Mapping 3D shapes to one another is a basic task in computer graphics and geometry processing. Correspondence is needed, for example, to transfer artist-generated assets such as texture and pose from one mesh to another [Sumner and Popović, 2004]
, to compute in-between shapes using shape interpolation[Heeren et al., 2012; Von-Tycowicz et al., 2015], and to carry out statistical shape analysis [Munsell et al., 2008]. In these applications, desirable correspondences satisfy some basic key properties: They should be smooth to avoid introducing geometric noise during transfer; they should preserve semantic features to ensure that key features are put in correspondence; and they should be reversible, namely invariant to which of the two shapes is chosen as the source.
Many exiting approaches to shape mapping focus on generating maps with low global distortion (e.g. preserving pairwise distances [Sahillioğlu and Yemez, 2011]) at the expense of large local distortion, which reduces the quality of the correspondence and hinders downstream applications. On the other hand, approaches that minimize local distortion measures mostly require an intermediate domain and construct the final map as a composition through this domain (e.g. [Aigerman and Lipman, 2016]). While such methods minimize distortion of the maps into the intermediate domain, the distortion of the composed map can be large. This problem is exacerbated when the input shapes have significantly different geometric features, such as four-legged animals with different dimensions, e.g. a cat and a giraffe. In this case, the isometric distortion of the optimal map is expected to be large, and thus minimizing the distortion of the two maps into an intermediate domain is quite different from minimizing the distortion of the composition.
We propose a novel approach for computing a smooth and reversible map between surfaces that are not isometric to each other, without requiring an intermediate domain. We incorporate semantic information by starting from some user guidance given in the form of sparse landmark constraints or a functional correspondence. Our main contribution is the formulation of an optimization problem whose objective is to minimize the geodesic Dirichlet energy of the forward and backward maps, while maximizing their reversibility. We compute an approximate solution to this problem using a high-dimensional Euclidean embedding and an optimization technique known as half-quadratic splitting [Geman and Yang, 1995]. We demonstrate that our maps have considerably lower local distortion than those from state-of-the-art methods for the difficult case of non-isometric deformations. We further show that our maps are semantically accurate by measuring their adherence to self-symmetries of the input shapes, their agreement with ground-truth when the deformation is known, and their compatibility with human-generated segmentations.
1.1. Related Work
The shape correspondence literature is vast, and we refer the reader to recent surveys for a thorough review of the state-of-the-art in geometry-driven [Van Kaick et al., 2011; Tam et al., 2013] and data-driven [Xu et al., 2016] shape correspondence. We will focus our related work overview on methods for computing maps between triangular meshes that can handle shapes that are far from being related by an isometric deformation. In this realm, we characterize methods by the type of input they require and the type of output they generate. We therefore distinguish between vertex-to-vertex maps that yield a correspondence between the source and target vertices of the triangular meshes, precise maps that map every vertex on the source to a point on the triangulated surface of the target, and generalized
maps that put in correspondence functions or probability distributions.
Kim et al.  suggested one of the first fully automatic methods for non-isometric shape matching, that consistently generated high-quality outputs on a benchmark of shapes. This approach, denoted by BIM, generates a precise map as a blending of conformal maps, with blending weights optimized to minimize isometric distortion. While providing excellent results in many cases, BIM is limited to genus-zero surfaces and can introduce large distortions for some shapes. Recently, Zheng et al.  suggested to map between high-genus surfaces with the same genus, by decomposing the surfaces using a pants decomposition, and then computing harmonic maps between a set of intermediate cylindrical domains. This leads to a piecewise harmonic map between the input surfaces, which is further relaxed using geodesic heat flow. While this approach can be used without user intervention, if a globally semantic map is needed then accurate input landmarks are required, and it is limited to shapes of the same genus. Finally, Lähner et al.  suggested a method that computes vertex-to-vertex maps based on a set of matching descriptors and pairwise distances. While the method is robust to topological changes, it may leave areas unmapped, and is therefore less appropriate for applications such as texture transfer.
