期刊
ACM TRANSACTIONS ON GRAPHICS
卷 29, 期 5, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/1857907.1857911
关键词
Algorithms; Point set surfaces; surface reconstruction; sparse signal reconstruction
资金
- Israel Science Foundation
- Natural Sciences and Engineering Council of Canada
We introduce an l(1)-sparse method for the reconstruction of a piecewise smooth point set surface. The technique is motivated by recent advancements in sparse signal reconstruction. The assumption underlying our work is that common objects, even geometrically complex ones, can typically be characterized by a rather small number of features. This, in turn, naturally lends itself to incorporating the powerful notion of sparsity into the model. The sparse reconstruction principle gives rise to a reconstructed point set surface that consists mainly of smooth modes, with the residual of the objective function strongly concentrated near sharp features. Our technique is capable of recovering orientation and positions of highly noisy point sets. The global nature of the optimization yields a sparse solution and avoids local minima. Using an interior-point log-barrier solver with a customized preconditioning scheme, the solver for the corresponding convex optimization problem is competitive and the results are of high quality.
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