The DOI matches its Crossref record; 3 of the 3 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.
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Cites
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
Möbius voting for surface correspondence
Lipman, Y., & Funkhouser, T. (2009) · ACM Transactions on Graphics · doi:10.1145/1531326.1531378 · Cited by 332
Self-citationOne 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
Cited by
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., Jiang, X., Fan, A., Jiang, J., & Yan, J. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01359-2 · Cited by 990
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
Ma, J., Zhao, J., Jiang, J., Zhou, H., & Guo, X. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-018-1117-z · Cited by 601
Metadata
- Authors
- Kim, V. G., Lipman, Y., & Funkhouser, T.
- Venue
- ACM SIGGRAPH 2011 papers
- Publisher
- ACM
- Published
- 2011-07-25
- Pages
- 1-12
- OpenAlex
- W2073431710
- Cited by
- 307
- References
- 3
- FWCI
- 41.19
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Kim, V. G., Lipman, Y., & Funkhouser, T. (2011). Blended intrinsic maps. ACM SIGGRAPH 2011 papers. https://doi.org/10.1145/1964921.1964974
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