Variational Implicit Surfaces


Greg Turk, James F. O’Brien

Èñòî÷íèê: ftp://ftp.cc.gatech.edu/pub/gvu/tr/1999/99-15.pdf


Abstract


We introduce a new method of creating smooth implicit surfaces of arbitrary manifold topology. These surfaces are described by specifying locations in 3D through which the surface should pass, and also identifying locations that are interior or exterior to the surface. A 3D implicit function is created from these constraints using a variational scattered data interpolation approach. We call the iso-surface of this function a variational implicit surface. Like other implicit surface descriptions, these surfaces can be used for CSG and interference detection, may be interactively manipulated, are readily approximated by polygonal tilings, and are easy to ray trace. A key strength is that variational implicit surfaces allow the direct specification of both the location of points on the surface and surface normals. These are two important manipulation techniques that are difficult to achieve using other implicit surface representations such as sums of spherical or ellipsoidal Gaussian functions (“blobbies”). We show that these properties make variational implicit surfaces particularly attractive for interactive sculpting using the particle sampling technique introduced byWitkin and Heckbert in [30]. Our formulation also yields a simple method for converting a polygonal model to a smooth implicit model.

1 Introduction


The computer graphics, computer-aided design and computer vision literatures are filled with an amazingly diverse array of approaches to surface description. The reason for this variety is that there is no single representation of surfaces that satisfies the needs of every problem in every application area. This paper introduces variational implicit surfaces, a new surface creation method that we believe will be useful in several areas in 3D modeling. Variational implicit surfaces are smooth, exactly pass through a set of given constraint points, and can describe closed surfaces of arbitrary topology.

Figure 1 (left) shows a variational implicit curve, the 2D analog to a surface, in order to illustrate our basic approach. The small open circles in this figure are constraint positions at which a 2D implicit function must take on the value of zero. The single plus sign indicates the position at which the implicit function is to take on the value one, and in fact any positive value will do. The locations and values (zeros and ones) at the small circles and at the plus sign are constraints that are passed along to a scattered data interpolation routine. The interpolation routine yields a smooth 2D function that meets the given constraints. The desired curve is defined to be the locus of points at which the function takes on the value of zero. The curve exactly passes through each of the zero-value constraints, and its defining function is positive inside this curve and negative outside. For this 2D example, we use a variational technique that minimizes the aggregate curvature of the function that it creates, and this technique is often referred to as thin-plate interpolation.

We can create surfaces in 3D in exactly the same way as the 2D curves in Figure 1. Zero-valued constraints are defined by the modeler at 3D locations, and positive values are specified at one or more places that are to be interior to the surface. A variational interpolation technique is then invoked that creates a scalar-valued function

Figure 1: Curves defined using variational implicit functions. The curve on the left is defined by four zero-valued and one positive constraint. This curve is refined by adding three new zero-valued constraints (at right).

over a 3D domain. The desired surface is simply the set of all points at which this scalar function takes on the value zero. Figure 2 (left) shows a surface that was created in this fashion by placing four zero-valued constraints at the vertices of a regular tetrahedron and placing a single positive constraint in the center of the tetrahedron. The result is a nearly spherical surface. More complex shapes such as that of Figure 2 (right) can be defined simply by specifying a larger number of constraints.

The remainder of this paper is organized as follows. In Section 2 we examine related work and review implicit surface and thin-plate interpolation techniques. We describe in Section 3 the mathematical framework for solving variational problems using radial basis functions. Section 4 presents three strategies that may be used together with variational methods to create implicit surfaces. These strategies differ in where they place the non-zero constraints. In Section 5 we show that variational implicit surfaces are well suited for interactive sculpting. In Section 6 we compare variational implicit surfaces with traditional thin-plate surface modeling and with implicit functions that are created using ellipsoidal Gaussian functions. Section 7 describes two rendering techniques, one that relies on polygonal tiling and another based on ray tracing. Finally, Section 8 indicates potential applications and directions for future research.

2 Background and Related Work

Variational implicit surfaces draw upon two areas of modeling: implicit surfaces and thin-plate interpolation. In this section we briefly review work in these two sub-areas.

