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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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In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Citations
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Sorted by citations
Items 201–288 of 288
A genus oblivious approach to cross parameterization
Bennett, J., Pascucci, V., & Joy, K. (2008) · Computer Aided Geometric Design · doi:10.1016/j.cagd.2008.06.003 · Cited by 5
“While the design of a good interpolation scheme is not the focus of this paper, one could use the approach of (Kilian et al., 2007) to minimize the number of self intersections in the morph.”
MethodPose Space Surface Manipulation
Yoshiyasu, Y., & Yamazaki, N. (2012) · Int. J. Comput. Games Technol. · doi:10.1155/2012/596953 · Cited by 5
“Kilian et al. [25], in contrast, introduced an interesting approach that formulates the optimization as a path problem instead of deforming the reference shape.”
BackgroundViPS: Video-informed Pose Spaces for Auto-Rigged MeshesPreprint
Chen, H., Pandey, K., Wu, R., Gadelha, M., Hold-Geoffroy, Y., Tewari, A., Mitra, N. J., Zheng, C., & Guerrero, P. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2604.17623
“…low-dimensional rig-based parameterizations, including linear blend skinning (LBS) [Lewis et al. 2000] and dual quaternion skinning [Kavan et al. 2008], often augmented with energy-based regularizers that preserve local rigidity or volume under articulation [Kilian et al. 2007; Lipman et al. 2007].”
Self-citationScanmove: Motion prediction and transfer for unregistered body meshes
Besnier, T., Arguillère, S., & Daoudi, M. (2025) · Computers & Graphics · doi:10.1016/j.cag.2025.104409 · Cited by 3
“Additionally, to improve the smoothness of predicted sequences, we add an As-Isometric-As-Possible (AIAP) regularization loss L I based on [29] and used in [20] to avoid small non-isometric local deformations.”
Learning high-fidelity robot self-model with articulated 3D Gaussian splatting
Hu, K.-J., Yu, P., & Tan, N. (2025) · The international journal of robotics research · doi:10.1177/02783649251396980 · Cited by 4
“…using sagej.cls Then, for the 3D Gaussian part, following the previous work (Prokudin et al., 2023), we use the as-isometric-as-possible constrain (Kilian et al., 2007) to restrict the distance between neighboring 3D Gaussians to keep similar before and after deformation: where N is the total…”
Mang, A., He, J.-W., & Azencott, R. (2023) · Journal of Computational Physics · doi:10.48550/arxiv.2307.10114 · Cited by 4
“There exists a plurality of mathematical and numerical frameworks to study shape variability; examples include [2,16,24,57,84,96,102,104, 110, 142, 149, 156, 163].”
BackgroundDomadiya, P., Shah, P., & Mitra, S. (2019) · European Conference on Visual Media Production · doi:10.1145/3359998.3369408 · Cited by 4
“In[Kilian et al. 2007], a geometric modeling based DT method is proposed.”
MethodXia, Q., Chen, C., Liu, J.-R., Li, S., Hao, A.-M., & Qin, H. (2019) · Computers & graphics · doi:10.1016/j.cag.2019.05.014 · Cited by 4
Kubota, T. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-008-0192-y · Cited by 4
“In this regard, the work is more closely related to landmark based treatments of Kendall (1984), Bookstein (1989) and Kilian et al. (2007) than parametric ones of Charpiat et al. (2005), Michor and Mumford (2006), Mio et al. (2007) and Younes (1998).”
MethodYang, Y., Yang, H., Nakayama, K., Huang, X., Guibas, L. J., & Qi-xing Huang, A. (2025) · ACM Transactions on Graphics · doi:10.1145/3731164 · Cited by 2
“Our approach is inspired by the success of studying organic shape collections using the lens of shape spaces as differential manifolds [Hart-man et al. 2023; Kilian et al. 2007; Klassen et al. 2004; Srivastava et al. 2011; Yang et al. 2023a].”
InfluentialStochastic Gradient Estimation for Higher-Order Differentiable Rendering
Wang, Z.-C., Fischer, M., & Ritschel, T. (2024) · IEEE International Conference on Computer Vision · doi:10.1109/iccv51701.2025.02618 · Cited by 3
“…in vision and graphics have been applied to several optimization tasks, for instance, for total-variation denoising [3], texture parameterization and surface mappings with approximated Hessians [28], 3D shape manipulation [13], and image deconvolution tasks via (Hessian-free) Newton methods [14].”
