The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Blended intrinsic maps
Abstract
This paper describes a fully automatic pipeline for finding an intrinsic map between two non-isometric, genus zero surfaces. Our approach is based on the observation that efficient methods exist to search for nearly isometric maps (e.g., Möbius Voting or Heat Kernel Maps), but no single solution found with these methods provides low-distortion everywhere for pairs of surfaces differing by large deformations. To address this problem, we suggest using a weighted combination of these maps to produce a "blended map." This approach enables algorithms that leverage efficient search procedures, yet can provide the flexibility to handle large deformations. The main challenges of this approach lie in finding a set of candidate maps { m i } and their associated blending weights { b i ( p )} for every point p on the surface. We address these challenges specifically for conformal maps by making the following contributions. First, we provide a way to blend maps, defining the image of p as the weighted geodesic centroid of m i ( p ). Second, we provide a definition for smooth blending weights at every point p that are proportional to the area preservation of m i at p . Third, we solve a global optimization problem that selects candidate maps based both on their area preservation and consistency with other selected maps. During experiments with these methods, we find that our algorithm produces blended maps that align semantic features better than alternative approaches over a variety of data sets.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“[8], [9] that can handle near-to approximateisometric deformation.”
From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [8]
In the citing paper
“[8] uses sum of isometric distortion-difference of geodesic distances between the end-points of one correspondence with respect to all the other correspondences.”
From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [8]
In the citing paper
“[8] also recruited a volunteer to label 10-35 semantically meaningful and consistent feature points for each class of objects in the watertight meshes dataset [114].”
From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [8]
In the citing paper
“For non-rigid shapes, many deformations are only near/approximateisometric, which may lead to matching ambiguities [8].”
From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [8]
In the citing paper
“[8] has started in this direction using blended conformal maps.”
From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [8]
Citations
Cites
A Method for Registration of 3-D Shapes
Besl, P., & Mckay, N. D. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.121791 · Cited by 22,163
The Earth Mover's Distance as a Metric for Image Retrieval
Rubner, Y., Tomasi, C., & Guibas, L. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026543900054 · Cited by 5,547
Computing Discrete Minimal Surfaces and Their Conjugates
Pinkall, U., & Polthier, K. (1993) · Experimental Mathematics · doi:10.1080/10586458.1993.10504266 · Cited by 1,278
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
Numerical Geometry of Non-Rigid Shapes
Bronstein, A. M., Bronstein, M. M., & Kimmel, R. (2008) · Texts and monographs in computer science · doi:10.1007/978-0-387-73301-2 · Cited by 767
The space of human body shapes
Allen, B., Curless, B., & Popović, Z. (2003) · ACM Transactions on Graphics · doi:10.1145/882262.882311 · Cited by 725
A Survey on Shape Correspondence
van Kaick, O. M., Zhang, H., Hamarneh, G., & Cohen‐Or, D. (2011) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2011.01884.x · Cited by 606
Generalized multidimensional scaling: A framework for isometry-invariant partial surface matching