2.1 Implicit Surfaces

An implicit surface is defined by an implicit function, a continuous scalar-valued function over the domain R3. The implicit surface of

Figure 2: Surfaces defined by variational implicit functions. The left surface is defined by zero-valued constraints at the corners of a tetrahedron and one positive constraint in its center. The branching surface at the right was created using constraints from the vertices of the inset polygonal object.

such a function is the locus of points at which the function takes on the value zero. For example, a unit sphere may be defined using the implicit function f (x) = 1 - |x|. Points on the sphere are those locations at which f (x)=0. This implicit function takes on positive values interior to the sphere and is negative outside the surface, as will be the convention in this paper. An important class of implicit surfaces are the blobby or metaball surfaces [3, 19]. The implicit functions of these surfaces are the sum of radially symmetric functions that have a Gaussian profile. Here is the general form of such an implicit function:

In the above equation, a single function hi describes the profile of a “blobby sphere” that has a particular center and radius. The bold letter x represents a point in the domain of our implicit function, and in this paper we will use bold letters to represent such points, both in 2D and 3D. The value t is the iso-surface threshold. When the centers of two blobby spheres are close enough to one another, the implicit surface appears as though the two spheres have melted together. A typical form for a blobby sphere function hi is the following:

In this equation, the constant sigmai specifies the standard deviation of a Gaussian function, and thus is the control over the radius of a blobby sphere. The center of a blobby sphere is given by ci. Evaluating an exponential function is computationally expensive, so some authors have used piecewise polynomial expressions instead of exponentials to define these blobby sphere functions [19, 31]. A greater variety of shapes can be created with the blobby approach by using ellipsoidal rather than spherical atomic functions.

Another important class of implicit surfaces are the algebraic surfaces. These are surfaces that are described by polynomial expressions in x, y and z. If a surface is simple enough, it may be described by a single polynomial expression. A good deal of attention has been devoted to this approach, and a good entry point into these kinds of surfaces is the work of Gabriel Taubin [27]. Much of the work in this area has been devoted to fitting an algebraic surfaces to a given collection of points. Usually it is not possible to interpolation all of the data points, so error minimizing techniques are sought. Surfaces may also be described by piecing together many separate algebraic surface patches, and here again there is a large literature on the subject. Good introductions to these surfaces may be found in the chapter by Chanddrajit Bajaj and the chapter by Alyn Rockwood in [6]. It is easier to control the approximation or interpolation of a collection of data points using algebraic patches than with a single algebraic surface. The tradeoff, however, is that these patches require a good deal of machinery to create smooth joins across patch boundaries.

2.2 Thin-Plate Interpolation and Variational Techniques

Thin-plate spline surfaces are a class of height fields that are closely related to the variational implicit surfaces of this paper. Thinplate interpolation is one approach to solving the scattered data interpolation problem. The two-dimensional version of this problem can be stated as follows: Given a collection of k constraint points {c1;c2; ... ;ck} that are scattered in the xy-plane, together with scalar height values at each of these points {h1;h2; ... ;hk}, construct a “smooth” surface that matches each of these heights at the given locations. We can think of this solution surface as a scalarvalued function f (x) so that f (ci) = hi, for 1<=i<=k. Ifwe define the word smooth in a particular way then there is a unique solution to such a problem, and this solution is the thin-plate interpolation of the points. Consider the energy function E:

The notation fxx means the second partial derivative in the x direction, and the other two terms are similar partial derivatives, one of them mixed. This energy function is basically a measure of the aggregate curvature of f (x) over the region of interest Omega (usually a portion of the plane). Any creases or pinches in a surface will result in a larger value of E. A smooth surface that has no such regions of high curvature will have a lower value of E. Note that because there are only squared terms in the integral, the value for E can never be negative. The thin-plate solution to an interpolation problem is the function f (x) that satisfies all of the constraints and that has the smallest possible value of E. Note that the most common form of thin-plate surfaces are restricted to be height fields, and thus they are in fact parametric surfaces.

This interpolation method gets its name because it is much like taking a thin sheet of metal, laying it horizontally and bending the it so that it just touches the tips of a collection of vertical poles that are set at the positions and heights given by the constraints of the interpolation problem. The metal plate resists bending so that it smoothly changes its height in the positions between the poles. This springy resistance is mimicked by the energy function E. Thin-plate interpolation is often used in the computer vision domain, where there are often sparse surface constraints [13, 28]. The above curvature minimization process is sometimes referred to as regularization, and can be thought of as an additional constraint that selects a unique surface out of an infinite number of surfaces that match a set of given height constraints. Solving such constrained problems draws from a branch of mathematics called the variational calculus, thus thin-plate techniques are sometimes referred to as variational methods.