The functional map approach, introduced by Ovsjanikov et al. , was originally designed for nearly-isometric shapes but has since been extended to non-isometric matching [Kovnatsky et al., 2013, 2015; Burghard et al., 2017]. This method generates a generalized map which puts in correspondence the function spaces on the mapped shapes. Nogneng and Ovsjanikov  recently suggested a method that computes functional maps using fewer input descriptors by formulating commutativity constraints, and Huang and Ovsjanikov  suggested to use adjoint functional maps to improve and analyze correspondences. Using a hybrid approach, Maron et al.  optimize jointly for a functional map and sparse correspondences, which are then extended to a dense vertex-to-vertex map. Finally, Solomon et al.  compute a generalized map, which puts in correspondence probability distributions on the input shapes, by minimizing the Gromov–Wasserstein distance between the shapes.
While generalized maps are beneficial for challenging mapping problems, such as mapping between shapes of different genus, extracting a precise map is a necessary post-processing step if the output map is to be used for transferring high-frequency data such as textures, normals, or deformations. Furthermore, when the shapes are geometrically different and the optimal map is not expected to be isometric, the shape correspondence problem is ill-posed without additional semantic information. Such information can be given in the form of landmark constraints or an initial generalized map, from which a refined dense map can be computed.
Parameterization-based approaches compute bijective smooth maps to a common intermediate domain, and define precise maps between arbitrary shapes as the composition of the maps to the common domain. A variety of intermediate domains have been used in the literature, e.g. the plane [Aigerman et al., 2015], the sphere [Gu et al., 2004], the hyperbolic disk [Shi et al., 2017] and orbifolds [Tsui et al., 2013; Aigerman and Lipman, 2015, 2016; Aigerman et al., 2017], to mention a few. These methods optimize the distortion of the map from the shapes to the target domain, but the composed map is not guaranteed in general to have low distortion. Furthermore, mapping through an intermediate domain places a topological restriction on the type of mapped shapes, as they should be topologically equivalent. Alternatively, Panozzo et al.  compute a direct map between two triangle meshes without requiring an intermediate domain. Their method computes on-surface barycentric coordinates with respect to the source landmarks, and then uses them with respect to the target landmarks to compute the corresponding point on the target shape. Similar to our approach, they use a high-dimensional Euclidean embedding to speed up the computation. Despite excellent results, in some cases a large number of landmarks is required to compute a correct correspondence. More recently, Mandad et al.  use landmarks or extrinsic alignment for initialization and then optimize simultaneously for a generalized map and a precise map. We compare with their approach and demonstrate that our maps achieve better conformal distortion and adherence to the shape semantics.
Input: generalized maps.
Several methods for recovery of vertex-to-vertex maps from generalized maps have been suggested. Shtern and Kimmel  suggest a refinement technique that is based on heat kernel alignment, Rodolà et al.  use a probabilistic model, and Vestner et al.  solve a linear assignment problem. In general, vertex-to-vertex map have higher conformal distortion than precise maps. Alternatively, Ezuz and Ben-Chen  suggest a pointwise recovery method that generates precise maps using a smoothness prior, based on a spectral approach. Our method does not rely on spectral representations, and thus exhibits fewer artifacts in complex cases.
We present an algorithm for shape correspondence between non-isometric triangular meshes, that has the following advantages:
The algorithm is widely applicable, and the resulting maps are semantic and exhibit low conformal distortion.
The formulation is simple and efficient to optimize and thus can be combined with additional energy terms and various initializations.
The maps are accurate enough for downstream applications, such as shape interpolation and quad mesh transfer.
2. Background: Harmonic Maps and Local Distortion
Suppose are smooth, compact surfaces with or without boundary. Given a map from one into another, a natural task is to measure the distortion of as it is mapped via onto ; this distortion measure eventually will serve as an objective function for optimization problems whose unknown is the correspondence . The basic role of these distortion measures is to evaluate whether nearby points are mapped to nearby points under , at least differentially, a common proxy for the quality of the map.
