Rust Aging Simulation Considering Object Geometries
Katsuhisa Kanazawa†

Ryoma Tanabe‡† Tokyo Healthcare University
Setagaya-ku, Tokyo, Japank-kanazawa@thcu.ac.jp

Tomoaki Moriya‡

Tokiichiro Takahashi‡*‡ Tokyo Denki University
Adachi-ku, Tokyo, Japan{kanazawa, r-tanabe, moriya, toki}@vcl.jp* UEI Research

Figure 2: Generated rusty appearance by using our method.

1 Introduction
Realistic representation of nature scenes is one of the mostchallenging areas in computer graphics community. There areimportant factors to synthesize realistic scenes in 3D CG which are
aged materials such as dead trees, weathered statues, rusty metals
and so on. We are interested in the methodology for simulating its
decaying processes. In this paper, we propose a simple method forrust aging simulation based on a probabilistic cellular automaton
model taking into account object’s geometries.

2 Proposed Method

probability that indicates corrosiveness, and it is in the range [0,1].
A cell with greater ρ tends to cause main-rust. d is a 2D vector thatindicates growth direction of rust. Main and sub-rust propagate
toward this direction d.

2.2 Simulation Algorithm
We used 512x512 cells for our simulation. Once cells are set up,
the update process is applied iteratively to all cells. The outline ofthis process is described in List 1. There are applicable ways toselect a cell randomly from its neighbouring cells at steps 3.1 and4.1. Here, the neighbouring cells are cells in a circle with a certain
radius. There are also applicable areas spreading from C'/ C" insteps 3.3 and 4.2 along the direction d. Here, the spreading area isan oblique ellipsoid as illustrated in Figure 1.

Rust aging is a progressive phenomenon that rust iteratively grows
and propagates while causing new rust. We have modeled growthand propagation of rust as a probabilistic cellular automaton model[Tanabe et al. 2015]. In this paper, we extend our previous method,in which simulated rust can grow and propagate toward arbitrary
direction. This mechanism enables to control rust growthconsidering surface orientation as well as the gravity direction. In
this section, we describe details of our method.

After repeating the update process a predetermined number oftimes, χ and ψ of each cells have natural gradients. It is possible
to get a texture image by visualizing these gradients of χ and ψ byappropriate color scales.

2.1 Probabilistic Cellular Automaton

4 Results

We consider two types of rusts: main-rust and sub-rust. Main-rustmeans a part of a material surface that is grossly corroded. Sub-rustis its adjacent areas that are eroded by exfoliation, water exuded
from a main-rust, and mixture of them. Actually, a stain spot
spreads from a rusty spot. These rusty spots and stain spots arecorresponding to main-rust and sub-rust, respectively.
We modeled this structure as a probabilistic cellular automaton. In
our model, each cell has four attributes: χ, ψ, ρ, and d. χ and ψ is amain/sub-rust growth levels that indicate how the cells are corroded
by main/sub-rust. χ and ψ are set to 0 initially and are incremented
at some simulation conditions are matched. ρ is a corrosion

2.3 Visualization

Figure 2 shows rust aging generation results by our method. In thisexperimental generation, d of each cell is determined by referringa gradient of a face corresponding to a cell in targeted 3D model.
Different appearances of rust propagation can be observed on topand side faces of this half cylinder as shown in Figure 2.

References
TANABE, R., MORIYA, T., MORIMOTO, Y., AND TAKAHASHI, T. 2015.
A Generation Method of Rust Aging Texture Considering RustSpreading. Proc. In Proc. 2015 Joint Conference of IWAIT andIFMIA, https://db.tt/0lqfUR2P

1. Iterate following steps for all cells a certain number of times.
 The current cell is described as C.
2. Increment χ of C with probability ρ.
3. If χ of C is greater than 1:
3.1 Choose a cell C' from C’s neighbouring cells.
3.2 Increment χ of C'.
3.3 Increment χ of cells in an area spread from C' along d.
4. If χ or ψ of C is greater than 1:
4.1 Choose a cell C" from C’s neighbouring cells.
4.2 Increment ψ of cells in an area spread from C" along d.

List 1 : The outline of our simulation algorithm.

Figure 1 : Neighboring cells and a spreading area.