The scattered data interpolation problem can be formulated in any number of dimensions. When the given points ci are positions in N-dimensions rather than in 2D, this is called the N-dimensional scattered data interpolation problem. There are appropriate generalizations to the energy function and to thin-plate interpolation for any dimension. In this paper we will make use of variational interpolation in two and three dimensions.

3 Variational Methods and Radial Basis Functions The scattered data interpolation task as formulated above is a variational problem where the desired solution is a function, f (x), that will minimize equation 3 subject to the interpolation constraints f (ci) = hi. There are several numerical methods that can be used to solve this type of problem. Two commonly used methods, finite elements and finite differencing techniques, discretize the region of interest, W, into a set cells or elements and define local basis functions over the elements. The function f (x) can then expressed as a linear combination of the basis functions so that a solution can be found, or approximated, by determining suitable weights for each of the basis functions. This approach has been widely used for height-field interpolation and deformable models, and examples of its use can be found in [28, 26, 7, 29]. While finite elements and finite differencing techniques have proven useful for many problems, the discretization that they require can lead to artifacts. An alternate approach is to express the solution in terms of radial basis functions centered at the constraint locations. Radial basis functions are radially symmetric about a single point, or center, and they have been widely used for function approximation. Remarkably, it is possible to choose these radial functions is such a way that they will automatically solve differential equations, such as the one required to solve equation 3, subject to constraints located at their centers. For the 2D interplation problem, equation 3 can be solved using the biharmonic radial basis function:

This is commonly know as the thin-plate radial basis function. For 3D interpolation, the appropriate radial basis function to use is f(x) = |x|3. Duchon did much of the early work on variational interpolation[8], and the report by Girosi, Jones and Poggio is a good entry point into the mathematics of variational interpolation [11]. Using the appropriate radial basis functions, we can then write the interpolation function in the following form:

In the above equation, cj are the locations of the constraints, the dj are the weights, and P(x) is a degree one polynomial that accounts for the linear and constant portions of f . Solving for the weights dj and the coefficients of P(x) subject to the given constraints yields a function that both interpolates the constraints and that minimizes equation 3. The resulting function exactly interpolates the constraints, and is not subject to approximation or discretization errors. Also, the number of weights to be determined does not grow with the size of the region of interest Omega. Rather, it is only dependent on the number of constraints. To solve for the set of dj that will satisfy the interpolation constraints

substitute the right side of equation 5 for f (ci) giving

In the above equation, N is the dimension of the domain of our interpolation function (either N = 2 or N =3 in this paper). Since this equation is linear with respect to the unknowns, dj and the coefficients of P(x), it can be formulated as a linear system. For interpolation in 3D, let ci =(cxi ;cyi ;czi ) and let fij =f(ci - cj). Then this linear system can be written as follows:

It can be show that the above system is symmetric and positive semi-definite, so there will always be a unique solution for the di and pi [12]. We used symmetric LU decomposition to solve this system of equations for all of the examples shown in this paper. Our implementation to set up the system, call the LU decomposition routine and evaluate the interpolating function of equation 5 for a given x consists of about 100 lines of commented C++ code. This code plus the public-domain polygonalization routine described in Section 7.1 is all that is needed to create variational implicit surfaces, and all of this can be assembled in an afternoon of programming.

Two concerns that arise with such matrix systems are computation times and ill-conditioned systems. For systems with up to a few thousand centers, including all of the examples in this paper, direct solution techniques such as LU decomposition and SVD are practical. However as the system becomes larger, the amount of work required to solve the system grows as O(k3). Work by Beatson and by Suter describe fast evaluation techniques that can be used to evaluate equation 5 in O(1) rather than the O(k) implied by the summation [25, 1]. The cost of solving the system can be reduced to approximately O(k2) by using the a fast evaluation technique with an iterative solution method such as biconjugate gradient, and much larger systems become feasible.

As the number of constraints grows, the condition number of the matrix in equation 8 is also likely to grow, leading to instability for some solution methods. Although we have been on the lookout, we have not noticed artifacts in our models that would arise from such ill-conditioning. Even if such problems do arise, variational interpolation is such a well-studied problem that methods exist for improving the conditioning of the system of equations [10].