Bronstein, A. M., Bronstein, M. M., & Kimmel, R. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0508601103 · Cited by 555
Gelfand, N., Mitra, N. J., Guibas, L., & Pottmann, H. (2005) · doi:10.2312/sgp/sgp05/197-206 · Cited by 545
Möbius voting for surface correspondence
Lipman, Y., & Funkhouser, T. (2009) · ACM Transactions on Graphics · doi:10.1145/1531326.1531378 · Cited by 332
Self-citation
Cited by
Sorted by citations
Geometric Deep Learning: Going beyond Euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2693418 · Cited by 3,661
Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs
Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., & Bronstein, M. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.576 · Cited by 1,820
Image Matching from Handcrafted to Deep Features: A Survey
Ma, J.-Y., Jiang, X., Fan, A., Jiang, J., & Yan, J. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01359-2 · Cited by 977
Geodesic Convolutional Neural Networks on Riemannian Manifolds
Masci, J., Boscaini, D., Bronstein, M. M., & Vandergheynst, P. (2015) · 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2015.112 · Cited by 721
Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid
Tam, G. K. L., Cheng, Z.-Q., Lai, Y.-K., Langbein, F. C., Liu, Y., Marshall, D., Martin, R. R., Sun, X.-F., & Rosin, P. L. (2013) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.310 · Cited by 679
FAUST: Dataset and Evaluation for 3D Mesh Registration
Bogo, F., Romero, J., Loper, M., & Black, M. J. (2014) · 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/cvpr.2014.491 · Cited by 688
Ma, J., Zhao, J. Y., Jiang, J., Zhou, H., & Guo, X. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1117-z · Cited by 615
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
Fey, M., Lenssen, J. E., Weichert, F., & Muller, H. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00097 · Cited by 459
Non-Rigid Point Set Registration by Preserving Global and Local Structures
Ma, J., Zhao, J. Y., & Yuille, A. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2467217 · Cited by 334
3D-CODED: 3D Correspondences by Deep Deformation
Groueix, T., Fisher, M., Kim, V. G., Russell, B. C., & Aubry, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_15 · Cited by 305
Self-citationDeep Functional Maps: Structured Prediction for Dense Shape Correspondence
Litany, O., Remez, T., Rodolà, E., Bronstein, A. M., & Bronstein, M. M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.603 · Cited by 298
Robust $L_{2}E$ Estimation of Transformation for Non-Rigid Registration
Ma, J., Qiu, W., Zhao, J. Y., Ma, Y., Yuille, A., & Zhuowen Tu, A. (2015) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2014.2388434 · Cited by 280
Partial Functional Correspondence
Rodolà, E., Cosmo, L., Bronstein, M. M., Andrea Torsello, A., & Cremers, D. (2016) · Computer Graphics Forum · doi:10.1111/cgf.12797 · Cited by 236
Deformable Shape Completion with Graph Convolutional Autoencoders
Litany, O., Bronstein, A. M., Bronstein, M. M., & Makadia, A. (2018) · 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/cvpr.2018.00202 · Cited by 236
Boscaini, D., Masci, J., Melzi, S., Bronstein, M. M., Castellani, U., & Vandergheynst, P. (2015) · Computer Graphics Forum · doi:10.1111/cgf.12693 · Cited by 205
Entropic metric alignment for correspondence problems
Solomon, J., Gabriel Peyré, A., Kim, V. G., & Sra, S. (2016) · ACM Transactions on Graphics · doi:10.1145/2897824.2925903 · Cited by 183
Self-citationConsistent Shape Maps via Semidefinite Programming
Qi-xing Huang, A., & Guibas, L. J. (2013) · Computer Graphics Forum · doi:10.1111/cgf.12184 · Cited by 174
Functional map networks for analyzing and exploring large shape collections
Qi-xing Huang, A., Wang, F., & Guibas, L. J. (2014) · ACM Transactions on Graphics · doi:10.1145/2601097.2601111 · Cited by 171
Unsupervised Learning of Dense Shape Correspondence
