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
Flagged first, then by citations
Cited by
Sorted by citations
Items 101–200 of 297
ResNet-LDDMM: Advancing the LDDMM Framework using Deep Residual Networks
Amor, B. B., Arguillère, S., & Shao, L. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3174908 · Cited by 28
Q-Match: Iterative Shape Matching via Quantum Annealing
Benkner, M. S., Lähner, Z., Golyanik, V., Wunderlich, C., Theobalt, C., & Moeller, M. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00749 · Cited by 29
A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching
Roetzer, P., Swoboda, P., Cremers, D., & Bernard, F. (2022) · 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/cvpr52688.2022.00052 · Cited by 28
Local deep feature learning framework for 3D shape
Bu, S., Han, P., Liu, Z., Han, J., & Lin, H. (2014) · Computers & Graphics · doi:10.1016/j.cag.2014.09.007 · Cited by 27
Regularized Pointwise Map Recovery from Functional Correspondence
Rodolà, E., Moeller, M., & Cremers, D. (2017) · Computer Graphics Forum · doi:10.1111/cgf.13160 · Cited by 26
Dirichlet Energy for Analysis and Synthesis of Soft Maps
Solomon, J., Guibas, L. J., & Butscher, A. (2013) · Computer Graphics Forum · doi:10.1111/cgf.12186 · Cited by 25
Partial 3‐D Correspondence from Shape Extremities
Sahillioğlu, Y., & Yemez, Y. (2014) · Computer Graphics Forum · doi:10.1111/cgf.12278 · Cited by 24
Robust Structure‐Based Shape Correspondence
Kleiman, Y., & Ovsjanikov, M. (2018) · Computer Graphics Forum · doi:10.1111/cgf.13389 · Cited by 25
Deformable registration using patch-wise shape matching
Bonarrigo, F., Signoroni, A., & Botsch, M. (2014) · Graphical Models · doi:10.1016/j.gmod.2014.04.004 · Cited by 24
Fast and Accurate Intrinsic Symmetry Detection
Nagar, R., & Raman, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_26 · Cited by 24
Understanding and Improving Features Learned in Deep Functional Maps
Attaiki, S., & Ovsjanikov, M. (2023) · 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/cvpr52729.2023.00133 · Cited by 24
Animation Setup Transfer for 3D Characters
Avril, Q., Ghafourzadeh, D., Ramachandran, S., Fallahdoust, S., Ribet, S., Dionne, O., de Lasa, M., & Paquette, E. (2016) · Computer Graphics Forum · doi:10.1111/cgf.12816 · Cited by 24
Analysis and Visualization of Maps Between Shapes
Ovsjanikov, M., Ben‐Chen, M., Chazal, F., & Guibas, L. J. (2013) · Computer Graphics Forum · doi:10.1111/cgf.12076 · Cited by 22
Discrete Time Evolution Process Descriptor for Shape Analysis and Matching
Melzi, S., Ovsjanikov, M., Roffo, G., Cristani, M., & Castellani, U. (2018) · ACM Transactions on Graphics · doi:10.1145/3144454 · Cited by 21
Geometrically Principled Connections in Graph Neural Networks
Gong, S., Bahri, M., Bronstein, M. M., & Zafeiriou, S. P. (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/cvpr42600.2020.01143 · Cited by 21
Pairwise Harmonics for Shape Analysis
Zheng, Y., Tai, C., Zhang, E., & Xu, P. (2012) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.309 · Cited by 21
Wavelet‐based Heat Kernel Derivatives: Towards Informative Localized Shape Analysis
Kirgo, M., Melzi, S., Patanè, G., Rodolà, E., & Ovsjanikov, M. (2020) · Computer Graphics Forum · doi:10.1111/cgf.14180 · Cited by 20
Impact ofPICALMandCLUon hippocampal degeneration
Yang, X., Li, J., Liu, B., Li, Y., & Jiang, T. (2016) · Human Brain Mapping · doi:10.1002/hbm.23183 · Cited by 22
Group Representation of Global Intrinsic Symmetries
Wang, H., & Huang, H. F. (2017) · Computer Graphics Forum · doi:10.1111/cgf.13271 · Cited by 20
GFrames: Gradient-Based Local Reference Frame for 3D Shape Matching
Melzi, S., Spezialetti, R., Tombari, F., Bronstein, M. M., Stefano, L. D., & Rodolà, E. (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.2019.00476 · Cited by 20
