the limited nature of the path planned dendrite (it is

fractal only over the small range of scales explicitly

programmed in). However, to the unaided human eye

the structures look extremely similar; the pragmatic

difference is that the path planned dendrite took about

MODELING DENDRITIC SHAPES - Using Path Planning

Figure 11: Top: dendritic form generated by diffusion-

limited aggregation. Bottom: imitation of DLA with a path

planned fractal (4 iterations).

100 times less computer time to create.

We used our system to build a model of staghorn

coral, shown in Figure 12. The coral model was cre-

ated by manually placing endpoints in a 3D graph;

the points were not chosen to exactly duplicate the in-

put model, but to give a visually similar appearance,

i.e., the synthetic coral could plausibly have come

from the same underlying growth process. Despite the

small amount of information provided to the modeler

(only the endpoints of the branches were speciﬁed),

the synthetic coral model resembles the real coral

quite well. The synthetic image was rendered using

Pixie (pixie.sourceforge.net), with the high-frequency

structure (thorns) on the surface of the branches ob-

tained from a Renderman displacement shader.

Figure 13 demonstrates one way to exploit the

graph to give high-level control over the dendritic

Figure 12: Left: real coral. Right: coral generated using

path planning.

Figure 13: “Hello” written with dendrites.

shapes. In generating this ﬁgure, we created a sepa-

rate graph for each letter, and arranged the nodes of

the graph into the shape of the desired letter. The

resulting paths ﬁlled a portion of the space within

the graph, causing the letter to become visible. A

similar mechanism could be used to generate three-

dimensional forms, in a manner akin to the synthetic

topiaries of Prusinkiewicz et al. (Prusinkiewicz et al.,

1994). Our 2D result is comparable to the lichen-

writing of Desbenoit et al., who distributed seeds for

DLA in letter-shaped regions.

Competition for space is one of the phenomena

simulatable within the framework of open L-systems.

We can imitate this phenomenon within our frame-

work by placing multiple disjoint generators within

our graph and scattering endpoints nearby. An exam-

ple of competition is shown in the left of Figure 14.

Although the dendrites do not actually communicate

during the path planning process, the dendritic forms

appear to exhibit an avoidance behaviour.

A dendritic form akin to lightning is shown in the

GRAPP 2007 - International Conference on Computer Graphics Theory and Applications

Figure 14: Left: competition for space on the part of two

lichens. Right: endpoint placement producing a shape re-

sembling lightning.

Figure 15: A mossy version of the peppers image.

right of Figure 14. For this 2D example, little needs

to be changed from the typical 2D dendrites we previ-

ously showed: the generator is a single node at the top

of the image, and we initially placed a small number

of endpoints at the bottom of the image, producing the

main lightning strokes. Subsequently, we added more

endpoints in the vicinity of the main strokes, with a

bias towards placing them lower in the image. This

approach can straightforwardly be extended to 3D by

using a 3D lattice.

The lightning example illustrates another control

handle our framework possesses: the endpoints of

the dendrite can be speciﬁed exactly. DLA can offer

control over the endpoints of the main branches (by

starting random walkers from the location the branch

should reach), but control over positions of secondary

branches is more difﬁcult to achieve.

We can produce forms resembling moss by having

a plane or other surface as a generator, and comput-

ing paths to destinations near the surface. Figure 15

shows a large number of paths computed up from the

plane. In this example, paths are rendered as chains

of line segments with lighting given by the variant of

deep shadow maps (Lokovic and Veach, 2000) pro-

posed by Bertails et al. (Bertails et al., 2005), and the

colors of the paths taken from the peppers test image.

Hair or fur could be generated similarly.

Figure 16: Lichen on rock, both generated with our method.

Table 1: Table of model timing results.

Model lattice endpoints time

Simple dendrite 600

15 0.94 s

Fractal dendrite 512

8930 7.55 s

Coral (no reﬁnement) 50

11 1.06 s

Coral (with reﬁnement) 50

24 3.06 s

Figure 16 shows a lichen growing over a rock.

Both the lichen and the rock models were generated

using our method: the rock mesh by segmenting a

lattice as described in section 3.4, and the lichen by

placing both the path endpoints and the dendrite gen-

erator on the rock and forbidding paths to enter inte-

rior rock nodes. Notice, therefore, that the modelled

lichen is able to leave the surface of the rock brieﬂy

before returning; had we so chosen, we could easily

have constrained the lichen to the rock face, simply

by considering only nodes on the rock boundary. The

rock and lichen models in Figure 16 demonstrate the

ﬂexibility of our approach, with two quite different

models generated by the same underlying process.

Table 1 and Table 2 show timing results for our

method. The ﬁgures in Table 1 give times for model

construction; these models were either shown directly

or were rendered using spheres. Timing results in Ta-

ble 1 are given for a 1.8GHz P4 with 512 MB RAM.

Table 2 gives timing ﬁgures for the models rendered

using extracted isosurfaces; here, “modeling” is the

Table 2: Timing data for implicit models.

Model lattice modeling isosurface

Rock model 128

8.0 s 3.2 s

Rock+lichen 128

9.5 s 7.4 s

MODELING DENDRITIC SHAPES - Using Path Planning

time needed to create the lattice, produce the den-

dritic shape, and compute the distance ﬁeld for that

shape, and “isosurface” is the time needed for march-

ing cubes to extract the isosurface from the distance

ﬁeld. In table 2, timing ﬁgures are given with respect

to a 3.2GHz P4 with 1GB RAM.

For 2D dendrites, the sub-one second modeling

time can be considered interactive, so that different

parameter settings can be experimented with live. The

3D modeling times, albeit on a somewhat coarser

grid, are nonetheless only around 10 seconds; for

comparison, the lightning simulations of Kim and Lin

require hours, and the ice simulations (in 2D) still re-

quire at least a few minutes. Desbenoit et al. (Des-

benoit et al., 2004) give times ranging from 1 second

to nearly 500 seconds, depending on the complexity

of the generated lichen. The DLA image shown in

Figure 11 was generated at a resolution of 500× 500

with 25000 particles; the basic random walker algo-

rithm was used on a 3.2GHz P4 and required about

7.5 minutes to complete.

5 CONCLUSIONS

We have presented a fast, simple method for generat-

ing dendritic forms. Because path planning has been

well studied in computer science, many standard al-

gorithms exist and should be familiar to computer

graphics practitioners; in consequence, our algorithm

is easy to implement. The path planning formulation

creates dendrites extremely quickly: less than a sec-

ond for simple structures, and less than 10 seconds for

complex fractal and 3D structures. Orders of magni-

tude more time are required for DLA and other re-

ported systems for creating dendritic shapes.

The range of natural objects expressible as den-

dritic forms is great. In addition to dendrites, the path

planning approach can generate irregular solid objects

by segmenting an input mesh. The versatility of den-

drites, combined with the ability to generate irregu-

lar solid models, gives our method potentially wide

applicability. Unlike L-systems, the path planning

framework is not very mature, and much remains to

be discovered. For example, future work can address

the endpoint placement process, perhaps by distribut-

ing them procedurally in a more sophisticated way.

In this paper, we have given a broad overview of

the dendritic shapes our method can generate. One

avenue for future work is to narrow in on speciﬁc phe-

nomena: lightning, trees, and lichens have long been

of interest in computer graphics, and we ﬁnd moss a

particularly intriguing direction.

ACKNOWLEDGEMENTS

Thanks to Peter O’Donovan for the mossy peppers

image. This work was supported by NSERC RGPIN

299070-04.

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