Halimi, O., Litany, O., Rodolà, E., Bronstein, A. M., & Kimmel, R. (2019) · 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/cvpr.2019.00450 · Cited by 170
Continuous and orientation-preserving correspondences via functional maps
Ren, J., Poulenard, A., Wonka, P., & Ovsjanikov, M. (2018) · ACM Transactions on Graphics · doi:10.1145/3272127.3275040 · Cited by 167
Map-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
Unsupervised Deep Learning for Structured Shape Matching
Roufosse, J.-M., Sharma, A., & Ovsjanikov, M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00170 · Cited by 158
SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator
Gong, S., Chen, L., Bronstein, M. M., & Zafeiriou, S. P. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00509 · Cited by 149
Nonrigid Point Set Registration With Robust Transformation Learning Under Manifold Regularization
Ma, J., Wu, J. X., Zhao, J. Y., Jiang, J., Zhou, H., & Sheng, Q. Z. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2872528 · Cited by 149
Point registration via efficient convex relaxation
Maron, H., Dym, N., Kezurer, I., Kovalsky, S., & Lipman, Y. (2016) · ACM Transactions on Graphics · doi:10.1145/2897824.2925913 · Cited by 168
Self-citationSchelling points on 3D surface meshes
Chen, X., Saparov, A., Pang, B., & Funkhouser, T. A. (2012) · ACM Transactions on Graphics · doi:10.1145/2185520.2185525 · Cited by 130
Self-citationRecent Trends, Applications, and Perspectives in 3D Shape Similarity Assessment
Biasotti, S., Cerri, A., Bronstein, A. M., & Bronstein, M. M. (2015) · Computer Graphics Forum · doi:10.1111/cgf.12734 · Cited by 131
Vestner, M., Litman, R., Rodolà, E., Bronstein, A. M., & Cremers, D. (2017) · 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/cvpr.2017.707 · Cited by 126
Anisotropic Diffusion Descriptors
Boscaini, D., Masci, J., Rodolà, E., Bronstein, M. M., & Cremers, D. (2016) · Computer Graphics Forum · doi:10.1111/cgf.12844 · Cited by 119
A Survey of Non‐Rigid 3D Registration
Deng, B., Yao, Y., Dyke, R., & Zhang, J.-Y. (2022) · Computer graphics forum (Print) · doi:10.1111/cgf.14502 · Cited by 154
Aigerman, N., & Lipman, Y. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818099 · Cited by 114
Self-citationSparse Modeling of Intrinsic Correspondences
Pokrass, J., Bronstein, A. M., Bronstein, M. M., Sprechmann, P., & Sapiro, G. (2013) · Computer Graphics Forum · doi:10.1111/cgf.12066 · Cited by 111
Fully Spectral Partial Shape Matching
Litany, O., Rodolà, E., Bronstein, A. M., & Bronstein, M. M. (2017) · Computer Graphics Forum · doi:10.1111/cgf.13123 · Cited by 102
Data-driven shape analysis and processing
Xu, K., Kim, V. G., Huang, Q., Mitra, N. J., & Kalogerakis, E. (2016) · SIGGRAPH Asia Courses · doi:10.1145/2988458.2988473 · Cited by 107
Self-citationLaga, H., Mortara, M., & Spagnuolo, M. (2013) · ACM Transactions on Graphics · doi:10.1145/2516971.2516975 · Cited by 101
Robust Nonrigid Registration by Convex Optimization
Chen, Q., & Koltun, V. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.236 · Cited by 94
Efficient Deformable Shape Correspondence via Kernel Matching
Vestner, M., Lähner, Z., Boyarski, A., Litany, O., Slossberg, R., Remez, T., Rodolà, E., Bronstein, A. M., Bronstein, M. M., Kimmel, R., et al. (2017) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00065 · Cited by 100
Lifted bijections for low distortion surface mappings
Aigerman, N., Poranne, R., & Lipman, Y. (2014) · ACM Transactions on Graphics · doi:10.1145/2601097.2601158 · Cited by 96
Self-citationGilani, S. Z., Mian, A. S., Faisal Shafait, B., & Reid, I. D. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2725279 · Cited by 89
Deblurring and Denoising of Maps between Shapes
Ezuz, D., & Ben‐Chen, M. (2017) · Computer Graphics Forum · doi:10.1111/cgf.13254 · Cited by 87
