Geometric modeling in shape space
Abstract
We present a novel framework to treat shapes in the setting of Riemannian geometry. Shapes -- triangular meshes or more generally straight line graphs in Euclidean space -- are treated as points in a shape space. We introduce useful Riemannian metrics in this space to aid the user in design and modeling tasks, especially to explore the space of (approximately) isometric deformations of a given shape. Much of the work relies on an efficient algorithm to compute geodesics in shape spaces; to this end, we present a multi-resolution framework to solve the interpolation problem -- which amounts to solving a boundary value problem -- as well as the extrapolation problem -- an initial value problem -- in shape space. Based on these two operations, several classical concepts like parallel transport and the exponential map can be used in shape space to solve various geometric modeling and geometry processing tasks. Applications include shape morphing, shape deformation, deformation transfer, and intuitive shape exploration.
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A deformation of a shape represented by a triangular mesh is isometric if and only if all edge lengths are preserved during the deformation.
“A deformation of a shape represented by a triangular mesh is isometric if and only if all edge lengths are preserved during the deformation [9].”
From MORPHING OF TRIANGULAR MESHES IN SHAPE SPACE (2010) · cites this paper as [9]
The newly presented approaches solve the morphing problem without needing to solve a minimization problem.
“All of the newly presented approaches solve the morphing problem without the need to solve a minimization problem.”
From MORPHING OF TRIANGULAR MESHES IN SHAPE SPACE (2010) · cites this paper as [9]
Kilian et al. [9] used shape space representations to guide morphs and other deformations between triangular meshes, modeling deformations as geodesics in shape space found using an energy-minimization approach.
“Recently, Kilian et al. [9] used shape space representations to guide morphs and other more general deformations between shapes represented as triangular meshes. Each shape is represented by a point in a high-dimensional shape space and deformations are modeled as geodesics in shape space. The geodesic paths in shape space are found using an energy-minimization approach.”
From MORPHING OF TRIANGULAR MESHES IN SHAPE SPACE (2010) · cites this paper as [9]
The results of the averaging algorithm depend heavily on the initial rigid alignment of the shapes.
“Just as in the algorithm by Kilian et al. [9], the results depend heavily on the initial rigid alignment of the shapes.”
From MORPHING OF TRIANGULAR MESHES IN SHAPE SPACE (2010) · cites this paper as [9]
Cites
SCAPE: shape completion and animation of people
Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., & Davis, J. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073207 · Cited by 1,489 · see citing works
The space of human body shapes: reconstruction and parameterization from range scans
Allen, B., Curless, B., & Popović, Z. (2003) · ACM SIGGRAPH 2003 Papers · doi:10.1145/1201775.882311 · Cited by 913 · see citing works
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Shape Analysis of Elastic Curves in Euclidean Spaces
Srivastava, A., Klassen, E., Joshi, S. H., & Jermyn, I. H. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.184 · Cited by 683 · see citing works
Optimization Methods on Riemannian Manifolds and Their Application to Shape Space
Ring, W., & Wirth, B. (2012) · SIAM Journal on Optimization · doi:10.1137/11082885x · Cited by 342 · see citing works
“introduced a Riemannian shape space in [13], in which shapes are represented by meshes with fixed connectivity.”
BackgroundA simple geometric model for elastic deformations
Chao, I., Pinkall, U., Sanan, P., & Schröder, P. (2010) · ACM Transactions on Graphics · doi:10.1145/1833349.1778775 · Cited by 307
One Point Isometric Matching with the Heat Kernel
Ovsjanikov, M., Mérigot, Q., Mémoli, F., & Guibas, L. (2010) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2010.01764.x · Cited by 301 · see citing works
“High quality correspondences are also essential in morphing, and shape interpolation [KMP07].”
Non‐Rigid Registration Under Isometric Deformations
Huang, Q., Adams, B., Wicke, M., & Guibas, L. J. (2008) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2008.01285.x · Cited by 279 · see citing works
3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splatting
Qian, Z., Wang, S., Mihajlovic, M., Geiger, A., & Tang, S. (2023) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52733.2024.00480 · Cited by 279 · see citing works
“Inspired by [44], we leverage the as-isometric-as-possible constraint [15] to restrict neighboring 3D Gaussian centers to preserve a similar distance after deformation.”
MethodExample-based elastic materials
Martin, S., Thomaszewski, B., Grinspun, E., & Gross, M. (2011) · ACM Transactions on Graphics · doi:10.1145/1964921.1964967 · Cited by 250 · see citing works
“…meshes [Sheffer and Kraevoy 2004; Sorkine et al. 2004; Lipman et al. 2005; Botsch et al. 2006], interpolating between meshes [Alexa et al. 2000; Kilian et al. 2007; Winkler et al. 2010; Chao et al. 2010] and for transferring deformations from one mesh to another [Sumner and Popović 2004;…”
BackgroundMethodInfluentialKilian, M., Flöry, S., Chen, Z.-G., Mitra, N., Sheffer, A., & Pottmann, H. (2008) · ACM Transactions on Graphics · doi:10.1145/1399504.1360674 · Cited by 219
“• Combining digital reconstruction, optimization and recently introduced algorithms for computing in shape space [Kilian et al. 2007] we have a rich toolbox for geometry processing with curved folds.”