Error-bounded compatible remeshing
Yang, Y., Zhang, W., Liu, Y., Liu, L., & Fu, X. (2020) · ACM Transactions on Graphics · doi:10.1145/3386569.3392434 · Cited by 20
Symmetry-Aware Nonrigid Matching of Incomplete 3D Surfaces
Yoshiyasu, Y., Yoshida, E., Yokoi, K., & Sagawa, R. (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.2014.534 · Cited by 20
Structure-oriented networks of shape collections
Fish, N., van Kaick, O. M., Bermano, A. H., & Cohen‐Or, D. (2016) · ACM Transactions on Graphics · doi:10.1145/2980179.2982409 · Cited by 19
Simulated Annealing for 3D Shape Correspondence
Holzschuh, B., Lähner, Z., & Cremers, D. (2020) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv50981.2020.00035 · Cited by 19
Efficient deformable shape correspondence via multiscale spectral manifold wavelets preservation
Hu, L., Li, Q., Liu, S., & Liu, X. (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/cvpr46437.2021.01430 · Cited by 19
Implicit Field Supervision for Robust Non-rigid Shape Matching
Sundararaman, R., Pai, G., & Ovsjanikov, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20062-5_20 · Cited by 19
A 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
A Robust Local Spectral Descriptor for Matching Non-Rigid Shapes With Incompatible Shape Structures
Wang, Y., Guo, J., Dong-ming Yan, A., Wang, K., & Zhang, X. (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.2019.00639 · Cited by 19
Non‐Isometric Shape Matching via Functional Maps on Landmark‐Adapted Bases
Panine, M., Kirgo, M., & Ovsjanikov, M. (2022) · Computer Graphics Forum · doi:10.1111/cgf.14579 · Cited by 19
Lai, R., & Hongkai Zhao, C. (2017) · SIAM Journal on Imaging Sciences · doi:10.1137/16m1068827 · Cited by 19
Spectral Meets Spatial: Harmonising 3D Shape Matching and Interpolation
Cao, D., Eisenberger, M., Amrani, N. E., Cremers, D., & Bernard, F. (2024) · 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/cvpr52733.2024.00351 · Cited by 18
A Mixture Model for Robust Point Matching under Multi-Layer Motion
Ma, J., Chen, J., Ming, D., & Tian, J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0092282 · Cited by 18
Evaluating the Automated Alignment of 3D Human Body Scans
Hirshberg, D. A., Loper, M., Rachlin, E., Tsoli, A., Weiss, A., Corner, B. D., & Black, M. J. (2011) · International Conference on 3D Body Scanning Technologies (3DBST) · doi:10.15221/11.076 · Cited by 18
Spectral Shape Recovery and Analysis Via Data-driven Connections
Marin, R., Rampini, A., Castellani, U., Rodolà, E., Ovsjanikov, M., & Melzi, S. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01492-6 · Cited by 21
Learning of Planning Models for Dexterous Manipulation Based on Human Demonstrations
Jäkel, R., Schmidt-Rohr, S. R., Ruehl, S. W., Kasper, A., Xue, Z., & Dillmann, R. (2012) · International Journal of Social Robotics · doi:10.1007/s12369-012-0162-y · Cited by 17
Compatible intrinsic triangulations
Takayama, K. (2022) · ACM Transactions on Graphics · doi:10.1145/3528223.3530175 · Cited by 16
A low-dimensional representation for robust partial isometric correspondences computation
Brunton, A., Wand, M., Wuhrer, S., Seidel, H., & Weinkauf, T. (2013) · Graphical Models · doi:10.1016/j.gmod.2013.11.003 · Cited by 16
Spatial Maps: From Low Rank Spectral to Sparse Spatial Functional Representations
Gasparetto, A., Cosmo, L., Rodolà, E., Bronstein, M., & Torsello, A. (2017) · International Conference on 3D Vision · doi:10.1109/3dv.2017.00061 · Cited by 16
Geometric Deep Learning for Shape Correspondence in Mass Customization by Three-Dimensional Printing
Huang, J., Sun, H., Kwok, T.-H., Zhou, C., & Xu, W. (2020) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.4046746 · Cited by 16