An optimization approach for extracting and encoding consistent maps in a shape collection
Qi-xing Huang, A., Zhang, G., Gao, L., Hu, S., Butscher, A., & Guibas, L. J. (2012) · ACM Transactions on Graphics · doi:10.1145/2366145.2366186 · Cited by 86
Hyperbolic orbifold tutte embeddings
Aigerman, N., & Lipman, Y. (2016) · ACM Transactions on Graphics · doi:10.1145/2980179.2982412 · Cited by 86
Self-citationFunctional correspondence by matrix completion
Kovnatsky, A., Bronstein, M. M., Bresson, X., & Vandergheynst, P. (2015) · 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/cvpr.2015.7298692 · Cited by 83
Spectral multidimensional scaling
Aflalo, Y., & Kimmel, R. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1308708110 · Cited by 80
Deep part induction from articulated object pairs
Yi, L., Huang, H., Liu, D., Kalogerakis, E., Su, H., & Guibas, L. (2018) · ACM Transactions on Graphics · doi:10.1145/3272127.3275027 · Cited by 81
Computing and processing correspondences with functional maps
Ovsjanikov, M., Corman, É., Bronstein, M. M., Rodolà, E., Ben‐Chen, M., Guibas, L. J., Chazal, F., & Bronstein, A. M. (2016) · SIGGRAPH Asia Courses · doi:10.1145/2988458.2988494 · Cited by 81
Lombaert, H., Grady, L. J., Polimeni, J. R., & Farida Cheriet, A. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.276 · Cited by 79
Eynard, D., Rodolà, E., Glashoff, K., & Bronstein, M. M. (2016) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.49 · Cited by 77
Topological Function Optimization for Continuous Shape Matching
Poulenard, A., Škraba, P., & Ovsjanikov, M. (2018) · Computer Graphics Forum · doi:10.1111/cgf.13487 · Cited by 73
FARM: Functional Automatic Registration Method for 3D Human Bodies
Marin, R., Melzi, S., Rodolà, E., & Castellani, U. (2019) · Computer Graphics Forum · doi:10.1111/cgf.13751 · Cited by 68
Reversible Harmonic Maps between Discrete Surfaces
Ezuz, D., Solomon, J., & Ben‐Chen, M. (2019) · ACM Transactions on Graphics · doi:10.1145/3202660 · Cited by 69
Computing and processing correspondences with functional maps
Ovsjanikov, M., Corman, É., Bronstein, M. M., Rodolà, E., Ben‐Chen, M., Guibas, L. J., Chazal, F., & Bronstein, A. M. (2017) · ACM SIGGRAPH Courses · doi:10.1145/3084873.3084877 · Cited by 68
Dym, N., Maron, H., & Lipman, Y. (2017) · ACM Transactions on Graphics · doi:10.1145/3130800.3130826 · Cited by 66
Self-citationPanozzo, D., Baran, I., Diamanti, O., & Sorkine‐Hornung, O. (2013) · ACM Transactions on Graphics · doi:10.1145/2461912.2461935 · Cited by 65
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
DPFM: Deep Partial Functional Maps
Attaiki, S., Pai, G., & Ovsjanikov, M. (2021) · International Conference on 3D Vision · doi:10.1109/3dv53792.2021.00040 · Cited by 99
PFCNN: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames
Yang, Y., Liu, S., Pan, H., Liu, Y., & Tong, X. (2020) · 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/cvpr42600.2020.01359 · Cited by 64
Matching Deformable Objects in Clutter
Cosmo, L., Rodolà, E., Masci, J., Andrea Torsello, A., & Bronstein, M. M. (2016) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.10 · Cited by 59
Variance-minimizing transport plans for inter-surface mapping
Mandad, M., Cohen‐Steiner, D., Kobbelt, L. P., Alliez, P., & Desbrun, M. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073671 · Cited by 58
Supervised 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
Han, Z., Liu, Z., Vong, C., Liu, Y.-S., Bu, S., Han, J., & Chen, C. L. P. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2816821 · Cited by 54
Discrete Optimization for Shape Matching
Ren, J., Melzi, S., Wonka, P., & Ovsjanikov, M. (2021) · Computer Graphics Forum · doi:10.1111/cgf.14359 · Cited by 54
Panozzo, D., Lipman, Y., Puppo, E., & Zorin, D. (2012) · ACM Transactions on Graphics · doi:10.1145/2185520.2185607 · Cited by 54