MethodInfluentialSelf-citationAnimating deformable objects using sparse spacetime constraints
Schulz, C., von Tycowicz, C., Seidel, H., & Hildebrandt, K. (2014) · ACM Transactions on Graphics · doi:10.1145/2601097.2601156 · Cited by 205
Structure-aware shape processing
Mitra, N., Wand, M., Zhang, H., Cohen-Or, D., Kim, V. G., & Huang, Q.-X. (2013) · Eurographics · doi:10.1145/2542266.2542267 · Cited by 188 · see citing works
“In our setting, the objects have only one part, with a number of corresponding parameters essentially representing a shape space (e.g., [KMP07]).”
BackgroundSelf-citationMap-based exploration of intrinsic shape differences and variability
Rustamov, R., Ovsjanikov, M., Azencot, O., Ben-Chen, M., Chazal, F., & Guibas, L. (2013) · ACM Transactions on Graphics · doi:10.1145/2461912.2461959 · Cited by 166 · see citing works
“Third, linear methods such as PCA are most often used — a notable exception being [Kilian et al. 2007] — even when it is not clear that a flat approximation to the shape space, either locally or globally is indicated.”
MethodInformative Descriptor Preservation via Commutativity for Shape Matching
Nogneng, D., & Ovsjanikov, M. (2017) · Computer graphics forum (Print) · doi:10.1111/cgf.13124 · Cited by 161 · see citing works
Style-content separation by anisotropic part scales
Xu, K., Li, H.-H., Zhang, H., Cohen-Or, D., Xiong, Y.-S., & Cheng, Z.-Q. (2010) · ACM Transactions on Graphics · doi:10.1145/1866158.1866206 · Cited by 154 · see citing works
“2005] or other creatures [Praun et al. 2001; Kilian et al. 2007].”
BackgroundLearning Smooth Neural Functions via Lipschitz Regularization
Liu, H., Williams, F., Jacobson, A., Fidler, S., & Litany, O. (2022) · International Conference on Computer Graphics and Interactive Techniques · doi:10.1145/3528233.3530713 · Cited by 148
“Shape interpolation is a fundamental task and several classic meth-ods exist, such as [Solomon et al. 2015] and [Kilian et al. 2007].”
BackgroundExploration of continuous variability in collections of 3D shapes
Ovsjanikov, M., Li, W., Guibas, L., & Mitra, N. (2011) · ACM Transactions on Graphics · doi:10.1145/1964921.1964928 · Cited by 143 · see citing works
Self-citationData‐Driven Shape Analysis and Processing
Xu, K., Kim, V. G., Qi-xing Huang, A., & Kalogerakis, E. (2016) · Computer Graphics Forum · doi:10.1111/cgf.12790 · Cited by 46 · see citing works
Self-citationExample‐Driven Deformations Based on Discrete Shells
Fröhlich, S., & Botsch, M. (2011) · Computer graphics forum (Print) · doi:10.1111/j.1467-8659.2011.01974.x · Cited by 118
Generating and exploring good building layouts
Bao, F., Yan, D.-M., Mitra, N., & Wonka, P. (2013) · ACM Transactions on Graphics · doi:10.1145/2461912.2461977 · Cited by 115
Self-citationElastic Shape Matching of Parameterized Surfaces Using Square Root Normal Fields
Jermyn, I. H., Kurtek, S., Klassen, E., & Srivastava, A. (2012) · European Conference on Computer Vision · doi:10.1007/978-3-642-33715-4_58 · Cited by 114
“Another paper by Kilian et al. [18] represents parameterized surfaces by discrete triangulated meshes, assumes a Riemannian metric on the space of such meshes, and computes geodesic paths between given meshes.”
BackgroundLie Bodies: A Manifold Representation of 3D Human Shape
Freifeld, O., & Black, M. J. (2012) · European Conference on Computer Vision · doi:10.1007/978-3-642-33718-5_1 · Cited by 111 · see citing works
“Note that since we provide closed-form formulas for geodesic paths, interpolation (or extrapolation) on our manifold is considerably simpler than the algorithms presented in [21].”
BackgroundMethodElastic Geodesic Paths in Shape Space of Parameterized Surfaces
Kurtek, S., Klassen, E. P., Gore, J. C., Ding, Z., & Srivastava, A. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.233 · Cited by 90 · see citing works
“Kilian et al. [21] presented a technique for computing geodesics between triangulated meshes (discretized surfaces) but at their given parameterizations.”
BackgroundNeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go
Eisenberger, M., Novotný, D., Kerchenbaum, G., Labatut, P., Neverova, N., Cremers, D., & Vedaldi, A. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00739 · Cited by 74 · see citing works
“This multi-scale optimization strategy, which is motivated by classical nonlearning interpolation algorithms [27, 23], leads to an overall faster and more robust convergence.”
