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The power crust
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Source ACM Symposium on Solid and Physical Modeling archive
Proceedings of the sixth ACM symposium on Solid modeling and applications table of contents
Ann Arbor, Michigan, United States
Pages: 249 - 266  
Year of Publication: 2001
ISBN:1-58113-366-9
Authors
Nina Amenta  University of Texas at Austin
Sunghee Choi  University of Texas at Austin
Ravi Krishna Kolluri  Computer Sciences Department, Austin, TX
Sponsor
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 13,   Downloads (12 Months): 91,   Citation Count: 60
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ABSTRACT

The power crust is a construction which takes a sample of points from the surface of a three-dimensional object and produces a surface mesh and an approximate medial axis. The approach is to first approximate the medial axis transform (MAT) of the object. We then use an inverse transform to produce the surface representation from the MAT.

This idea leads to a simple algorithm with theoretical guarantees comparable to those of other surface reconstruction and medial axis approximation algorithms. It also comes with a guarantee that does not depend in any way on the quality of the input point sample. Any input gives an output surface which is the `watertight' boundary of a three-dimensional polyhedral solid: the solid described by the approximate MAT. This unconditional guarantee makes the algorithm quite robust and eliminates the polygonalization, hole-filling or manifold extraction post-processing steps required in previous surface reconstruction algorithms.

In this paper, we use the theory to develop a power crust implementation which is indeed robust for realistic and even difficult samples. We describe the careful design of a key subroutine which labels parts of the MAT as inside or outside of the object, easy in theory but non-trivial in practice. We find that we can handle areas in which the input sampling is scanty or noisy by simply discarding the unreliable parts of the MAT approximation. We demonstrate good empirical results on inputs including models with sharp corners, sparse and unevenly distributed point samples, holes, and noise, both natural and synthetic.

We also demonstrate some simple extensions: intentionally leaving holes where there is no data, producing approximate offset surfaces, and simplifying the approximate MAT in a principled way to preserve stable features.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  60

Collaborative Colleagues:
Nina Amenta: colleagues
Sunghee Choi: colleagues
Ravi Krishna Kolluri: colleagues