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Screened poisson surface reconstruction
Open accessAbstract
Poisson surface reconstruction creates watertight surfaces from oriented point sets. In this work we extend the technique to explicitly incorporate the points as interpolation constraints. The extension can be interpreted as a generalization of the underlying mathematical framework to a screened Poisson equation. In contrast to other image and geometry processing techniques, the screening term is defined over a sparse set of points rather than over the full domain. We show that these sparse constraints can nonetheless be integrated efficiently. Because the modified linear system retains the same finite-element discretization, the sparsity structure is unchanged, and the system can still be solved using a multigrid approach. Moreover we present several algorithmic improvements that together reduce the time complexity of the solver to linear in the number of points, thereby enabling faster, higher-quality surface reconstructions.
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Cites
Reconstruction and representation of 3D objects with radial basis functions
Carr, J. C., Beatson, R. K., Cherrie, J. B., Mitchell, T. J., Fright, W. R., McCallum, B. C., & Evans, T. R. (2001) · Proceedings of the 28th annual conference on Computer graphics and interactive techniques · doi:10.1145/383259.383266 · Cited by 1,852
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All-optical machine learning using diffractive deep neural networks
Lin, X., Rivenson, Y., Yardimci, N. T., Veli, M., Luo, Y., Jarrahi, M., & Ozcan, A. (2018) · Science · doi:10.1126/science.aat8084 · Cited by 2,568
MVSNet: Depth Inference for Unstructured Multi-view Stereo
Yao, Y., Luo, Z., Li, S., Fang, T., & Quan, L. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01237-3_47 · Cited by 1,455
A Multi-view Stereo Benchmark with High-Resolution Images and Multi-camera Videos
Schops, T., Schonberger, J. L., Galliani, S., Sattler, T., Schindler, K., Pollefeys, M., & Geiger, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.272 · Cited by 915
Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction
Chabra, R., Lenssen, J. E., Ilg, E., Schmidt, T., Straub, J., Lovegrove, S., & Newcombe, R. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58526-6_36 · Cited by 380
Metadata
- Authors
- Kazhdan, M., & Hoppe, H.
- Venue
- ACM Transactions on Graphics
- Published
- 2013-06
- Volume
- 32
- Issue
- 3
- Pages
- 1-13
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W1992642990
- Cited by
- 2,231
- References
- 1
- FWCI
- 63.49
- Type
- journal article
- Language
- en
- Open access
- green
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