BackgroundMethodMelzi, S., Ren, J., Rodolà, E., Sharma, A., Wonka, P., & Ovsjanikov, M. (2019) · ACM Transactions on Graphics · doi:10.1145/3355089.3356524 · Cited by 181 · see citing works
“Shape matching is a task that occurs in countless applications in computer graphics, including shape interpolation [Kilian et al. 2007], statistical shape analysis [Bogo et al.”
MethodPosition-based tensegrity design
Pietroni, N., Tarini, M., Vaxman, A., Panozzo, D., & Cignoni, P. (2017) · ACM Transactions on Graphics · doi:10.1145/3130800.3130809 · Cited by 102
Smooth Rotation Enhanced As-Rigid-As-Possible Mesh Animation
Levi, Z., & Gotsman, C. (2015) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2359463 · Cited by 102
Shape space exploration of constrained meshes
Yang, Y.-L., Yang, Y.-J., Pottmann, H., & Mitra, N. (2011) · ACM Transactions on Graphics · doi:10.1145/2024156.2024158 · Cited by 100
“Even seemingly simple handle-driven deformations restricted to such shape spaces turn out to be challenging, and remain an active topic of research (see [Botsch et al. 2006; Kilian et al. 2007; Botsch and Sorkine 2008; Gal et al. 2009]).”
BackgroundSelf-citationSundaramoorthi, G., Mennucci, A., Soatto, S., & Yezzi, A. (2011) · SIAM Journal of Imaging Sciences · doi:10.1137/090781139 · Cited by 98 · see citing works
“It must be noted that, one sure way to avoid infinite-dimensional Riemannian geometry (and the pathologies associated with some metrics) is to model the shape space as a finite dimensional manifold: such is the case for example in the work of Kendall [24] (some recent works include, e.g. [26] [3]).”
BackgroundInteractive 3D Modeling with a Generative Adversarial Network
Liu, J., Yu, F., & Funkhouser, T. (2017) · International Conference on 3D Vision · doi:10.1109/3dv.2017.00024 · Cited by 96 · see citing works
“For example, [Kilian et al. 2007], [Allen et al. 2003], and others describe methods for interpolation between surfaces with consistent parameterizations.”
MethodShape Decomposition using Modal Analysis
Huang, Q.-X., Wicke, M., Adams, B., & Guibas, L. (2009) · Computer graphics forum (Print) · doi:10.1111/j.1467-8659.2009.01380.x · Cited by 95 · see citing works
“Other shell energies, such as a thin-shell energy [GHDS03], or an energy favoring isometric deformations [KMP07] can be used as well, with similar results.”
ResultA novel riemannian framework for shape analysis of 3D objects
Kurtek, S., Klassen, E., Ding, Z., & Srivastava, A. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539778 · Cited by 93
“One exception is Kilian et al. [8] which represents parameterized 1625978-1-4244-6985-7/10/$26.00 ©2010 IEEE surfaces by discrete triangulated meshes, assumes a Riemannian metric on the space of such meshes, and computes geodesic paths between given meshes.”
BackgroundInfluentialMulti‐Scale Geometry Interpolation
Winkler, T., Drieseberg, J., Alexa, M., & Hormann, K. (2010) · Computer graphics forum (Print) · doi:10.1111/j.1467-8659.2009.01600.x · Cited by 93
“For example, approaches like [ 23, 29, 14 ] can be used on top of our method, and it can also be used for shape editing based on user-defined constraints, similar to how it is described in [ 15 ] .”
BackgroundMethodResultInfluentialTime‐Discrete Geodesics in the Space of Shells
Heeren, B., Rumpf, M., Wardetzky, M., & Wirth, B. (2012) · Computer graphics forum (Print) · doi:10.1111/j.1467-8659.2012.03180.x · Cited by 92 · see citing works
A Continuum Mechanical Approach to Geodesics in Shape Space
Wirth, B., Bar, L., Rumpf, M., & Sapiro, G. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0416-9 · Cited by 84 · see citing works
“Miller and Younes (2001) generalized this concept to the space of images and also examined the more general case of computing geodesics when a group acts on a Riemannian manifold so that this Riemannian manifold can be endowed with a new metric which incorporates also the group action (so-called metamorphosis, Trouvé…”
BackgroundWrinkling Captured Garments Using Space‐Time Data‐Driven Deformation
Popa, T., Zhou, Q.-J., Bradley, D., Kraevoy, V., Fu, H.-B., Sheffer, A., & Heidrich, W. (2009) · Computer graphics forum (Print) · doi:10.1111/j.1467-8659.2009.01382.x · Cited by 83
Interactive Shape Interpolation through Controllable Dynamic Deformation
Huang, J., Tong, Y., Zhou, K., Bao, H., & Desbrun, M. (2011) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2010.109 · Cited by 82
“Geometric modeling in " shape space " is another purely geometric approach to shape interpolation, where a notion of geodesics in shape space is used to provide an as-isometric-as-possible path between two poses [3].”
