期刊
ACM TRANSACTIONS ON GRAPHICS
卷 27, 期 3, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/1360612.1360642
关键词
regular structure; repetitive pattern; transformation group; shape analysis; similarity transformation
资金
- NIGMS NIH HHS [U54 GM072970, U54 GM072970-05] Funding Source: Medline
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 mesh-based 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.
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