Self-citationMinimum-Distortion Isometric Shape Correspondence Using EM Algorithm
Sahillioglu, Y., & Yemez, Y. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.26 · Cited by 53
Distortion-minimizing injective maps between surfaces
Schmidt, P., Born, J., Campen, M., & Kobbelt, L. P. (2019) · ACM Transactions on Graphics · doi:10.1145/3355089.3356519 · Cited by 53
Han, Z., Liu, Z., Han, J., Vong, C., Bu, S., & Chen, C. L. P. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2582532 · Cited by 52
Improved 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
Diffusion pruning for rapidly and robustly selecting global correspondences using local isometry
Tam, G. K. L., Martin, R. R., Rosin, P. L., & Lai, Y. (2014) · ACM Transactions on Graphics · doi:10.1145/2517967 · Cited by 49
Unsupervised 3D Local Feature Learning by Circle Convolutional Restricted Boltzmann Machine
Han, Z., Liu, Z., Han, J., Vong, C., Bu, S., & Li, X. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2605920 · Cited by 49
Masci, J., Rodolà, E., Boscaini, D., Bronstein, M. M., & Li, H. (2016) · SIGGRAPH Asia Courses · doi:10.1145/2988458.2988485 · Cited by 51
SHREC’20: Shape correspondence with non-isometric deformations
Dyke, R. M., Lai, Y., Rosin, P. L., Zappalá, S., Dykes, S., Guo, D., Li, K., Marin, R., Melzi, S., & Yang, J. (2020) · Computers & Graphics · doi:10.1016/j.cag.2020.08.008 · Cited by 45
Consistent Partial Matching of Shape Collections via Sparse Modeling
Cosmo, L., Rodolà, E., Albarelli, A., Mémoli, F., & Cremers, D. (2016) · Computer Graphics Forum · doi:10.1111/cgf.12796 · Cited by 43
Isospectralization, or How to Hear Shape, Style, and Correspondence
Cosmo, L., Panine, M., Rampini, A., Ovsjanikov, M., Bronstein, M. M., & Rodolà, R. (2019) · 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/cvpr.2019.00771 · Cited by 43
Data‐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
Self-citationRobust Non-Rigid Registration with Reweighted Position and Transformation Sparsity
Li, K., Yang, J., Lai, Y., & Guo, D. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2832136 · Cited by 43
Inter-surface maps via constant-curvature metrics
Schmidt, P., Campen, M., Born, J., & Kobbelt, L. P. (2020) · ACM Transactions on Graphics · doi:10.1145/3386569.3392399 · Cited by 41
Smooth Non-Rigid Shape Matching via Effective Dirichlet Energy Optimization
Magnet, R., Ren, J., Sorkine‐Hornung, O., & Ovsjanikov, M. (2022) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv57658.2022.00061 · Cited by 40
Bilateral Maps for Partial Matching
van Kaick, O. M., Zhang, H., & Hamarneh, G. (2013) · Computer Graphics Forum · doi:10.1111/cgf.12084 · Cited by 37
Embedding 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 38
Han, Z., Liu, Z., Han, J., Vong, C., Bu, S., & Chen, C. L. P. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2778764 · Cited by 36
Non-rigid registration under anisotropic deformations
Dyke, R., Lai, Y.-K., Rosin, P. L., & Tam, G. K. L. (2019) · Computer Aided Geometric Design · doi:10.1016/j.cagd.2019.04.014 · Cited by 22
Modern techniques and applications for real-time non-rigid registration
Bouaziz, S., Tagliasacchi, A., Li, H., & Pauly, M. (2016) · SIGGRAPH ASIA Courses · doi:10.1145/2988458.2988490 · Cited by 40
Hamiltonian Operator for Spectral Shape Analysis
Choukroun, Y., Shtern, A., Bronstein, A. M., & Kimmel, R. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2867513 · Cited by 35
Elastic Correspondence between Triangle Meshes
Ezuz, D., Heeren, B., Azencot, O., Rumpf, M., & Ben‐Chen, M. (2019) · Computer Graphics Forum · doi:10.1111/cgf.13624 · Cited by 35
A 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
Elastic Shape Analysis of Three-Dimensional Objects
