Discovering structural regularity in 3D geometry
We introduce a computational framework for discovering regular or repeated geometric structures in 3D shapes. We describe and classify possible regular structures and present an effective algorithm for detecting such repeated geometric patterns in point- or meshbased models. Our method assumes no prior knowledge of the geometry or spatial location of the individual elements that define the pattern. Structure discovery is made possible by a careful analysis of pairwise similarity transformations that reveals prominent lattice structures in a suitable model of transformation space. We introduce an optimization method for detecting such uniform grids specifically designed to deal with outliers and missing elements. This yields a robust algorithm that successfully discovers complex regular structures amidst clutter, noise, and missing geometry. The accuracy of the extracted generating transformations is further improved using a novel simultaneous registration method in the spatial domain. We demonstrate the effectiveness of our algorithm on a variety of examples and show applications to compression, model repair, and geometry synthesis. © 2008 ACM.
2008_Discovering.png
Thumbnail
openaccess
copyright
74.53 KB
PNG
3f59a19bebb28bc4100d93ef9fa422f2
pauly_2008_DSR.pdf
openaccess
10.95 MB
Adobe PDF
b861acc2fcd5a28b8d2f4ce291d7bcbe