4 Creating Variational Implicit Surfaces


With tools for solving the scattered data interpolation problem in hand, we now turn our attention to creating implicit functions. In this section, we will examine three ways in which to define a variational implicit surface. Common to all three approaches is the specification of zero-valued constraints through which the surface must pass. The three methods differ in specifying where the implicit function takes on positive and negative values. We will look at creating both 2D variational implicit curves and 3D variational implicit surfaces. The 2D curve examples are for illustrative purposes, and our actual goal is the creation of 3D surfaces.

4.1 Positive Interior Constraints

The left portion of Figure 1 (earlier in this paper) shows the first method of describing a variational implicit curve. Four zero-valued constraints have been placed in the plane. We call such zero-value constraints boundary constraints because these points will be on the boundary between the interior and exterior of the shape that is being defined. In addition to the four boundary constraints, a single constraint with a value of one is placed at the location marked with a plus sign. We construct an implicit function from these five constraints simply by invoking the 2D variational interpolation technique described in earlier sections. The interpolation method returns a set of scalar coefficients wi that weight a collection of radially symmetric functions f that are centered at the constraint positions. The implicit curve shown in the figure is given by those locations at which the variationally-defined function takes on the value zero. The function takes on positive values inside the curve and is negative at locations outside the curve. Figure 1 (right) shows a refinement of the curve that is made by adding three more boundary constraints to the original set of constraints in the left portion of the figure. Why does a positive constraint surrounded by zero-valued constraints yield a function that is negative beyond the boundary constraints? The key is that the energy function is larger for functions that take on positive values on both sides of a zero-valued constraint. Each boundary constraint acts much like a see-saw— pull one side up and the other side tends to move down. Creating surfaces in 3D is accomplished in exactly the same way as the 2D case. Zero-valued constraints are specified by the modeler as those 3D points through which the surfaces should pass, and positive values are specified at one or more places that are to be interior to the surface. Variational interpolation is then invoked to create a scalar-valued function over R3. The desired surface is simply the set of all points at which this scalar function takes on the value zero. Figure 2 (left) shows a surface that was created in this fashion by placing four zero-valued constraints at the vertices of a regular tetrahedron and placing a single positive constraint in the center of the tetrahedron. The resulting implicit surface is nearly spherical.

Figure 3: Curves defined using surrounding negative constraints. Just two zero-valued constraints yield an ellipse-like curve (on the left). More constraints create a more complex curve (at right).

Figure 2 (right) shows a recursive branching object that is a variational implicit surface. The basic building block of this object is a triangular prism. Each of the six vertices of a large prism specified the location of a zero-valued constraint, and a single positive constraint was placed in the center of this prism. Next, three smaller and slightly tilted prisms were placed atop the first large prism. Each of these smaller prisms, like the large one, contributes boundary constraints at its vertices and has a single positive-valued constraint placed at its center. Each of the three smaller prisms have even smaller prisms placed on top of them, and so on.

Placing one or more positive-valued constraints on the interior of a shape is an effective method of defining variational implicit surfaces when the shape one wishes to create is well-defined. We have found, however, that there is another approach that is even more flexible for interactive free-form surface sculpting.

4.2 Negative Exterior Constraints

Figure 3 illustrates a second approach to creating variational implicit functions. Instead of placing positive-valued constraints inside a shape, negative-valued constraints can be placed on the exterior of the shape that is being created. As before, zero-valued constraints specify locations through which the implicit curve will pass through. In Figure 3 (left), eight negative-valued constraints surround the region at which a curve is being created. As with positivevalued constraints, the magnitude of the values is unimportant, and we use the value negative one. These negative constraints, coupled with the curvature-minimizing nature of variational method, induce the interpolation function to take on positive values interior to the shape outlined by the zero-valued constraints. Even specifying just two boundary constraints defines a reasonable closed curve, as shown by the ellipse-like curve at the left in Figure 3. More boundary constraints result in a more complex curve, as shown on the right in Figure 3.

We have found that creating a circle or sphere of negative-valued constraints is the approach that is best suited to interactive freeform design of curves and surfaces. Once these negative constraints are defined, the user is free to place boundary constraints in any location interior to these negative constraints. Section 5 describes the use of exterior constraints for interactive sculpting.