Sparse Data Driven Mesh Deformation
Gao, L., Lai, Y.-K., Yang, J., Zhang, L.-X., Xia, S., & Kobbelt, L. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2941200 · Cited by 81 · see citing works
Topology-Invariant Similarity of Nonrigid Shapes
Bronstein, A., Bronstein, M., & Kimmel, R. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-008-0172-2 · Cited by 78 · see citing works
Landmark‐Guided Elastic Shape Analysis of Spherically‐Parameterized Surfaces
Kurtek, S., Srivastava, A., Klassen, E., & Laga, H. (2013) · Computer graphics forum (Print) · doi:10.1111/cgf.12063 · Cited by 75 · see citing works
“On the other hand, assuming the availability of a dense correspondence, as in [KMP06,HRWW12], is not practical for large and complex shapes.”
BackgroundMethodResultInfluentialOn Discrete Killing Vector Fields and Patterns on Surfaces
Ben-Chen, M., Butscher, A., Solomon, J., & Guibas, L. (2010) · Computer graphics forum (Print) · doi:10.1111/j.1467-8659.2010.01779.x · Cited by 71 · see citing works
Real-Time Nonlinear Shape Interpolation
von Tycowicz, C., Schulz, C., Seidel, H., & Hildebrandt, K. (2015) · ACM Transactions on Graphics · doi:10.1145/2729972 · Cited by 68 · see citing works
“Such metrics measure the metric distortion or viscous dissipation required to physically deform an elastic object [Kilian et al. 2007; Chao et al. 2010; Wirth et al. 2011; Heeren et al. 2012].”
BackgroundSupervised Descriptor Learning for Non-Rigid Shape Matching
Corman, E., Ovsjanikov, M., & Chambolle, A. (2014) · ECCV Workshops · doi:10.1007/978-3-319-16220-1_20 · Cited by 67 · see citing works
“Finding high quality correspondences is a key component in many tasks including statistical shape analysis [12], deformation transfer [30] and interpolation (morphing) [16] among others.”
BackgroundIsometry‐Aware Preconditioning for Mesh Parameterization
Claici, S., Bessmeltsev, M., Schaefer, S., & Solomon, J. (2017) · Computer graphics forum (Print) · doi:10.1111/cgf.13243 · Cited by 66 · see citing works
“In computer graphics, this approach appears in work on shape interpolation including [KMP07], which defines Riemannian metrics for triangle stretching in shape interpolation, and [HRWW12], which interpolates between surfaces using a metric inspired by thin-shell deformation.”
BackgroundMethodString Actuated Curved Folded Surfaces
Kilian, M., Monszpart, Á., & Mitra, N. (2017) · TOGS · doi:10.1145/3072959.3015460 · Cited by 66
“In other efforts, isometric deformations have been investigated as geodesics in appropriate shape spaces [Kilian et al. 2007; Wirth et al. 2011], or in the context of bending energies in the PriMo setup [Botsch et al. 2006].”
BackgroundSelf-citationSplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction
Mihajlovic, M., Prokudin, S., Tang, S., Maier, R., Bogo, F., Tung, T., & Boyer, E. (2024) · European Conference on Computer Vision · doi:10.48550/arxiv.2409.11211 · Cited by 65
“Although regularizations such as the enforcement of local rigidity and isometry losses [30,56] help stabilize the learning process to a certain extent, the overall pipeline still requires a large number of input views for each step and struggles to reconstruct scenes faithfully from sparse…”
BackgroundImproved Functional Mappings via Product Preservation
Nogneng, D., Melzi, S., Rodolà, E., Castellani, U., Bronstein, M., & Ovsjanikov, M. (2018) · Computer graphics forum (Print) · doi:10.1111/cgf.13352 · Cited by 64 · see citing works
“Shape correspondence is one of the key problems in digital geometry processing with applications in fields ranging from manufacturing to shape morphing [KMP07], statistical shape analysis [HSS ∗ 09,BRLB14], texture mapping, animation and deformation transfer [SP04] among a wide variety of others.”
BackgroundExploring the Geometry of the Space of Shells
Heeren, B., Rumpf, M., Schröder, P., Wardetzky, M., & Wirth, B. (2014) · Computer graphics forum (Print) · doi:10.1111/cgf.12450 · Cited by 63
Skeleton‐based Variational Mesh Deformations
Yoshizawa, S., Belyaev, A. G., & Seidel, H.-P. (2007) · Computer graphics forum (Print) · doi:10.1111/j.1467-8659.2007.01047.x · Cited by 63 · see citing works
Variational time discretization of geodesic calculus
Rumpf, M., & Wirth, B. (2012) · doi:10.1093/imanum/dru027 · Cited by 62
“motivates our work. There is a rich diversity of Riemannian shape spaces in the literature. While some of them are finite-dimensional and represent shapes as polygonal curves or triangulated surfaces [13,17], most deal with infinite-dimensional shapes such as planar curves with curvature-based or Sobolev-type metrics [1…”
BackgroundA generalized model for optimal transport of images including dissipation and density modulation
Maas, J., Rumpf, M., Schönlieb, C., & Simon, S. (2015) · ESAIM Mathematical Modelling and Numerical Analysis · doi:10.1051/m2an/2015043 · Cited by 60 · see citing works
“These concepts have a continuously increasing impact on the development of novel computational tools in computer vision and imaging, ranging from shape morphing and modeling [33], and shape statistics, e.”