In the theory of differential geometry, a key distortion measure is the Dirichlet energy (defined below) of ; minimizers of are called harmonic maps. Intuitively, if we think of as a rubber sheet, a harmonic map represents an equilibrium position of the sheet after stretching it over and letting it compress. The Dirichlet energy and its minimizers find many roles in the geometry processing literature, most prominently in surface parameterization [Lévy et al., 2002], due to its intuitive measurement of distortion and connections to notions of conformality. At the same time, theory and practice of harmonic mapping become considerably more challenging when has areas of positive curvature; intuitively these can cause the rubber sheet to slip or bunch, yielding singularities in gradient flow procedures designed to uncover harmonic maps.
In this section, we describe the basic construction of the Dirichlet energy and point out its advantages and flaws in the context of surface-to-surface correspondence; we also provide basic constructions for approximating the Dirichlet energy of a map between discrete surfaces. In §3, we then propose a modified notion of harmonicity designed to avoid singularities and asymmetry in the surface-to-surface correspondence pipeline.
2.1. Smooth Surfaces
where is the map differential and is the volume element of . measures the total stretch of after it is warped onto , as measured by the integrated norm of the Jacobian . Formally, given an orthonormal basis for at , the integrand can be expanded as
where is the metric of .
Existence, uniqueness, and regularity of harmonic maps given assumptions on the geometry/topology of and as well as the homotopy class of is a key theme in the twentieth-century differential geometry literature. A landmark paper by Eells and Sampson  proves existence of a harmonic map in each homotopy class under the assumption that has non-positive curvature. The proof technique in this paper is attractive from a computational perspective: Essentially they start with an arbitrary map in the prescribed homotopy class and use an analog of gradient descent to decrease the Dirichlet energy.
A key drawback of the Eells and Sampson proof technique from a computational perspective, however, highlights an issue with harmonic correspondence in the context of algorithmic mapping between surfaces. In particular, their gradient descent procedure can fail when the target has regions of positive curvature. Roughly, this singular behavior is explained by the fact that the objective is minimized globally by , the constant map! This observation highlights the difference between harmonic mapping and elastic models like the ones proposed in [Sorkine and Alexa, 2007; Chao et al., 2010], which seek
to be close to a rotation matrix rather than to the zero matrix. We will address this issue in our “reversible harmonic” formulation by adding the Dirichlet energy offor the case of diffeomorphic correspondence; this has the added benefit of making forward maps and reverse maps critical points of the same objective function.
2.2. Triangle Meshes
For surfaces that are discretized as triangle meshes , represented by their vertex, edge and face sets , a pointwise vertex map assigns a point on a face of to each vertex of . The extension of the vertex map to the interior of faces of determines the corresponding Dirichlet energy.
If the map is assumed to be affine on every face , then the Dirichlet energy is given by
is the unique linear transformation betweenand its image triangle , and is the area of [Pinkall and Polthier, 1993]. Equivalently, the energy can also be written as:
where is the cotangent weight of the edge . This energy is convex and quadratic in the images of the vertices of , and is therefore straightforward to minimize efficiently when is unrestricted, e.g. for planar parameterization [Lévy et al., 2002].
When is a non-Euclidean space, should be restricted to lie on , leading to a constrained optimization problem that is harder to solve. In addition, a more serious issue is the linearity assumption itself. When does not sample the target surface well, the linear extension of can be far from . In this case, minimizing the Dirichlet energy of the piecewise-affine map can lead to incorrect results.
Consider for example, as in Figure 1, mapping a disk (a) to an enneper surface (b) with Dirichlet boundary conditions; since the target has negative curvature, in the smooth case [Eells and Sampson, 1964] gradient flow will reach a harmonic map . An initial map (c) maps all the interior vertices of to a single interior vertex on , and the boundary of the disk is mapped to the boundary of the enneper. Minimizing Eq. (3) using gradient descent, the analog of Eells & Sampson’s heat flow, with the side constraint that is restricted to lie on , leads to a map (d) that is clearly not smooth. Effectively, Eq. (3) aims to place the image of every vertex of in the Euclidean weighted average of the image of its neighbors. When the affine map samples the target poorly, this strategy fails to generate an approximation of a smooth map.