Few-shot Image Generation via Information Transfer from the Built Geodesic Surface
Han, Y.-X., Ruan, L., & Wang, B. (2024) · Pattern Recognition · doi:10.48550/arxiv.2401.01749 · Cited by 3
“For instance, Kilian et al. [33] presented continuous deformation of 3D models using Geodesic interpolation in the Shape Space.”
MethodKernel-GPA: A globally optimal solution to deformable SLAM in closed-form
Bai, F., Wu, K.-Z., & Bartoli, A. (2023) · Int. J. Robotics Res. · doi:10.1177/02783649231195380 · Cited by 3
“This representation has a long history in shape analysis Kendall (1984); Kilian, Mitra and Pottmann (2007).”
Dihedral Rigidity and Deformation
Amenta, N., & Rojas, C. (2018) · Canadian Conference on Computational Geometry · doi:10.48550/arxiv.1810.01546 · Cited by 3
“[KMP07].”
Mesh2Fab: Reforming Shapes for Material-specific Fabrication
Yang, Y.-L., Wang, J., & Mitra, N. (2014) · arXiv.org · doi:10.48550/arxiv.1411.3632 · Cited by 3
Self-citationDiscrete geodesic calculus in the space of viscous fluidic objectsPreprint
Rumpf, M., & Wirth, B. (2012) · arXiv.org · doi:10.48550/arxiv.1210.0822 · Cited by 3
“Applications include shape modeling in computer vision [17, 16], computational anatomy, where the morphing path establishes correspondences between a patient and a template [2, 26], shape clustering based on Riemannian distances [32], as well as shape statistics [9, 13], where geodesic paths in…”
Geometry of diffeomorphism groups and shape matching
Bruveris, M. (2012) · doi:10.25560/9692 · Cited by 3
“A different Riemannian metric defined on discrete meshes was presented in [45].”
BackgroundMixing Board Versus Mouse Interaction In Value Adjustment TasksPreprint
Bergner, S., Crider, M., Kirkpatrick, A., & Möller, T. (2011) · arXiv.org · doi:10.48550/arxiv.1110.2520 · Cited by 3
Continuous Deformations by Isometry Preserving Shape Integration
Esturo, J. M., Rössl, C., & Theisel, H. (2010) · Curves and Surfaces · doi:10.1007/978-3-642-27413-8_29 · Cited by 3
“In this sense, we are aware of only two approaches to continuous deformations so far: [14] and its extension [15] which applies divergence-free vector fields for volume preserving shape deformation, and [18] which considers the deformations in a shape space.”
BackgroundMethodInfluentialIncompressible, Quasi-Isometric Deformations of 2-Dimensional Domains
Wolansky, G. (2009) · SIAM Journal of Imaging Sciences · doi:10.1137/080720772 · Cited by 3
“In another approach [9] the authors use a semi-Riemann metric on a discrete surface (simplicial complex) to define a geodesic flow, modulo the space of rigid deformations.”
MethodRelational databases for motion data
Riaz, Q., Krüger, B., & Weber, A. (2016) · International Journal of Innovative Computing and Applications · doi:10.1504/ijica.2016.078723 · Cited by 3
Generating shapes by analogies: An application to hearing aid design
Ünal, G. B., Nain, D., Slabaugh, G., & Fang, T. (2011) · Comput. Aided Des. · doi:10.1016/j.cad.2010.09.008 · Cited by 3
“[20], who utilized Riemannian metrics for both a rigid and an isometric deformation among shapes, would be an alternatively applicable approach for our problem.”
Matching and morphing of isometric models
Wuhrer, S. (2009) · doi:10.22215/etd/2009-06338 · Cited by 3
“[55], the results depend heavily on the initial rigid alignment of the shapes.”
BackgroundMethodInfluentialA shape control method for soap bubble simulation using external forces
Horimoto, A., Sato, S., Sakurai, K., & Nakamoto, K. (2025) · Scientific Reports · doi:10.1038/s41598-025-90850-2 · Cited by 2
“Many researches have traditionally been conducted for the shape morphing 26–30 .”
Geodesic Regression Characterizes 3D Shape Changes in the Female Brain During Menstruation
Myers, A., Taylor, C. M., Jacobs, E. G., & Miolane, N. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00268 · Cited by 3
“Kilian et al. [11] focus on geodesic interpolation or extrapolation of parameterized surfaces and do not study regression.”