Nonparametric Regression between General Riemannian Manifolds
Steinke, F., Hein, M., & Schölkopf, B. (2010) · SIAM Journal of Imaging Sciences · doi:10.1137/080744189 · Cited by 57 · see citing works
“More abstractly, it is often useful in image processing to study whole sets of images themselves as Riemannian manifolds [42], or similarly in shape processing, to define a Riemannian structure on the set of all shapes [19, 9].”
BackgroundARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators
Huang, Q.-X., Huang, X., Sun, B., Zhang, Z., Jiang, J.-F., & Bajaj, C. (2021) · IEEE International Conference on Computer Vision · doi:10.1109/iccv48922.2021.00576 · Cited by 55 · see citing works
“Finally, ARAPReg is relevant to early works on modeling tangent spaces of shape manifolds [23, 20, 58].”
Interactive physically-based shape editing
Mezger, J., Thomaszewski, B., Pabst, S., & Strasser, W. (2008) · Symposium on Solid and Physical Modeling · doi:10.1145/1364901.1364915 · Cited by 55
Interactive design exploration for constrained meshes
Deng, B., Bouaziz, S., Deuss, M., Kaspar, A., Schwartzburg, Y., & Pauly, M. (2015) · Comput. Aided Des. · doi:10.1016/j.cad.2014.01.004 · Cited by 53
“[24] represented a triangle mesh as a point in a high-dimensional space that treats each vertex coordinate as a free variable.”
BackgroundTransformations Based on Continuous Piecewise-Affine Velocity Fields
Freifeld, O., Hauberg, S., Batmanghelich, K., & Fisher, J. W. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2646685 · Cited by 52 · see citing works
“, in medical imaging [16], [31], robotics [32], [33], [34], geometric modeling [35] and fluid dynamics [36].”
BackgroundPlanar shape interpolation with bounded distortion
Chen, R.-J., Weber, O., Keren, D., & Ben-Chen, M. (2013) · ACM Transactions on Graphics · doi:10.1145/2461912.2461983 · Cited by 52
“The same drawback holds for the shape space method in [Kilian et al. 2007] where an additional concern is computational efficiency, due to the involved non-linear and nonconvex optimization method.”
MethodEfficient simulation of example-based materials
Schumacher, C., Thomaszewski, B., Coros, S., Martin, S., Sumner, R., & Gross, M. (2012) · Symposium on Computer Animation · doi:10.2312/sca/sca12/001-008 · Cited by 52
“Of the numerous solutions that have been proposed (see, e.g., [ACOL00, LSLCO05, KMP07]), our work is closest related to methods that interpolate in deformation or strain space: the scheme described by Martin et al. [MTGG11] interpolates between volumetric solids using the Green strain; Winkler et…”
MethodNumerical Inversion of SRNF Maps for Elastic Shape Analysis of Genus-Zero Surfaces
Laga, H., Xie, Q., Jermyn, I. H., & Srivastava, A. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2647596 · Cited by 51 · see citing works
“[8] represent surfaces by discrete triangulated meshes and compute geodesic paths (deformations) between them, while assuming that the meshes are registered.”
BackgroundInteractive spacetime control of deformable objects
Hildebrandt, K., Schulz, C., von Tycowicz, C., & Polthier, K. (2012) · ACM Transactions on Graphics · doi:10.1145/2185520.2185567 · Cited by 51
“Efficient multiresolution solvers for computing geodesics in shape spaces were proposed by Kilian et al. [2007] and by Wirth et al.”
MethodRecent Trends in 3D Reconstruction of General Non‐Rigid Scenes
Yunus, R., Lenssen, J. E., Niemeyer, M., Liao, Y., Rupprecht, C., Theobalt, C., Pons‐Moll, G., Huang, J., Golyanik, V., & Ilg, E. (2024) · Computer Graphics Forum · doi:10.1111/cgf.15062 · Cited by 35 · see citing works
“…non-rigid deformations, e.g. of a skirt, where traditional linear blend skinning-based models fail by incorporating regularization techniques such as as-isometric-as-possible [KMP07, HAWG08] and additionally supervising for highly dynamic and complex deformations with keypoint correspondences.”
MethodHartman, E., Sukurdeep, Y., Klassen, E., Charon, N., & Bauer, M. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01743-0 · Cited by 50 · see citing works
“There exists a plethora of different numerical frameworks for shape analysis of parametrized surfaces, see e.g. Jermyn et al. (2017), Kilian et al. (2007), Rumpf and Wirth (2015a), Iglesias et al. (2018) and Pierson et al. (2022) and the references therein.”