As shown in Figure 1(e), minimizing this energy instead of the Euclidean one yields a significantly better result at the cost of having to compute geodesic distances. Motivated by this idea, we propose to use this energy as the main building block in a shape mapping algorithm. We reformulate it to allow efficient optimization and combination with other terms that address the case of positively-curved target surfaces , as described in the following section.
3. Reversible Harmonic Maps
We represent a triangle mesh by its vertex, edge and face sets , respectively, where we denote , and its given embedding by . We denote scalar functions
by a vector of coefficients of piecewise linear hat functions, with. The squared norm of a function on the surface is given by , where is the diagonal (lumped) mass matrix of the vertices. The total area of the mesh is denoted by . The squared gradient norm is given by , where is the matrix of cotangent weights. Similarly, for matrices whose columns are scalar functions, we use the matrix trace: , . When two meshes are involved we use a subscript, e.g. is the mass matrix of .
While the geodesic harmonic energy can be effective, harmonic maps in general can become degenerate and map large regions to a single point. Indeed, the map taking all the points on to a single point on is harmonic. An example of such behavior is demonstrated in Figure 2. Here, the source is a sphere with a small number of triangles (a), that is mapped to a high-resolution target sphere . Even if the initial map is the ground-truth map (b) between the spheres, during the optimization the map quickly “slides over” to a single hemisphere of the target sphere (c), and then degenerates and collapses to a single point (d). Intuitively, in the discrete case, we can think of as an elastic fishnet instead of a continuous rubber sheet, stretched over and allowed to compress. Then, in addition to the usual degeneracies in the smooth case, the target surface can effectively “slip through” one of the holes in the net, allowing the map to degenerate to a single point.
To prevent this, we minimize the geodesic harmonic energies of both the forward and backward maps, together with a reversibility constraint relating both maps. As we later demonstrate, this approach is highly effective in generating non-degenerate harmonic maps.
Given two triangle meshes , and maps , the total harmonic energy of both forward and backward maps is given by
where is given in Equation (4).
The reversibility energy prevents the maps from collapsing. In the smooth case, if the reversibility energy is bounded pointwise, it easily follows that the maps are close to being injective and surjective, as we show in the Appendix. For both energies, care is required to handle correctly meshes of different scales, hence the normalization by , the total area of .
Finally, the full energy is given by:
where the parameter controls the trade-off between smoothness and reversibility.
To demonstrate the effect of the different components of the energy we set the parameter to different values. Figure 3 shows the results of this experiment, computing a map between a disk (a) and a folded disk. An initial map maps all the interior vertices of to a single interior vertex on , and the boundary of the disk is mapped to the boundary of the folded disk. In (b,c) we visualize the resulting map by mapping a texture from (a) to the source, and in (d) we show the graphs of the total area of the image (top) and discrete geodesic Dirichlet energy, computed using Eq. (4) and normalized with respect to its value in the first iteration (bottom), during the optimization. Taking models reversibility only, and leads to a non-collapsed map, yet with a high Dirichlet energy (b). On the other hand, taking models harmonicity only, and leads to a map that collapses the image to a single point (the total area is zero in the graph). Finally, taking leads to a balance between the harmonic energy and reversibility, and a good overall map (c). Note that the mapped boundary vertices were not constrained to lie on the boundary of the target shape during the optimization. For all our experiments, we use .
Minimizing the energy in Eq. (7) requires computing the gradient of the geodesic distances with respect to the forward and backward maps, as well as tracing vector fields on the surface, which are both computationally heavy. We therefore apply two approximations to address these issues.