BackgroundDihedral deformation and rigidity
Amenta, N., & Rojas, C. (2020) · Computational geometry · doi:10.1016/j.comgeo.2020.101657 · Cited by 2
Statistical shape analysis of tap roots: a methodological case study on laser scanned sugar beets
Heeren, B., Paulus, S., Goldbach, H., Kuhlmann, H., Mahlein, A., Rumpf, M., & Wirth, B. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03654-8 · Cited by 2
“[26] model the space of triangulated shapes.”
Yao, Z., Xia, Y.-Q., & Fan, Z. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2023.2257892 · Cited by 2
“…2016; Fef-ferman et al. 2018; Yao and Xia 2023; Yao et al. 2023), as in the examples of geometric shapes in the shape space (Turk and Levoy 1994; Kilian, Mitra, and Pottmann 2007; Bradley, Nowrouzezahrai, and Beardsley 2013; Dryden and Mardia 2016) and graphs in computer graphics (Phillips et…”
Minimum action principle and shape dynamics
Koehl, P. (2017) · Journal of the Royal Society Interface · doi:10.1098/rsif.2017.0031 · Cited by 2
Corman, E. (2016) · doi:10.70675/b13b6531zb35az4d3dz9800zc6d4979d9b19 · Cited by 2
“Finally, our joint deformation design is related to the deformation transfer and interpolation techniques such as [117, 9] and [61] to name a few.”
BackgroundMethodInfluentialShape exploration of 3D heterogeneous models based on cages
Meng, W.-L., Guo, J.-W., Bonaventura, X., Sbert, M., & Zhang, X. (2016) · Multimedia tools and applications · doi:10.1007/s11042-016-3642-5 · Cited by 2
“[22] design the metrics based on the shape space metrics, and propose a framework of model interpolation for multiresolution models.”
MethodRiemannian methods for optimization in a shape space of triangular meshes
Kniely, M., & Ring, W. (2015) · doi:10.1080/17415977.2014.980243 · Cited by 2
“(See [5] for an overview.)”
BackgroundMethodResolving occlusion in multiframe reconstruction of deformable surfaces
Shaji, A., Varol, A., Fua, P., Yashoteja, Jain, A., & Chandran, S. (2011) · CVPR 2011 WORKSHOPS · doi:10.1109/cvprw.2011.5981724 · Cited by 2
“It was previously shown [10] that we can associate a well defined Riemannian metric to the space of isometric deformations.”
BackgroundMORPHING OF TRIANGULAR MESHES IN SHAPE SPACE
WUHRER, S., BOSE, P., CHANG, S., O'ROURKE, J., & BRUNTON, A. (2010) · International Journal of Shape Modeling · doi:10.1142/s0218654310001341 · Cited by 1
“[9] used shape space representations to guide morphs and other more general deformations between shapes represented as triangular meshes.”
BackgroundMethodInfluentialYang, J., & Cao, X.-Y. (2026) · Computers & graphics · doi:10.1016/j.cag.2026.104657 · Cited by 1
Khanam, T., Laga, H., Bennamoun, M., Wang, G.-J., Sohel, F., Boussaid, F., & Srivastava, A. (2026) · arXiv.org · doi:10.48550/arxiv.2607.13456 · Cited by 1
“[15] quantify shape differences using deviations from rigidity and isometry.”
Ferhat, A., Proudhon, H., Remacha, C., & Ryckelynck, D. (2026) · Engineering computations · doi:10.1007/s00366-025-02243-8 · Cited by 1
“…or shape registration, is a central challenge in digital geometry processing, with applications spanning across various fields such as manufacturing, shape interpolation, shape morphing [1, 2], deformation transfer [3], statistical shape analysis [4] and segmentation of 3D shapes [5, 6 ].”
Geodesic feature augmentation for zero-shot text-guided diffusion style transfer
Han, Y., Ruan, L., & Wang, B. (2025) · The Visual Computer · doi:10.1007/s00371-025-04104-3 · Cited by 1
PaNDaS: Learnable Deformation Modeling with Localized Control
Besnier, T., Pierson, E., Arguillere, S., Ovsjanikov, M., & Daoudi, M. (2024) · doi:10.48550/arxiv.2412.02306 · Cited by 1
“Proposed solutions use geometric regularization [8, 37], model directly the shape space [2, 4, 16, 18, 21] or, more recently, learn shape interpolation by regularizing neural networks [10– 12].”
Elbow Joint Classification for Total Elbow Arthroplasty
Wang, J., Zhang, Y., Ding, X., Ma, X., Peng, C., Xue, J., Lin, F., Wu, Y., & Zhang, Q. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc53108.2024.10781912 · Cited by 1
“The shape-based algorithm considers the 3D shape of the bones, representing them as points on Riemannian manifold [6]; while the anatomical structure-based algorithm incorporates local geometric measurements of the bones, as suggested by clinicians.”
