Zhiyang Huang
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We propose a new method for reconstructing an implicit surface from an un-oriented point set. While existing methods often involve non-trivial heuristics and require additional constraints, such as normals or labelled points, we introduce a direct definition of the function from the points as the solution to a constrained quadratic optimization problem. The definition has a number of appealing features: it uses a single parameter (parameter-free for exact interpolation), applies to any dimensions, commutes with similarity transformations, and can be easily implemented without discretizing the space. More importantly, the use of a global smoothness energy allows our definition to be much more resilient to sampling imperfections than existing methods, making it particularly suited for sparse and non-uniform inputs.
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This code implements the algorithm described in Variational Implicit Point Set Surface by Zhiyang Huang, Nathan Carr, and Tao Ju. ACM Transactions on Graphics (Proc. ACM SIGGRAPH 2019).The primary function of this program is to predict the normal as well as the underlying surface of a given set of unoriented points. Currently, the code is only tested on Mac OS X 10.13 or above, it should work at other platforms with minor modification.The code is also available on GitHub: https://github.com/adshhzy/VIPSS
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Index Terms
- Variational implicit point set surfaces
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