4.3 Constraints Near the Boundary: Normal Constraints

For some applications we may have detailed knowledge about the shape that is to be modeled. In particular, we may know approximate surface normals at many locations on the surface to be created. In this case there is a third method of defining a variational

Figure 5: A polygonal surface (left) and the variational implicit surface defined by the 800 vertices and their normals (right).

implicit function that may be preferred over the two methods described above. Rather than placing positive or negative values far from the boundary constraints, we can create constraints very close to the boundary constraints. Figure 4 shows this method in the plane. In left portion of this figure, there are six boundary constraints and in addition there are six normal constraints. These normal constraints are positive-valued constraints that are placed very near the boundary constraints, and they are positioned towards the center of the shape that is being created. A normal constraint is created by placing a positive constraint a small distance in the direction -N, where N is an approximate normal to the shape that we are creating. (Alternatively, we could choose to place negativevalued constraints in the outward-pointing direction.) A normal constraint is always paired with a boundary constraint, although not every boundary constraint requires a normal constraint. The right part of Figure 4 shows that a normal constraint can be used to bend a curve at a given point.

Figure 4: Two curves defined using nearly identical boundary and normal constraints. By moving just a single normal constraint (the north-west one), the curve on the left is changed to that shown on the right.

There are at least two ways in which a normal constraint might be defined. One way is to allow a user to hand-specify the surface normals of a shape that is being created. A second way allows us to create smooth surfaces based on polyhedral models. If we wish to create a variational implicit surface from a polyhedral model, we simply need to create one boundary constraint and one normal constraint for each vertex in the polyhedron. The location of a boundary constraint is given by the position of the vertex, and the location of a normal constraint is given by moving a short distance in a direction opposite to the surface normal at the vertex. We place normal constraints 0:01 units from the corresponding boundary constraints for objects that fit within a unit cube. Figure 5 (right) shows a variational implicit surface created in the manner just described from the polyhedral model in Figure 5 (left). This is a simple yet effective way to create an everywhere smooth analytically defined surface. This stands in contrast to the complications of patch stitching inherent in most parametric surface modeling approaches.

In this section we have seen three methods of creating variational implicit functions. These methods are in no way mutually exclusive, and a user of an interactive sculpting program could well use a mixture of these three techniques to define a single surface.

5 Interactive Model Building


Variational implicit surfaces seem ready-made for interactive 3D sculpting. In this section we will describe how they can be gracefully incorporated into an interactive modeling program.

In 1994, Andrew Witkin and Paul Heckbert presented an elegant method for interactive manipulation of implicit surfaces [30]. Their method uses two types of oriented particles that lie on the surface of an implicitly defined object. One class of particles, the floaters, are passive elements that are attracted to the surface of the shape that is being sculpted. Floaters repel one another in order to evenly cover the surface. Even during large changes to the surface, a nearly constant density of floaters is maintained by particle fissioning and particle death. A second type of particle, the control

Figure 6: Interactive sculpting of variational implicit surfaces. The left image shows an initial configuration with four boundary constraints (the red markers). The right surface is a sculpted torus.

point, is the method by which a user interactively shapes an implicit surface. Control points provide the user with direct control of the surface that is being created. A control point tracks a 3D cursor position that ismoved by the user, and the free parameters of the implicit function are adjusted so that the surface always passes exactly through the control point. The mathematical machinery needed to implement floaters and control points is presented clearly inWitkin and Heckbert’s paper, and the interested reader should consult it for details.

The implicit surfaces used in Witkin and Heckbert’s modeling program are blobby spheres and blobby cylinders. We have created an interactive sculpting program based on their particle sampling techniques, but we use variational implicit surfaces instead of blobbies as the underlying shape description. Our implementation of floaters is an almost verbatim transcription of their equations into code. The only change needed was to represent the implicit function as a sum of f(x) =jxj3 radial basis functions and to provide an evaluation routine for this function and its gradient. Floater repulsion, fissioning and death work just as well as when using blobby implicit functions. As in the original system, the floaters provide a means of interactively viewing an object during editing that may even change the topology of the surface.

The main difference between our sculpting system and Witkin and Heckbert’s is that we use an entirely different mechanism for direct interaction with a surface. Witkin/Heckbert control points provide an indirect link between a 3D cursor and the free parameters of a blobby implicit function. Because our system is based on variational implicit functions, however, we simply allow users to directly create and move boundary constraints.