High-order shape interpolation
Huang, Z., Liu, S.-B., & Fu, X.-M. (2024) · Computer Aided Geometric Design · doi:10.1016/j.cagd.2024.102301 · Cited by 1
KernelGPA: A Deformable SLAM Back-end
Bai, F., & Bartoli, A. (2022) · Robotics: Science and Systems Conference · doi:10.15607/rss.2022.xviii.002 · Cited by 1
“This representation has a long history in shape analysis [28, 29].”
BackgroundConsistent Curvature Approximation on Riemannian Shape Spaces
Effland, A., Heeren, B., Rumpf, M., & Wirth, B. (2019) · IMA Journal of Numerical Analysis · doi:10.1093/imanum/draa092 · Cited by 1
Correspondence Between 3D Shapes Based on Improved Functional Map
Yang, J., Li, L., & Obaseki, A. J. (2019) · Journal of Physics, Conference Series · doi:10.1088/1742-6596/1229/1/012022 · Cited by 1
“In the area of computer graphics and computer vision, shape correspondence have wide range of applications, such as shape morphing and interpolation [2, 3], shape segmentation [4], mesh parameterization [5], shape recognition and retrieval [6] and so on.”
BackgroundNguyen, M. K. (2018) · doi:10.70675/32956660z02b2z4be9za9d9ze71a5f9a6ce6 · Cited by 1
“Other methods include the As-Similar-As-Possible method [129, 251] which preserves the tetrahedral meshes composing an object, or the Example-Driven method [84] which minimizes the deformations of edges, angles and dihedral angles.”
MethodVector Graph Representation for Deformation Transfer Using Poisson Interpolation
Domadiya, P., Shah, P., & Mitra, S. (2018) · IEEE Workshop/Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00101 · Cited by 1
“, in [6], propose a geometric modeling method that projects a shape (mesh) in higher dimensions called the shape space and suggests its use for DT.”
MethodElastic 3D shape analysis using square-root normal field representation
Laga, H., Jermyn, I. H., Kurtek, S., & Srivastava, A. (2017) · IEEE Conference on Decision and Control · doi:10.1109/cdc.2017.8264053 · Cited by 1
“[14] represent surfaces by discrete triangulated meshes and compute geodesic paths (deformations) between them, while assuming that the meshes are registered.”
BackgroundVisualization of Variation and Variability
Busking, S. (2014) · doi:10.4233/uuid:96610558-5d81-4a0a-b9dc-5e59b8214b29 · Cited by 1
Normal-controlled coordinates based feature-preserving mesh editing
Wang, S.-F., Cai, Y., Yu, Z., Cao, J., & Su, Z.-X. (2013) · Multimedia tools and applications · doi:10.1007/s11042-013-1517-6 · Cited by 1
“To solve these problems non-linear techniques are proposed [3, 4, 7, 11, 14, 18, 20, 21, 25, 28].”
MethodAlgorithms for 3D isometric shape correspondence (3B izometrik şekil eşleme için algoritmalar)
Sahillioğlu, Y. (2012) · doi:10.2312/8296 · Cited by 1
“Dealing with the 3D shape correspondence problem is important because it lays the foundations of numerous practical applications such as shape morphing and interpolation [1][2], mesh parameterization [3][4], rigid or non-rigid shape registration [5][6], time-varying reconstruction [7][8], shape recognition and retriev…”
BackgroundGPU‐friendly shape interpolation based on trajectory warping
Chen, L., Huang, J., Zhang, H.-X., & Hua, W. (2011) · Comput. Animat. Virtual Worlds · doi:10.1002/cav.407 · Cited by 1
“Alternatively, literature [12] formulates the shape interpolation problem as computing geodesic curves in the shape space.”
BackgroundPaquet, E., & Viktor, H. (2011) · Electronic imaging · doi:10.1117/12.872862 · Cited by 1
“…component analysis, [9] which maintains the use of the Mahalanobis distance but consider the later as only valid locally on an open subset, [10] which defines the metric in terms of “ad hoc” constraints on the geometry transformations, [11] which calculates the Eigen vector in terms of the…”
Comparative Analysis of Quasi-Conformal Deformations in Shape Space
Taimouri, V., He, H.-G., & Hua, J. (2010) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-642-15711-0_61 · Cited by 1
“Some geometric structures have been established to provide the Isometric and Rigid metrics based on the Riemannian metric definition [7].”