3.2. Energy approximation
Any point can be represented uniquely using its barycentric coordinates with respect to the face it lies on. We denote by the row vector that is zero everywhere except at the vertices of , where we have . In addition, we denote the feasible row set of , i.e. the set of all possible such vectors, by . Finally, the feasible set of all possible precise maps from to is given by , where denotes the -th row of the matrix . Thus, any map can be represented using a matrix , by setting , which, by definition, is in the feasible set . Furthermore, the matrix represents the images of the vertices under the map .
3.2.2. High-dimensional embedding
As we have seen, if the target space is Euclidean then the geodesic distances are Euclidean distances, and the optimization is simple and efficient. Following similar ideas in the literature [Bronstein et al., 2006], we therefore suggest to use a high-dimensional Euclidean embedding as a proxy for fast geodesic distance computation.
Given a mesh , we seek an embedding , for such that the Euclidean distance approximates well the geodesic distance , for all . The literature on such embeddings is quite vast, and we chose to leverage the method suggested by Panozzo et al.  that relies on multidimensional scaling [Cox and Cox, 2000]. Any other embedding method could be used as well, as long as the geodesic distances are well approximated. We took in all our experiments, following Panozzo et al. .
Our goal now is to compute a harmonic map between the high-dimensional Euclidean embeddings. We denote by the matrix whose rows are the embeddings . Rewriting the harmonic and reversibility energies in terms of the high-dimensional embeddings, and in matrix form, leads to:
Note that the weights in the harmonic energy, given now in matrix form in , remain the same for the high-dimensional embedding, since the embedding is nearly isometric.
3.2.3. Half quadratic splitting
While Equation (8) can be minimized using gradient descent, we found, similarly to [Ezuz and Ben-Chen, 2017], that it is more efficient to use the half quadratic splitting optimization method [Geman and Yang, 1995] (see also e.g. [Wang et al., 2008; Zoran and Weiss, 2011]). We introduce auxiliary variables
, which estimate the images of the verticesgiven by . Substituting, the energies are:
for . In addition, we need soft constraints for the auxiliary variables:
The full energy is now:
where controls the accuracy of the auxiliary variables. In practice, an update scheme for is often tailored per application, with the general guideline of increasing as the iterations advance [Wang et al., 2008]. In practice, in all of our experiments, we took where is the optimization iteration number for the first 100 iterations, and then kept the value of constant until convergence.
3.3. The optimization problem
Our optimization problem is now given by:
where is the feasible set of precise maps from to . Despite the two approximations that we used, solving this optimization problem succeeds in decreasing the total discrete Dirichlet energy from Eq. (4) while preventing the map from collapsing, as is illustrated in Figure 4. In addition to the energy, we show the initial map that was created from landmarks as described in Section 4.2, and the intermediate map at a few iterations.
Our optimization problem has block structure, in the sense that if some of the variables are kept fixed it becomes a linear least squares problem. We therefore chose to use block coordinate descent (see e.g. [Xu and Yin, 2013]) as the optimization algorithm.
4.1. Block coordinate descent
In each sub-iteration we solve for one of the matrices , while keeping the others fixed. Since the energy is quadratic in all the variables, every sub-iteration involves a relatively simple optimization problem, with the only complication arising because of the non-convex feasible sets .
Optimizing for .
When are fixed, the optimization problem is a linear least squares minimization of the form , with known matrices and , where is sparse, which we solve using a direct method. The system is highly over-constrained, as even if degenerates, the system is well-conditioned due to the term , as long as the vertex areas of the input mesh do not vanish.
Optimizing for .
When are fixed, the energy has the form , where are known, with the constraints that . Following [Ezuz and Ben-Chen, 2017], the optimization is done by solving for every row of separately. Intuitively, we can think of as a high-dimensional embedding of the faces of , and of as a high-dimensional point cloud. The optimal projects each point in to its closest point on the faces given by . As shown in [Ezuz and Ben-Chen, 2017], this process is guaranteed to find which are globally optimal when are kept fixed.
