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Accurate and Scalable Surface Representation and Reconstruction from Images

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dc.contributor.author Zeng, Gang
dc.contributor.author Paris, Sylvain
dc.contributor.author Quan, Long
dc.contributor.author Sillion, Francois
dc.contributor.other Computer Graphics
dc.date.accessioned 2005-12-22T02:41:10Z
dc.date.available 2005-12-22T02:41:10Z
dc.date.issued 2005-11-18
dc.identifier.other MIT-CSAIL-TR-2005-076
dc.identifier.other MIT-LCS-TR-1011
dc.identifier.uri http://hdl.handle.net/1721.1/30586
dc.description.abstract We introduce a new surface representation, the patchwork, to extend the problem of surface reconstruction from multiple images. A patchwork is the combination of several patches that are built one by one. This design potentially allows the reconstruction of an object of arbitrarily large dimensions while preserving a fine level of detail. We formally demonstrate that this strategy leads to a spatial complexity independent of the dimensions of the reconstructed object, and to a time complexity linear with respect to the object area. The former property ensures that we never run out of storage (memory) and the latter means that reconstructing an object can be done in a reasonable amount of time. In addition, we show that the patchwork representation handles equivalently open and closed surfaces whereas most of the existing approaches are limited to a specific scenario (open or closed surface but not both).Most of the existing optimization techniques can be cast into this framework. To illustrate the possibilities offered by this approach, we propose two applications that expose how it dramatically extends a recent accurate graph-cut technique. We first revisit the popular carving techniques. This results in a well-posed reconstruction problem that still enjoys the tractability of voxel space. We also show how we can advantageously combine several image-driven criteria to achieve a finely detailed geometry by surface propagation. The above properties of the patchwork representation and reconstruction are extensively demonstrated on real image sequences.
dc.format.extent 35 p.
dc.format.extent 126753884 bytes
dc.format.extent 4493080 bytes
dc.format.mimetype application/postscript
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.relation.ispartofseries Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
dc.title Accurate and Scalable Surface Representation and Reconstruction from Images


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