Multiple Shape Correspondence by Dynamic Programming
Sahillioğlu, Y., & Yemez, Y. (2014) · Computer Graphics Forum · doi:10.1111/cgf.12480 · Cited by 17
Spatial feature mapping for 6DoF object pose estimation
Mei, J., Jiang, X., & Ding, H. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108835 · Cited by 15
Non-Rigid Registration Via Intelligent Adaptive Feedback Control
Tajdari, F., Huysmans, T., & Song, Y. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3283990 · Cited by 15
NICP: Neural ICP for 3D Human Registration at Scale
Marin, R., Corona, E., & Pons‐Moll, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73636-0_16 · Cited by 21
Vector Field Map Representation for Near Conformal Surface Correspondence
Wang, Y., Liu, B., Zhou, K., & Tong, Y. (2017) · Computer Graphics Forum · doi:10.1111/cgf.13312 · Cited by 15
Anisotropic Spectral Manifold Wavelet Descriptor
Li, Q., Hu, L., Liu, S., Yang, D., & Liu, X. (2020) · Computer Graphics Forum · doi:10.1111/cgf.14120 · Cited by 15
A Low-dimensional Representation for Robust Partial Isometric Correspondences Computation
Brunton, A., Wand, M., Wuhrer, S., Seidel, H., & Weinkauf, T. (2014) · Max Planck Digital Library · Cited by 15
Deep Neural Network for 3D Shape Classification Based on Mesh Feature
Gao, M., Ruan, N., Shi, J., & Zhou, W. (2022) · Sensors · doi:10.3390/s22187040 · Cited by 14
Xie, J., Hsu, Y.-F., Feris, R., & Sun, M. (2015) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.08.007 · Cited by 14
Sun, Z., He, Y., Gritsenko, A., Lendasse, A., & Baek, S. (2020) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwaa003 · Cited by 26
Higher-Order Graph Principles towards Non-Rigid Surface Registration
Zeng, Y., Wang, C., Gu, X. D., Samaras, D., & Nikos Paragios, A. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2528240 · Cited by 13
Quadrangulation of non-rigid objects using deformation metrics
Zhou, J., Campen, M., Zorin, D., Tu, C., & Silva, C. T. (2018) · Computer Aided Geometric Design · doi:10.1016/j.cagd.2018.03.003 · Cited by 12
Joint Map and Symmetry Synchronization
Sun, Y., Liang, Z., Huang, X., & Qi-xing Huang, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01228-1_16 · Cited by 12
WTFM Layer: An Effective Map Extractor for Unsupervised Shape Correspondence
Liu, S., Xu, H., Dong-ming Yan, A., Hu, L., Liu, X., & Li, Q. (2022) · Computer Graphics Forum · doi:10.1111/cgf.14656 · Cited by 12
Tensor maps for synchronizing heterogeneous shape collections
Qi-xing Huang, A., Liang, Z., Wang, H., Zuo, S., & Bajaj, C. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3322944 · Cited by 12
Template-based surface reconstruction from cross-sections
Holloway, M., Grimm, C. M., & Ju, T. (2016) · Computers & Graphics · doi:10.1016/j.cag.2016.05.012 · Cited by 12
Symmetry aware embedding for shape correspondence
Yoshiyasu, Y., Yoshida, E., & Guibas, L. J. (2016) · Computers & Graphics · doi:10.1016/j.cag.2016.07.002 · Cited by 12
Correspondence estimation for non-rigid point clouds with automatic part discovery
Guo, H., Zhu, D.-H., & Mordohai, P. (2016) · The Visual Computer · doi:10.1007/s00371-015-1136-5 · Cited by 12
Spectral Descriptors for 3D Deformable Shape Matching: A Comparative Survey
Liu, S., Wang, H., Dong-ming Yan, A., Li, Q., Luo, F., Teng, Z., & Liu, X. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3368083 · Cited by 11
Geometrically Consistent Partial Shape Matching
Ehm, V., Roetzer, P., Eisenberger, M., Gao, M., Bernard, F., & Cremers, D. (2024) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv62453.2024.00062 · Cited by 11
CCuantuMM: Cycle-Consistent Quantum-Hybrid Matching of Multiple Shapes