The alternating descent guarantees that the energy is reduced at every iteration, since the sub-iterations find a global optimum of the reduced optimization problems. In practice, we stopped the optimization when the change of energy was less than , or after a maximum of iterations. In most cases, we have observed convergence of the energy to high precision even when early stopping after iterations was used.
We provide the details of the alternating descent in Algorithm 1.
Our method is general and can receive as input various initial data. Depending on the input, we describe the initialization of the variables . Given a pointwise map its corresponding matrix representation is given by for . Similarly, given a matrix representation , we have that . Therefore, in the following refer to either or according to which notation is more convenient.
Given a pointwise map we approximate an inverse map by taking .
Then, the initialization of is .
The term functional map [Ovsjanikov et al., 2012] denotes a map between scalar functions. It is a linear operator that can be represented using a matrix when scalar functions are represented in a linear basis. Let be a matrix whose columns are basis functions of a subspace of scalar functions on , where each function is piecewise linear and is defined by values assigned to vertices. Given a pointwise map that maps vertices of to points on , the corresponding functional map maps functions on to functions on , represented in the reduced basis. Therefore, given two functional maps and , we initialize . Optimizing does not require initialization. Figure 9 shows results where functional maps were used for initialization.
Given input landmark pairs where and , we first construct a rough initial pointwise map and then use it to initialize the rest of the variables, as previously described. We first compute the geodesic Voronoi diagram on with centers , and then set , where is the geodesic cell corresponding to the center . Note that this initialization is highly degenerate, as all the points on are mapped to the landmarks on , yet it is enough for our needs. In Figure 4 initialization using input landmarks is visualized.
Using the projection step when optimizing for has some limitations. First, as discussed in [Panozzo et al., 2013], such a projection is not smooth. Furthermore, the closest point on the high-dimensional embedding of the triangle mesh might not be unique, therefore the solution of the optimization for might alternate between two configurations with the same energy. Thus, while the energy is guaranteed to converge, we do not have a similar guarantee for the convergence of the solution. In practice, we have not encountered a case where these limitations posed a practical problem. In future work it could be possible to handle the first issue using a Phong projection, as in [Panozzo et al., 2013], and the second issue using an additional regularization that penalizes diverting from the current solution.
The most expensive step in the optimization process is the projection on a triangle mesh for optimizing . However, this procedure is highly parallelizable since the projection of each point is independent of the other points. We used CUDA 8 to implement the projection in parallel, while the rest of the optimization method was written in MATLAB. On a desktop machine with a TITANX GPU and an Intel Core i7 processor, 200 optimization iterations of our method, for shapes with 5K vertices, took around seconds.
To validate our method we have compared with a variety of state-of-the-art mapping techniques, in accordance with the type of input they can accept. In addition, we show applications to shape interpolation and quad mesh transfer.
5.1. Quality metrics
To evaluate the quality of a map we measure its smoothness through its conformal distortion and its semantic accuracy, using the distance to the ground truth, when given. We also use alternative measures, such as symmetry and compatibility with ground truth segmentations, when no dense ground truth map is available.
We use the definition by Hormann and Greiner [Eq. (3)]hormann2000mips for the conformal distortion of a single triangle : , where
are the singular values of the linear transformation which mapsfrom to . We subtract so that the minimal conformal distortion is zero and visualize the result as a cumulative graph showing the percentage of triangles with less than a certain distortion value.
Distance from ground truth.
When a ground truth map is given, we measure the distance from the ground truth using the protocol suggested by Kim et al. [2011, Section 8.2]. For every mapped vertex, we measure its geodesic distance from the ground truth location, relative to the square root of the total area of , and visualize the percent of vertices whose distortion is less than a given value.
Compatibility with segmentations.
For some datasets ground truth labeled segmentations are available. In this case, for every pair of shapes and a given map we measure the consistency of the segmentation with respect to the map. This is done by computing the relative vertex area of vertices that are mapped to a face that belongs to the same segment as the source vertex.
Compatibility with symmetry.