We initialize a sculpting session with a simple variational implicit surface that is nearly spherical, and this is shown at the left in Figure 6. It is described by four boundary constraints at the vertices of a unit tetrahedron (the thick red disks) and with eight exterior (negative) constraints surrounding these at the corners of a cube with a side width of six. (The exterior constraints are not drawn.) A user is free to drag any of the boundary constraint locations using a 3D cursor, and the surface follows. The user may also create any number of new boundary constraints on the surface. The location of a new boundary constraint is found by intersecting the surface with a ray that passing through the camera position and the cursor. After a user creates or moves a boundary constraint, the matrix equation from Section 3 is solved anew. The floaters are then moved and displayed. The right portion of Figure 6 shows a toroidal surface that was created using this interactive sculpting paradigm. The interactive program continuously executes the following loop:

repeat create or move constraints based on user interaction solve new variational matrix equation adjust floater positions (with floater birth and death) render floaters

An important consequence of the matrix formulation given by equation 8 is that adding a new boundary constraint on the existing surface does not affect the surface shape at all. This is because the implicit function already takes on the value of zero at the surface, so adding new zero-valued constraint on the surface will not alter the weights of the old constraints. Only when such a new boundary constraint is moved does it begin to affect the shape of the surface. This ability to retain the exact shape of a surface while adding new boundary constraints is similar in spirit to knot insertion for polynomial spline curves and surfaces. We do not know of any similar capability for blobby implicit surfaces.

In addition to control of boundary constraints, we also allow a user to create and move normal constraints. By default, no normal constraint is provided for a newly created boundary constraint. At the user’s request, a normal constraint can be created at any specified boundary constraint. The initial direction of the normal constraint is given by the gradient of the current implicit function. The value for such a constraint is given by the implicit function’s value at the constraint location. A normal constraint is drawn as a spike that is fixed at one end to the disk of its corresponding boundary point. The user may drag the free end of this spike to adjust the normal to the surface, and the surface follows this new constraint.

What has been gained by using variational implicit functions instead of blobby spheres and cylinders? First, the variational implicit approach is easier to implement because the optimization machinery for control points of blobby implicits is not needed. Second, the user has control over the surface normal as well as the surface position. Finally, the user does not need to specify which implicit parameters are to be fixed and which are to be free at different times during the editing session. Using the blobby formulation, the user must choose at any given time which parameters such as sphere centers, radii of influence and cylinder endpoints may be altered by moving a control point. With the variational formulation, the user is always changing the position of just a single boundary or normal constraint. We believe that this direct control of the parameters of the implicit function is more natural and intuitive. Witkin and Heckbert state the following [30]:

Another result of this work is that we have discovered that implicit surfaces are slippery: when you attempt to move them using control points they often slip out of your grasp. [emphasis from the original] We have found that variational implicit surfaces are not at all slippery. Users easily grasp and re-shape these surfaces with no thought to the underlying parameters of the model.

6 Conclusion and Future Work


In this paper we have introduced variational implicit surfaces, a new paradigm for creating implicit surfaces. Specific advantages of this method include:

  • Direct specification of points on the implicit surface
  • Specification of surface normals
  • Conversion of polygon models to smooth implicit forms
  • Intuitive controls for interactive sculpting
  • Addition of new control points that leave the surface unchanged

(like knot insertion) A number of techniques have been developed for working with implicit surfaces. Many of these techniques could be directly applied to variational implicits, indicating several directions for future work. The critical point analysis of Stander and Hart could be used to guarantee topologically correct tessellation of such surfaces [24]. Interval techniques, explored by Duff, Snyder and others, might be applied to tiling and ray tracing of variational implicits [9, 23]. The interactive texture placement methods of Pedersen should be directly applicable to variational implicit surfaces [20, 21]. Finally, many marvelous animations have been produced using blobby implicit surfaces [3, 31]. We anticipate that the interpolating properties of variational implicit surfaces may provide animators with an even greater degree of control over implicit surfaces. Beyond extending existing techniques for this new form of implicit surface, there are also research directions that are suggested by issues that are specific to our variational technique. Like blobby sphere implicits, variational implicit surfaces are everywhere smooth. Perhaps there are ways in which sharp features such as edges and corners can be incorporated into a variational implicit model. We have showed how gradients of the implicit function may be specified indirectly using positive constraints that are near zero constraints, but it may be possible to modify the approach to allow the exact specification of gradient values. Finally, these surfaces are well behaved even with sparse sets of constraints such as those found using computer vision techniques. Thin-plate methods have been used extensively in computer vision, but the resulting surfaces have been limited to a height-field topology. We plan to investigate application areas such as surface reconstruction from stereo depths that could benefit from the topological freedom of the variational implicit surface approach.

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