Bhatia, H., Tretschk, E., Lähner, Z., Benkner, M. S., Moeller, M., Theobalt, C., & Golyanik, V. (2023) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52729.2023.00131 · Cited by 17
ΣIGMA: Scale-Invariant Global Sparse Shape Matching
Gao, M., Roetzer, P., Eisenberger, M., Lähner, Z., Moeller, M., Cremers, D., & Bernard, F. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00066 · Cited by 11
Symmetry Robust Descriptor for Non‐Rigid Surface Matching
Zhang, Z., Yin, K., & Foong, K. W. C. (2013) · Computer Graphics Forum · doi:10.1111/cgf.12243 · Cited by 11
Shift-invariant ring feature for 3D shape
Bu, S., Han, P., Liu, Z., Li, K., & Han, J. (2014) · The Visual Computer · doi:10.1007/s00371-014-0970-1 · Cited by 11
Zell, E., & Botsch, M. (2013) · doi:10.1145/2486042.2486045 · Cited by 11
Nonisometric Surface Registration via Conformal Laplace–Beltrami Basis Pursuit
Schonsheck, S. C., Bronstein, M. M., & Lai, R. (2021) · Journal of Scientific Computing · doi:10.1007/s10915-020-01390-y · Cited by 11
Zeng, Y., Wang, C., Gu, X. D., Samaras, D., & Nikos Paragios, A. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.417 · Cited by 11
Topology Constrained Shape Correspondence
Xiang, L., Wen, C., Wang, L., & Fang, Y. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.2994013 · Cited by 11
Partial-to-Partial Shape Matching with Geometric Consistency
Ehm, V., Gao, M., Roetzer, P., Eisenberger, M., Cremers, D., & Bernard, F. (2024) · 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/cvpr52733.2024.02595 · Cited by 10
A Unified Deep Metric Representation for Mesh Saliency Detection and Non-Rigid Shape Matching
Hu, S., Shum, H. P. H., Aslam, N., Li, F. W. B., & Liang, X. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2952983 · Cited by 10
Properly constrained orthonormal functional maps for intrinsic symmetries
Liu, X., Li, S., Liu, R., Wang, J., Wang, H., & Cao, J. (2014) · Computers & Graphics · doi:10.1016/j.cag.2014.09.016 · Cited by 10
Partial correspondence of 3D shapes using properties of the nearest-neighbor field
Arbel, N., Tal, A., & Zelnik‐Manor, L. (2019) · Computers & Graphics · doi:10.1016/j.cag.2019.05.011 · Cited by 10
Wu, Y., Yang, J., & Zhao, J.-L. (2020) · Computers & graphics · doi:10.1016/j.cag.2020.09.004 · Cited by 13
Dense Point‐to‐Point Correspondences Between Genus‐Zero Shapes
Lee, S. C., & Kazhdan, M. (2019) · Computer graphics forum (Print) · doi:10.1111/cgf.13787 · Cited by 15
Extracting a functional representation from a dictionary for non-rigid shape matching
Colombo, M., Boracchi, G., & Melzi, S. (2023) · Computers & Graphics · doi:10.1016/j.cag.2023.04.010 · Cited by 9
Le, T., Nguyen, K., Sun, S., Ho, N., & Xie, X. S. (2024) · 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/cvpr52733.2024.02188 · Cited by 9
Geng, H., Fan, J., Yang, S., Chen, S., Xiao, D., Ai, D., Fu, T., Song, H., Yuan, K., Duan, F., et al. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3406876 · Cited by 9
Attention And Positional Encoding Are (Almost) All You Need For Shape Matching
Raganato, A., Pasi, G., & Melzi, S. (2023) · Computer Graphics Forum · doi:10.1111/cgf.14912 · Cited by 12
Deformation Recovery: Localized Learning for Detail-Preserving Deformations
Sundararaman, R., Donati, N., Melzi, S., Corman, É., & Ovsjanikov, M. (2024) · ACM Transactions on Graphics · doi:10.1145/3687968 · Cited by 8
Augmented Paths and Reodesics for Topologically-Stable Matching
Sahillioğlu, Y., & Horsman, D. (2022) · ACM Transactions on Graphics · doi:10.1145/3554978 · Cited by 8
A methodology for shape matching of non-rigid structures based on integrated graphical information
Zhang, M., Hassan, M. U., Niu, D., Zhao, X., Nawaz, R., Hameed, I. A., & Hassan, S. (2022) · Displays · doi:10.1016/j.displa.2022.102336 · Cited by 8