For some datasets a ground truth map is only known for a subset of the points, yet a full intrinsic symmetry can be computed for every shape separately. We assume that a good map should respect the intrinsic symmetries of the source and target shapes, given by , respectively. We therefore use these symmetries as input, and measure the compatibility of the map with the symmetries, given by the geodesic distance . We visualize the result using a cumulative graph, similarly to the ground truth error. To compute the symmetries we use the method by Kim et al. . We also manually filtered the results to use only the accurate symmetries.
5.2. Dataset: SHREC, input: landmarks
We use the BIM benchmark [Kim et al., 2011] that provides more than 200 pairs of highly non-isometric shapes from the SHREC dataset [Giorgi et al., 2007] with user-verified landmarks. We compare our method with a state of the art parameterization based method [Aigerman and Lipman, 2016] (HOT) and the weighted averages method [Panozzo et al., 2013] (WA). Both receive as input landmark points, which are not modified during the optimization, and generate precise maps. In addition we compare to the recent method by Mandad et al.  (VMTP), that similarly gets as input landmark points, yet can modify them during the optimization. Since VMTP requires uniform isotropic meshes, we recursively add edges using the longest edge bisection method to meshes with less than 10K vertices, before applying VMTP. All the methods we compare with produce precise maps, as vertex-to-vertex maps induce high local distortion. As input to our method we also use the user defined landmarks, and extend them to a full initial map as described in Section 4.2. The landmarks are not used after the initialization.
Quantitative results are shown in Figure 5, where we measure conformal distortion, compatibility with symmetries and distance from the ground truth landmarks. Note that our method achieves the best conformal distortion. In addition, the distance from the ground truth landmarks is also improved when compared to the other method which modifies them (VMTP). As shown in section 5.3, the option to modify the input landmarks is valuable when the input is not completely reliable. In terms of compatibility with symmetry, our method is comparable with existing techniques, notably achieving a better ratio of perfect matches with about 15% of the vertices exactly symmetric for our method, where the next best method has less than 10% exactly symmetric vertices. On this dataset ground truth segmentations are also available [Kalogerakis et al., 2010; Chen et al., 2009], and measuring the relative mapped area which is compatible with the segmentations we have HOT: 90.39%, WA: 90.35%, VMTP: 81.8%, our method: 89.62%. Therefore, this measure also demonstrates that our maps are as compatible semantically as existing techniques, while being considerably more conformal.
Qualitative results are shown in Figure 6, where we have selected a subset of pairs to visually show the differences between the maps. In every row we show, from left to right, the target texture, and the results of our method, HOT, WA and VMTP. Figure 7 shows qualitative results of the mapping methods for two pairs of ants, where the pair on the top row is of different genus. Despite the topological difference, our method generates a semantically accurate map.
5.3. Dataset: SHREC quadrupeds, input: noisy landmarks
In many cases, the selection of the landmarks by the user has some variability (see, e.g. [Chen et al., 2012]), and it might be better to treat these landmarks as guidelines rather than exact ground truth. Our approach is compatible with this notion, since our method only uses the landmarks for initialization, and their final location will, in most cases, vary from their initial one. To check the sensitivity of our approach to the landmark locations, we repeated the experiment from Section 5.2 with various landmark modifications. Figure 8 shows the landmark geodesic error of the output maps when starting from the noisy landmarks compared to starting from the original landmarks. We ran the experiment on the “quadrupeds” class (20 pairs) from the SHREC dataset and did the following modifications: (a) moved every landmark randomly to a vertex in its -ring neighborhood, (b) switched between the two eyes, or mapped both eyes on to a single eye on , and (c) mapped both feet on to the same foot on . In addition to the error graph we show some example maps, as well as the input noisy landmarks. As the figure shows, our results are not sensitive to landmark noise, and even a relatively severe modification, such as mapping both feet to the same foot, yields good qualitative and quantitative results.