BackgroundMethodInfluentialRecognition of objects in various situations from two dimensional images
Han, Y.-X., Koike, H., Wang, B., & Idesawa, M. (2010) · International Conference on Image Processing Theory Tools and Applications · doi:10.1109/ipta.2010.5586785 · Cited by 1
“VI. 978-1-4244-7249-9/10/$26.00 ©2010 IEEE”
InfluentialDiscrete Riemannian calculus on shell space
Heeren, B., Rumpf, M., Wardetzky, M., & Wirth, B. (2020) · Handbook of numerical analysis · doi:10.1016/bs.hna.2019.06.005 · Cited by 1
Nizamani, A., Laga, H., Wang, G.-J., Boussaïd, F., Bennamoun, M., & Srivastava, A. (2026) · doi:10.48550/arxiv.2609.27728
“For example, Kilian et al. [18] use a metric that penalizes deviations from rigidity and isometry.”
Li, Z.-Q., Amrani, A., Rai, S., & Laga, H. (2026) · Computer graphics forum (Print) · doi:10.1111/cgf.70521
SmoothMotionVectors: Optimizing Your Content for Video Codecs in Free View Video Compression
Song, M., Zhang, Y., Tang, S., & Aydın, T. (2026) · International Conference on Computer Graphics and Interactive Techniques · doi:10.1145/3799902.3811112
“Photometric supervision and regularization are applied after ras-terization: where R(·) enforces local rigidity or smoothness [Duan et al. 2024; Gao et al. 2024; Huang et al. 2023; Kilian et al. 2007; Oblak et al. 2024; Prokudin et al. 2023; Song et al. 2025; Sorkine and Alexa 2007].”
ARAPDiffusion: ARAP Regularization for Diffusion-Based Deformable Shape Space Learning
Liu, H.-B., Ke, J.-H., Yang, H.-T., Huang, X.-R., Pavlakos, G., & Huang, Q.-X. (2026) · arXiv.org · doi:10.48550/arxiv.2606.06887
“Related work in computational shape analysis studies geometric metrics and geodesics for deformable shape spaces [23, 29, 34, 17, 41, 15, 14, 16, 22, 12]; GeoLatent [41], for example, derives a Riemannian metric from ARAP deformation geometry.”
Khanam, T., Laga, H., Bennamoun, M., Wang, G.-J., Sohel, F., Boussaid, F., Wang, G., & Srivastava, A. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3726133
“For example, Kilian et al. [21] compute geodesics by penalizing deviations from rigidity and isometry.”
Sinkhorn Normalization of Diffusion Kernels
Kessler, N., Magnet, R., & Feydy, J. (2025) · doi:10.48550/arxiv.2507.06161
“In Figure 5, following Kilian et al. (2007), we perform geodesic interpolation of anatomical poses.”
Han, Y.-X., Ruan, L., & Wang, B. (2024) · arXiv.org · doi:10.48550/arxiv.2408.10533
“For example, Kilian et al. [38] utilize Geodesic interpolation within the Shape Space to enable the continuous deformation of 3D models.”
MethodFradi, A., Samir, C., & Adouani, I. (2024) · Journal of Optimization Theory and Applications · doi:10.1007/s10957-024-02473-8
“In most previous works the problem was either presumed or solved as an independent preprocessing step [6, 11, 19, 27].”
Parametrizing Product Shape Manifolds by Composite Networks
Sassen, J., Hildebrandt, K., Rumpf, M., & Wirth, B. (2023) · International Conference on Learning Representations · doi:10.48550/arxiv.2302.14665
“We follow an approach similar to Kilian et al. (2007) to perform all computations on a coarsened mesh and prolongate solutions to a fine one only for visualizations.”
BackgroundMethodFunctional maps based dense 3D human body correspondence from single view point clouds
Li, W., Wang, K.-K., & Zheng, H. (2021) · Other Conferences · doi:10.1117/12.2626548
“Correspondence is a key component in tasks such as 3D alignment and spatiotemporal reconstruction, which is also an indispensable prerequisite for various applications such as attribute migration [5], shape interpolation [7], and statistical modeling [8].”
BackgroundEhm, V., Cremers, D., & Bernard, F. (2021) · International Conference on 3D Vision · doi:10.1109/3dv53792.2021.00126
“This is closely related to shape interpolation [35, 27, 20, 19], which is typically based on the assumption that there exists a smooth deformation between two given shapes.”