Revisiting Map Relations for Unsupervised Non-Rigid Shape Matching
Cao, D., Roetzer, P., & Bernard, F. (2024) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv62453.2024.00128 · Cited by 8
DiscoMatch: Fast Discrete Optimisation for Geometrically Consistent 3D Shape Matching
Roetzer, P., Abbas, A. R., Cao, D., Bernard, F., & Swoboda, P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73668-1_26 · Cited by 8
Carra, E., & Pellacini, F. (2019) · ACM Transactions on Graphics · doi:10.1145/3355089.3356550 · Cited by 8
SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondence
Li, L., Attaiki, S., & Ovsjanikov, M. (2022) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv57658.2022.00027 · Cited by 8
Linear variational principle for Riemann mappings and discrete conformality
Dym, N., Slutsky, R., & Lipman, Y. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1809731116 · Cited by 8
Self-citationConsistent Shape Matching via Coupled Optimization
Azencot, O., Dubrovina, A., & Guibas, L. J. (2019) · Computer Graphics Forum · doi:10.1111/cgf.13786 · Cited by 8
Synchronous Diffusion for Unsupervised Smooth Non-rigid 3D Shape Matching
Cao, D., Lähner, Z., & Bernard, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72652-1_16 · Cited by 7
Generalizable Local Feature Pre-training for Deformable Shape Analysis
Attaiki, S., Li, L., & Ovsjanikov, M. (2023) · 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/cvpr52729.2023.01312 · Cited by 7
4D Human Body Correspondences from Panoramic Depth Maps
Li, Z., Wu, M., Zhou, W., & Jingyi Yu, 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.2018.00304 · Cited by 7
Learning-Based Intrinsic Reflectional Symmetry Detection
Qiao, Y.-L., Gao, L., Liu, S., Liu, L., Lai, Y., & Chen, X. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3172361 · Cited by 7
AWEDD: a descriptor simultaneously encoding multiscale extrinsic and intrinsic shape features
Liu, S., Luo, F., Li, Q., Liu, X., & Hu, L. (2023) · The Visual Computer · doi:10.1007/s00371-023-02935-6 · Cited by 7
3D skeleton transfer for meshes and clouds
Seylan, Ç., & Sahillioğlu, Y. (2019) · Graphical Models · doi:10.1016/j.gmod.2019.101041 · Cited by 7
High-Resolution Augmentation for Automatic Template-Based Matching of Human Models
Marin, R., Melzi, S., Rodolà, E., & Castellani, U. (2019) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2019.00034 · Cited by 7
Robust Shape Collection Matching and Correspondence from Shape Differences
Cohen, A., & Ben‐Chen, M. (2020) · Computer Graphics Forum · doi:10.1111/cgf.13952 · Cited by 7
Geodesic spin contour for partial near-isometric matching
Liang, L., Szymczak, A., & Wei, M. (2014) · Computers & Graphics · doi:10.1016/j.cag.2014.09.024 · Cited by 7
Locality Preserving Refinement for Shape Matching with Functional Maps
Xia, Y., Lu, Y., Gao, Y., & Ma, J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i6.28438 · Cited by 6
The hitchhiker's guide to the galaxy of mathematical tools for shape analysis
Biasotti, S., Falcidieno, B., Giorgi, D., & Spagnuolo, M. (2012) · ACM SIGGRAPH Courses · doi:10.1145/2343483.2343499 · Cited by 6
Continuous semantic description of 3D meshes
Léon, V., Bonneel, N., Lavoué, G., & Vandeborre, J. (2015) · Computers & Graphics · doi:10.1016/j.cag.2015.07.018 · Cited by 6
A parametric analysis of discrete Hamiltonian functional maps
Postolache, E., Fumero, M., Cosmo, L., & Rodolà, E. (2020) · Computer Graphics Forum · doi:10.1111/cgf.14072 · Cited by 6
Deep generation of 3D articulated models and animations from 2D stick figures
Akman, A., Sahillioğlu, Y., & Sezgin, T. M. (2022) · Computers & Graphics · doi:10.1016/j.cag.2022.10.004 · Cited by 6
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