5.4. Dataset: SHREC two pairs, input: functional map
The functional map [Ovsjanikov et al., 2012] machinery is quite versatile, and allows to compute generalized maps in a variety of cases. Our method can also be used to extract a precise pointwise map from a given functional map. We use the SHREC dataset with its landmark data from the BIM benchmark [Kim et al., 2011], and use the landmarks to compute a functional map using the Wave Kernel Map and the Wave Kernel Signature [Aubry et al., 2011]. Any other recent method for computing functional maps could be used as well. We provide the functional map as input to our approach and the recent map deblurring approach [Ezuz and Ben-Chen, 2017] (DND), which is the only other method that recovers precise maps from a functional map. Specifically, we used the consistency extension of DND with . Figure 9 qualitatively visualizes the difference between the methods for two pairs of shapes. Note the map improvement on the handle of the cup and the legs of the cow. We also show graphs of the conformal distortion and ground-truth error of the landmarks.
5.5. Dataset: caricatures, input: landmarks
One of the advantages of our formulation is its simplicity, that leads to flexibility in adding additional components to the energy. For example, in some cases it can be beneficial to add weak landmark constraints, to encourage feature points to remain in the neighborhood of the input landmarks.
Weak landmark constraints.
Given pairs of matching landmarks , we add the following term to the energy:
The value of depends on the reliability of the landmarks, if the landmarks are not accurate then should be small. To demonstrate the effectiveness of this approach, we used as input the Homer and Max Planck models, and caricatures of these models generated using the method by Sela et al. . The caricatures have the same triangulation as the original shapes. We remesh the caricatures to avoid bias, and project the original caricature to the remeshed model to generate the ground truth for validation. As input to all approaches, we picked 28 and 14 landmarks from the input map for the homer and Max models respectively. For our algorithm we added the energy in Equation (13) with . The qualitative and quantitative results are shown in figures 10 and 11, respectively. As in previous experiments, we achieve considerably better conformal distortion, with comparable ground truth error.
5.6. Application: Shape Interpolation
Existing shape interpolation methods require the input shapes to share the same connectivity, while real data rarely satisfies this requirement. Our mapping can be used to remesh the target surface using the image of the vertices of , given by , and the connectivity of . We used our map between two birds from SHREC to demonstrate this application, starting from the BIM landmarks as described in section 5.2. After remeshing , as a few faces had a zero area, we iteratively moved vertices of the degenerate faces to an average of their 1-ring neighbors until there were no degenerate faces. We then used the shape interpolation method by Heeren et al.  to interpolate between the shapes, as shown in Figure 12. Note that we correctly mapped the wings, head and tail of the birds, as is evident from the natural interpolation results.
5.7. Application: Quad Mesh Transfer
Finally, we demonstrate a potential application of our correspondence to quad mesh transfer of artist-generated quad meshes to scanned meshes. In this experiment, we start from a set of 41 landmark points, and use weak landmark constraints with . We choose to have a larger number of landmarks in this example to preserve the fine features such as fingernails and joints. The results are shown in Figure 13, with two views of the input and output quad meshes on the left and right, respectively. Note, that the edge flow of the output quad mesh closely follows the features of the hand, and the special structures in the input, such as the fingernails and knuckles, are nicely preserved in the output mesh. Such a high quality transfer can only be achieved if the computed map has a low conformal distortion, which leads to well preserved quad shapes.
We have suggested a novel correspondence method between non-isometric shapes, which considerably improves conformal distortion over existing techniques while preserving semantic features. Our approach is based on an alternating minimization of a quadratic energy, which is efficient, easy to implement, and flexible. In addition to demonstrating effectiveness on various benchmarks, we have shown applications to quad mesh transfer and shape interpolation.
Beyond its current utility, our formulation can serve as a framework for more involved energies on the differential of the map. For example, one can consider various metrics, such as non-isotropic, higher order, sparsity based and data-driven. We hope that our approach can be a stepping stone to generalizing the large body of literature on planar shape parameterization to the more general setting of shape correspondence.
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