Jermyn, I. H., Kurtek, S., Laga, H., & Srivastava, A. (2017) · Synthesis Lectures on Computer Vision · doi:10.2200/s00785ed1v01y201707cov012 · Cited by 67
Unsupervised Deep Multi-shape Matching
Cao, D., & Bernard, F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20062-5_4 · Cited by 34
As‐Conformal‐As‐Possible Surface Registration
Yoshiyasu, Y., Ma, W., Yoshida, E., & Kanehiro, F. (2014) · Computer Graphics Forum · doi:10.1111/cgf.12451 · Cited by 34
Wang, Y., Ren, J., Dong-ming Yan, A., Guo, J., Zhang, X., & Wonka, P. (2020) · ACM Transactions on Graphics · doi:10.1145/3386569.3392443 · Cited by 33
Deformation-driven topology-varying 3D shape correspondence
Alhashim, I., Xu, K., Zhuang, Y., Cao, J., Simari, P. D., & Zhang, H. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818088 · Cited by 32
Coarse‐to‐Fine Isometric Shape Correspondence by Tracking Symmetric Flips
Sahillioğlu, Y., & Yemez, Y. (2013) · Computer Graphics Forum · doi:10.1111/cgf.12007 · Cited by 33
Spatiotemporal atlas parameterization for evolving meshes
Prada, F., Kazhdan, M., Chuang, M., Collet, A., & Hoppe, H. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073679 · Cited by 32
Srivastava, A., Turaga, P. K., & Kurtek, S. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.03.006 · Cited by 30
SuperMatching: Feature Matching Using Supersymmetric Geometric Constraints
Cheng, Z., Chen, Y., Martin, R. R., Lai, Y., & Wang, A. (2013) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2013.15 · Cited by 32
A Genetic Isometric Shape Correspondence Algorithm with Adaptive Sampling
Sahillioğlu, Y. (2018) · ACM Transactions on Graphics · doi:10.1145/3243593 · Cited by 29
Isometric Multi-Shape Matching
Gao, M., Lähner, Z., Thunberg, J., Cremers, D., & Bernard, F. (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/cvpr46437.2021.01396 · Cited by 30
Spectral coarsening of geometric operators
Liu, H. D., Jacobson, A., & Ovsjanikov, M. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3322953 · Cited by 31
Transferring Category-Based Functional Grasping Skills by Latent Space Non-Rigid Registration
Rodriguez, D., & Behnke, S. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2826057 · Cited by 28
Interactive Curve Constrained Functional Maps
Gehre, A., Bronstein, M. M., Kobbelt, L. P., & Solomon, J. (2018) · Computer Graphics Forum · doi:10.1111/cgf.13486 · Cited by 28
Continuous Geodesic Convolutions for Learning on 3D Shapes
Yang, Z., Litany, O., Birdal, T., Sridhar, S., & Guibas, L. J. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00018 · Cited by 28
Metadata
- Authors
- Kim, V. G., Lipman, Y., & Funkhouser, T.
- Affiliations
- Princeton University (US)
- ORCID
- Kim, Vladimir G.
- Venue
- ACM Transactions on Graphics
- Published
- 2011-07
- Volume
- 30
- Issue
- 4
- Pages
- 1-12
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W4241422529
- Cited by
- 298
- References
- 27
- Citations by year
- 2026: 13 · 2025: 16 · 2024: 19 · 2023: 16 · 2022: 22 · 2021: 10 · 2020: 21 · 2019: 27 · 2018: 38 · 2017: 27 · 2016: 29 · 2015: 14 · 2014: 16 · 2013: 17 · 2012: 12
- FWCI
- 14.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Graphics (publishedVersion)
- Funders
- Air Force Office of Scientific Research (1096101)
- Division of Computer and Network Systems (IIS-0612231CNS-0831374CCF-0702672CCF-0937139)
- Division of Computing and Communication Foundations (IIS-0612231CNS-0831374CCF-0702672CCF-0937139)
- Division of Information and Intelligent Systems (IIS-0612231CNS-0831374CCF-0702672CCF-0937139)
- 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 › Computer Graphics and Visualization Techniques
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computational Geometry and Mesh Generation
- Keywords
- surface registration, genus zero surfaces, conformal mapping, area preservation, global optimization
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review