Shape space – a paradigm for character animation in computer graphics
Heeren, B., & Rumpf, M. (2020) · doi:10.14760/snap-2020-007-en
“Here, the notion of shape includes planar curves [16, 15, 1], smooth surfaces [14, 2], images [17, 19], or triangle meshes [13].”
BackgroundXu, L.-H., Yao, B., Nie, Y.-W., Jiang, J., Wu, S., & Li, G.-Q. (2020) · Computer-Aided Design and Applications · doi:10.14733/cadaps.2021.486-501
“6 and 7 depict the frame transition smoothness of sequences by Kilian et al. [16], Heeren et al. [11], Xia et al. [33] and our approach via stacking up the frames together.”
BackgroundInfluentialNogneng, D. (2018) · doi:10.70675/33441ae5zd99bz415dz9eefz5ddf2758a863
“This problem has many applications such as deformation transfer [91], shape interpolation [42] and even statistical shape modeling [35] among myriad others.”
BackgroundHao, A.-M., Lan, Y., Li, S., & Yang, C. (2017) · Science Discovery · doi:10.11648/j.sd.20170502.15
Hachani, M. (2015) · doi:10.70675/3dddf517z91c8z4d46z9f5bz9563cd958bb8
“[Kilian 2007] proposed a numerical approach to calculate geodesic conductivities between triangulated surfaces using Euclidean metrics.”
BackgroundInfluentialA Quantum Particle Swarm Optimization and Genetic Algorithm approach to the correspondence problem
Hadavi, H., Viktor, H., & Paquet, E. (2014) · 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings · doi:10.1109/inista.2014.6873622
“These areas include the measurement of the level of similarity between two objects, shape registration [1], indexing and retrieval [2], shape morphing, and detecting deformations of objects [3, 4], amongst others.”
BackgroundKechadi, M. (2014) · doi:10.70675/0da5f51az70a0z4814zb479z7b2cc16e15bc
“La résistance des films en PET au contact des acides est attribuée à la phase cristalline dans le PET [11], [12] .”
BackgroundInfluentialFrom differential equations to differential geometry: aspects of regularisation in machine learning
Steinke, F. (2009) · doi:10.22028/d291-25950
“Impressive results in shape processing have recently been obtained [Davis et al., 2007; Kilian et al., 2007] by imposing a Riemannian metric on the set of shapes, so that shape interpolation is reduced to the estimation of a smooth curve in the manifold of all shapes.”
Learnable Geometric Filter Banks for Multiscale Representation of 3-D Shapes
Zhou, J.-Y., Dai, W.-R., Zou, J.-N., Zheng, Z.-Y., Li, S., Li, C.-L., & Xiong, H.-K. (2026) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2026.3707884
“D shapes in the field of geometry processing, with specific applications including shape interpolation [5], texture and deformation transfer [7], and statistical shape modeling and analysis [8].”
Tian, F.-Z., & Kim, S. (2026) · IEEE transactions on multimedia · doi:10.1109/tmm.2026.3719157
“IsometricConstraint: Inspiredby[24],[57]andbasedon[95], we introduce an isometric constraint that preserves the relative distances between adjacent 3D Gaussian means during deformation.”
Near-isometric motion analysis using spectral geometry
Hua, J., Hu, J., & Zhong, Z.-C. (2020) · doi:10.1016/b978-0-12-813842-7.00012-7
(2019) · Elsevier eBooks · doi:10.1016/b978-0-12-813842-7.00017-6
Energy Minimization Methods in Computer Vision and Pattern Recognition
Heyden, A., Kahl, F., Olsson, C., Oskarsson, M., & Tai, X.-C. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40395-8
“We compare our results with Gamma expansion (GE) method [2] and Banterle’s inverse tone mapping operators (iTMO) method [11].”
BackgroundMethodResultInfluentialObject Recognition with a Limited Database Using Shape Space Theory
Han, Y.-X., Wang, B., Koike, H., & Idesawa, M. (2012) · doi:10.4018/978-1-61350-429-1.ch008
Fractal geometryand multimedia retrieval: a theoretical framework
Paquet, E., & Viktor, H. (2012) · doi:10.1117/12.905086
“Also, [4] constructed a Riemannian space by imposing ad hoc constraints on the metric based on predetermined geometrical transformations.”
Capturing and modeling of deformable objects
Popa, T. (2009) · doi:10.14288/1.0051172
Metadata
- 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 › Computer Science › Computer Graphics and Computer-Aided Design › 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
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