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Poisson surface reconstruction and its applications

Published: 02 June 2008 Publication History

Abstract

Surface reconstruction from oriented points can be cast as a spatial Poisson problem. This Poisson formulation considers all the points at once, without resorting to heuristic spatial partitioning or blending, and is therefore highly resilient to data noise. Unlike radial basis function schemes, the Poisson approach allows a hierarchy of locally supported basis functions, and therefore the solution reduces to a well conditioned sparse linear system. To reconstruct detailed models in limited memory, we solve this Poisson formulation efficiently using a streaming framework. Specifically, we introduce a multilevel streaming representation, which enables efficient traversal of a sparse octree by concurrently advancing through multiple streams, one per octree level. Remarkably, for our reconstruction application, a sufficiently accurate solution to the global linear system is obtained using a single iteration of cascadic multigrid, which can be evaluated within a single multi-stream pass. Finally, we explore the application of Poisson reconstruction to the setting of multi-view stereo, to reconstruct detailed 3D models of outdoor scenes from collections of Internet images.
This is joint work with Michael Kazhdan, Matthew Bolitho, and Randal Burns (Johns Hopkins University), and Michael Goesele, Noah Snavely, Brian Curless, and Steve Seitz (University of Washington).

References

[1]
M. Kazhdan, M. Bolitho, and H. Hoppe. Poisson surface reconstruction. Symposium on Geometry Processing 2006.
[2]
M. Bolitho, M. Kazhdan, R. Burns, H. Hoppe. Multilevel streaming for out-of-core surface reconstruction. Symposium on Geometry Processing 2007.
[3]
M. Goesele, N. Snavely, B. Curless, H. Hoppe, S. Seitz. Multi-view stereo for community photo collections. ICCV 2007.

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cover image ACM Conferences
SPM '08: Proceedings of the 2008 ACM symposium on Solid and physical modeling
June 2008
423 pages
ISBN:9781605581064
DOI:10.1145/1364901
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 June 2008

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