BackgroundMethodInfluentialParaGlide: Interactive Parameter Space Partitioning for Computer Simulations
Bergner, S., Sedlmair, M., Moeller, T., Abdolyousefi, S. N., & Saad, A. (2011) · IEEE Transactions on Visualization and Computer Graphics · doi:10.48550/arxiv.1110.5181 · Cited by 48
“Dimension reduction: Instead of arranging points in a circle or another prescribed shape, it is also possible to place them in a data adaptive way using dimension reduction techniques. Kilian et al. [21] use a distance preserving embedding of points to represent shape descriptors to control different designs of shapes…”
HAHA: Highly Articulated Gaussian Human Avatars with Textured Mesh Prior
Svitov, D., Morerio, P., Agapito, L., & Bue, A. D. (2024) · Asian Conference on Computer Vision · doi:10.48550/arxiv.2404.01053 · Cited by 47
“The authors also applied as-isometric-as-possible regularization [32] to the Gaussians to preserve geometric consistency.”
MethodConformal mesh deformations with Möbius transformations
Vaxman, A., Müller, C., & Weber, O. (2015) · ACM Transactions on Graphics · doi:10.1145/2766915 · Cited by 46
“Shape-space based methods, such as [Kilian et al. 2007], navigate the space of possible deformations between two shapes, by finding geodesics of a deformation metric.”
MethodAdjoint Map Representation for Shape Analysis and Matching
Huang, R., & Ovsjanikov, M. (2017) · Computer graphics forum (Print) · doi:10.1111/cgf.13253 · Cited by 44 · see citing works
PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions
Mo, K., Wang, H., Yan, X., & Guibas, L. J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58539-6_41 · Cited by 33 · see citing works
“nd correspondence-based shape retrieval [15,14,74]. From the perspective of graphics design, 3D object creation involves both coarse-level structural design as well as ne-grained geometric generation [39,54,30]. Specically, structural design choices re ect the discrete (e.g. number of chair legs to support the seat) a…”
MethodScale Invariant Geometry for Nonrigid Shapes
Aflalo, Y., Kimmel, R., & Raviv, D. (2013) · SIAM Journal of Imaging Sciences · doi:10.1137/120888107 · Cited by 41 · see citing works
“Once correspondence between two objects is achieved, the same measures can then be used to deform, morph, or warp one shape into another [31].”
BackgroundThe Shape Space of 3D Botanical Tree Models
Wang, G., Laga, H., Xie, N., Jia, J.-Y., & Tabia, H. (2018) · ACM Transactions on Graphics · doi:10.1145/3144456 · Cited by 40 · see citing works
Prokudin, S., Ma, Q., Raafat, M., Valentin, J. P. C., & Tang, S.-Y. (2023) · IEEE International Conference on Computer Vision · doi:10.1109/iccv51070.2023.00732 · Cited by 39 · see citing works
“However, this technique can be complementary to our basic pipeline in case of the reconstruction from unoriented, noisy point clouds, as well as when a water-tight mesh reconstruction is required.”
BackgroundEmbedding shapes with Green’s functions for global shape matching
Burghard, O., Dieckmann, A., & Klein, R. (2017) · Computers & Graphics · doi:10.1016/j.cag.2017.06.004 · Cited by 34 · see citing works
A New Riemannian Setting for Surface Registration
Bauer, M., & Bruveris, M. (2011) · doi:10.48550/arxiv.1106.0620 · Cited by 38
“They include a metric that preservers local rigidity [11], a generalization of the elastic metric for curves to higher dimensions [18, 13], a metric inspired by a continuum mechanics which is defined in the interior of a two dimensional in ria -0 06 24 21 0, v er si on 1 16 S ep 2 01 1 Author manuscript, published in…”
BackgroundThe shape space of discrete orthogonal geodesic nets
Rabinovich, M., Hoffmann, T., & Sorkine-Hornung, O. (2018) · ACM Transactions on Graphics · doi:10.1145/3272127.3275088 · Cited by 37
Time-Dependent Flow seen through Approximate Observer Killing Fields
Hadwiger, M., Mlejnek, M., Theußl, T., & Rautek, P. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2864839 · Cited by 36
“We have already mentioned the concept of Killing fields [37] and approximate Killing fields [6, 28].”
BackgroundMethodTime Discrete Geodesic Paths in the Space of Images
Berkels, B., Effland, A., & Rumpf, M. (2015) · SIAM Journal of Imaging Sciences · doi:10.1137/140970719 · Cited by 36 · see citing works
“[KMP07], and shape statistics, e.g .”
BackgroundDAG amendment for inverse control of parametric shapes
Michel, É., & Boubekeur, T. (2021) · ACM Transactions on Graphics · doi:10.1145/3476576.3476760 · Cited by 35 · see citing works
“We will see in Section 4 the need to identify points across multiple meshes of potentially varying connectivity, which is commonly referred to as shape correspondence or cross-parameterization [Kilian et al. 2007; Kraevoy and Sheffer 2004; Schreiner et al. 2004].”
