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JOURNAL OF IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 13, NO. 9, SEPTEMBER 2015 2
complex topological changes while preserving the surface
details. Both accuracy and efficiency are greatly improved
compared to the standard level set methods and the VIIM,
as is demonstrated in the results part. In the next section,
we briefly discuss some related work. Section 3 provides an
overview of our method, followed by several sections that
explain each part of our method in detail. Finally, we give
the results of our method in Section 7 and conclude our
paper in Section 8.
2RELATED WORK
2.1 Multiphase Interface Tracking
Level set methods [3], [5], [8], [9], [10] are commonly used
in fluid simulation. For multiphase interface tracking, each
phase is represented as a separate level set function, i.e., the
multiple level set (MLS) method. However, this may cause
inconsistencies in the interfacial regions. To solve this prob-
lem, Merriman et al. [11] presented a predictive-corrective
scheme and Losasso et al. [12] used a post-projection opera-
tion. Zhang et al. [13] further reduced the numerical errors
to the grid resolution level by applying multiple corrector
iterations. Starinshak et al. [14] also presented a new level
set model to reduce the numerical errors. Memory overhead
is one of the main concerns of MLS methods as it increas-
es with the number of the phases, which easily becomes
its bottleneck. Alternatively, Zheng et al. [15] proposed a
regional level set method to track multi-manifold surfaces,
by defining new operators for the level sets. Although the
regional level set method has been widely used in bubbling
dynamics [15], [16], the extension to more-than-two-phase
applications was not clear. Later, Kim et al. [17] improved
the regional level set method by applying a regional level
set graph to track thin films between adjacent phases to
address the problem in multiphase fluid simulation. Saye
and Sethian [4], [6] presented the VIIM, which is also built
on classical level sets, to track multiphase interfaces with
detailed numerical analysis.
More recently, Da et al. [18] proposed the first mesh-
based multimaterial front tracking method. Misztal et
al. [19] tracked the multiphase interface using the deformable
simplicial complex. These mesh-based tracking methods can
preserve thin features well, but also induce lots of com-
plicated mesh operations. Our method avoids the mesh
operations by reconstructing the mesh surfaces at each time
step.
2.2 Interface Reconstruction
Bloomenthal and Ferguson [20] proposed one of the first
approaches for generating non-manifold meshes defined by
multiple regions of space. Ronald and Kevin [21] proposed
an adaptive polygonization of non-manifold implicit sur-
faces with octree subdivision. Hege et al. [22] extended the
basic marching cubes algorithm by allowing multiple vertex
classes with an automatic method for generating topolog-
ically correct triangulations similar as our polygonization
procedure but with larger stencils for up to only 3 classes.
Bertram et al. [23] generated a quadrilateral for every edge
connecting voxels with two different materials on a dual
grid composed of voxels. Bernhard et al. [24] made use of
domain subdivision to construct non-manifold meshes from
multi-labeled volumetric datasets. Wu and Sullivan [25]
proposed the multi-material marching cubes (M3C) algorith-
m, which extracted boundary surfaces between different
materials. Most of these works deal with multi-label data
without distance value and generate surfaces that suffer
from stair-stepped artifacts. The artifacts may be removed
with post processing, such as, smoothing. However, it may
also smear out detailed features. Our polygonization proce-
dure takes all the information of the distance and indicator
into consideration and improves the accuracy of intersection
computation with bisection. To make it fast enough for
simulation, we design a new set of stencils which is suitable
for the polygonization of arbitrary number of phases.
Dey et al. [26] applied a recent Delaunay refinement algo-
rithm to generate high quality triangular interface surfaces.
Bronson et al. [27] introduced a new algorithm for generat-
ing tetrahedral meshes that conform to volumetric domains
of multiple materials. Saye and Sethian [6], [28] extracted
the multiphase interfaces with piecewise linear interpola-
tion and further quality-improved under the framework
of VIIM, which requires the continuous piecewise inter-
polation of every distance function and mesh abstraction
and chopping. Ju et al. [29] presented a dual contouring
method for Hermite data, which might also be used for
multiphase contouring. Anderson et al. [30], [31] addressed
the Material Interface Reconstruction problem in the standard
VOF method.
3OVERVIEW
Since our method is built on the VIIM and SLC, we first
briefly review these two methods.
The VIIM applies an unsigned distance function together
with a material indicator function to track the entire multi-
phase system on a regular Eulerian grid. Instead of tracking
the distance and indicator function directly, VIIM tracks
the movement of
-surface and reconstructs the multiphase
interface as the Voronoi interface of the
-surface. VIIM
handles multiphase interfaces with a large number of phases
easily and handles the topological changes automatically
without special treatments. The main drawback of VIIM
is the loss of volume and thin features. Besides, it’s also
difficult to be made adaptive. The uniform representation of
multiphase system used in VIIM greatly inspired our work.
The SLC was developed under the framework of semi-
Lagrangian level set method. Several intelligent modifi-
cations were made to improve the accuracy. It maintains
an explicit mesh that defines the surface and replaces the
interpolation of distance function with exact distance com-
putation, which eliminates substantial interpolation errors.
To make the computation efficient, an octree data structure
is carefully designed to provide fast retrieval of the mesh
and a means for approximating the signed distance. Addi-
tionally, SLC uses bisection to locate the intersections along
the edges with exact evaluation of the distance function.
SLC is excellent in preserving surface features and volumes.
However, SLC only works for two-phase problem, so we
improve it to handle multiphase problem. For more details
of the development of SLC, please refer to the theoretical
work of Strain [32] and Bargteil et al. [7]