BackgroundSpaces in the Brain: From Neurons to Meanings
Balkenius, C., & Gärdenfors, P. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01820 · Cited by 35 · see citing works
“…little is known about how shapes are represented in the brain, work in mathematics (Kendall, 1984) and computer graphics (Blanz and Vetter, 1999; Kilian et al., 2007) show that it is possible to design shape spaces where different shapes can be synthesized from combinations of basic shapes in a…”
BackgroundA Data‐Driven Approach to Realistic Shape Morphing
Gao, L., Lai, Y.-K., Huang, Q.-X., & Hu, S.-M. (2013) · Computer graphics forum (Print) · doi:10.1111/cgf.12065 · Cited by 34 · see citing works
“Killian et al. [KMP07] treat shapes as points on the Riemannian surfaces derived from typical rigid or isometric transformations, and convert the plausible morphing problem to finding a shortest path in the shape space which can be solved using a variational approach.”
BackgroundStructure-aware shape processing
Mitra, N., Wand, M., Zhang, H., Cohen-Or, D., Kim, V. G., & Huang, Q.-X. (2014) · International Conference on Computer Graphics and Interactive Techniques · doi:10.1145/2614028.2615401 · Cited by 34
Self-citationGeometric Flows of Curves in Shape Space for Processing Motion of Deformable Objects
Brandt, C., von Tycowicz, C., & Hildebrandt, K. (2016) · Computer graphics forum (Print) · doi:10.1111/cgf.12832 · Cited by 33
“Riemannian metrics on the shape space of triangular surface meshes (with a fixed connectivity) have been introduced by Kilian et al. [KMP07].”
BackgroundMethodInfluentialCashman, T., & Hormann, K. (2012) · Computer graphics forum (Print) · doi:10.1111/j.1467-8659.2012.03032.x · Cited by 31 · see citing works
“More generally, we can explore meaningful poses by interactively navigating this pose space and even create new animations from scratch (Section 4.4), in a similar way to the systems Kilian et al. [KMP07] and Yang et al. [YYPM11] propose for shape-space exploration.”
BackgroundInfluentialRock, Paper, and Scissors: extrinsic vs. intrinsic similarity of non-rigid shapes
Bronstein, A., Bronstein, M., & Kimmel, R. (2007) · IEEE International Conference on Computer Vision · doi:10.1109/iccv.2007.4409076 · Cited by 31 · see citing works
“, morphing and animation problems), in a way similar to [10].”
ResultSuitable Spaces for Shape Optimization
Welker, K. (2017) · Applied Mathematics and Optimization · doi:10.1007/s00245-021-09788-2 · Cited by 30
Shell PCA: Statistical Shape Modelling in Shell Space
Zhang, C., Heeren, B., Rumpf, M., & Smith, W. (2015) · IEEE International Conference on Computer Vision · doi:10.1109/iccv.2015.195 · Cited by 30
“[24] model the space of triangulated shapes.”
MethodSrivastava, A., Turaga, P. K., & Kurtek, S. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.03.006 · Cited by 30 · see citing works
“[69] presented a technique for computing geodesics between triangulated meshes (discretized surfaces) but at their given parameterizations.”
MethodData‐Driven Shape Interpolation and Morphing Editing
Gao, L., Chen, S.-Y., Lai, Y.-K., & Xia, S. (2017) · Computer graphics forum (Print) · doi:10.1111/cgf.12991 · Cited by 28 · see citing works
“Killian et al. [KMP07] consider shapes as points on Riemannian surfaces derived from typical transformations (e.g. isometric), and formulate plausible morphing as finding a shortest path in the shape space.”
BackgroundSplines in the Space of Shells
Heeren, B., Rumpf, M., Schröder, P., Wardetzky, M., & Wirth, B. (2016) · Computer graphics forum (Print) · doi:10.1111/cgf.12968 · Cited by 28
Baek, S. S.-Y., Lim, J., & Lee, K. (2015) · Computers & graphics · doi:10.1016/j.cag.2014.09.025 · Cited by 28
Cho, Y.-S., Seong, J., Shin, S.-Y., Jeong, Y., Kim, J. H., Qiu, A., Im, K., Lee, J., & Na, D. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.05.066 · Cited by 27 · see citing works
“In the work of Kilian et al. (2007), however, both the source and target meshes are fixed during optimization.”
BackgroundMethodResultInfluentialMultiresolution Mean Shift Clustering Algorithm for Shape Interpolation
Chu, H.-K., & Lee, T.-Y. (2009) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2009.40 · Cited by 27 · see citing works
Shape Analysis of Surfaces Using General Elastic Metrics
Su, Z., Bauer, M., Preston, S., Laga, H., & Klassen, E. (2019) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-020-00959-4 · Cited by 26 · see citing works
“Shape analysis of surfaces in R 3 has been motivated by many applications in bioinformatics, computer graphics and medical imaging, see e.g., [16,18,2,11,13,28].”
MethodA Survey on Non-rigid 3D Shape Analysis
Laga, H. (2018) · arXiv.org · doi:10.1016/b978-0-12-811889-4.00007-5 · Cited by 26 · see citing works
“Since Kilian et al. [21]’s approach works on triangulated meshes then where s and t are two adjacent vertices on the tri-angulated mesh that represents the surface f .”
BackgroundInfluentialAn Elasticity-Based Covariance Analysis of Shapes
Rumpf, M., & Wirth, B. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0358-2 · Cited by 26
“in Kendall (1984), Cootes et al. (1995), Hafner et al. (2000), Kilian et al. (2007) .”
Optimized subspaces for deformation-based modeling and shape interpolation
von Radziewsky, P., Eisemann, E., Seidel, H., & Hildebrandt, K. (2016) · Computers & graphics · doi:10.1016/j.cag.2016.05.016 · Cited by 25 · see citing works
“The spaces of meshes can be equipped with Riemannian metrics [43, 44].”
BackgroundA blendshape model that incorporates physical interaction
Ma, W.-C., Wang, Y.-H., Fyffe, G., Barbič, J., Chen, B.-Y., & Debevec, P. (2011) · Comput. Animat. Virtual Worlds · doi:10.1002/cav.1441 · Cited by 25
“[17] presented an isometric deformation method based on Riemannian geometry, considering shape interpolation as a geodesic curve in shape space.”
MethodA Computational Model of Multidimensional Shape
Liu, X.-W., Shi, Y.-G., Dinov, I., & Mio, W. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-010-0323-0 · Cited by 24
“Thus far, the main applications of the model have been to analysis of magnetic resonance images of the human brain.”
BackgroundGeodesics in Shape Space via Variational Time Discretization
Wirth, B., Bar, L., Rumpf, M., & Sapiro, G. (2009) · Energy Minimization Methods in Computer Vision and Pattern Recognition · doi:10.1007/978-3-642-03641-5_22 · Cited by 24 · see citing works
“[20], where an inner product of deformations fields as the underlying metric measures the local distance from a rigid body motion.”
BackgroundGeneralized Surface Flows for Deformable Registration and Cortical Matching
Eckstein, I., Joshi, A. A., Kuo, C.-C. J., Leahy, R., & Desbrun, M. (2007) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-540-75757-3_84 · Cited by 24 · see citing works
“Using generalized flows to compute continuous morphs that follow geodesics in shape spaces [25, 26] is another exciting avenue of future work.”
BackgroundA Numerical Framework for Elastic Surface Matching, Comparison, and Interpolation
Bauer, M., Charon, N., Harms, P., & Hsieh, H.-W. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-021-01476-6 · Cited by 23
“The boundary value problem on functions (seen as parametrized surface) is closely related to mesh interpolation (Kilian et al. 2007; Fröhlich and Botsch 2011) and can be solved by the direct method of variational calculus or, for certain elastic metrics, by exploiting isometries to simpler spaces.”
BackgroundMethodInfluentialPrincipal Geodesic Analysis in the Space of Discrete Shells
Heeren, B., Zhang, C., Rumpf, M., & Smith, W. (2018) · Computer graphics forum (Print) · doi:10.1111/cgf.13500 · Cited by 23 · see citing works
“Kilian et al. [KMP07] showed how to compute geodesic paths between tri-angle meshes using a metric that measures changes in triangle edge lengths.”
Gauge Invariant Framework for Shape Analysis of Surfaces
Tumpach, A., Drira, H., Daoudi, M., & Srivastava, A. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2430319 · Cited by 23
“[2] represents parameterized surfaces by discrete triangulated meshes, assumes a Riemannian metric on the space of such meshes, and computes geodesic paths between given meshes.”
BackgroundShape Correspondence with Isometric and Non-Isometric Deformations
Dyke, R., Stride, C., Lai, Y.-K., Rosin, P. L., Aubry, M., Boyarski, A., Bronstein, A., Bronstein, M., Cremers, D., Fisher, M., et al. (2019) · 3DOR@Eurographics · doi:10.2312/3dor.20191069 · Cited by 23
“Shape correspondence is also necessary in common applications such as modelling [KMP07], reconstruction [LSP08] and tracking [NFS15].”
Method
- Authors
- Kilian, M., Mitra, N. J., & Pottmann, H.
- Affiliations
- TU Wien (AT)
- Venue
- ACM SIGGRAPH 2007 papers
- Publisher
- ACM
- Published
- 2007-07-29
- Pages
- 64
- OpenAlex
- W2154084374
- Cited by
- 224
- References
- 0
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 4 · 2023: 6 · 2022: 6 · 2021: 11 · 2020: 12 · 2019: 12 · 2018: 10 · 2017: 14 · 2016: 11 · 2015: 19 · 2014: 16 · 2013: 12 · 2012: 18
- FWCI
- 37.03
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGGRAPH Conference (SIGGRAPH) (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computational Geometry and Mesh Generation
- Physical Sciences › Mathematics › Applied Mathematics › Geometric Analysis and Curvature Flows
- Keywords
- shape space, Riemannian metrics, triangular meshes, straight line graphs, geodesics, boundary value problems, initial value problems, parallel transport, exponential map, shape morphing, shape deformation, deformation transfer, shape exploration, isometric deformations
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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