Given an input point cloud P in R³, this paper proposes a novel algorithm to identify surface neighbors of each point p Î P respecting the underlying surface S, and then to construct a piecewise linear surface for P. The algorithm utilizes the simple k-nearest neighborhood in constructing local surfaces. It makes use of two concepts: a local convexity criterion to extract a set of surface neighbors for each point, and a global projection test to determine an order for the reconstruction. Our algorithm not only produces a topologically correct surface for well-sampled point sets, but also adapts well to handle under-sampled point sets. Furthermore, the computational cost of the algorithm increases almost linearly in the size of the point cloud. It thus scales well to deal with large input point sets.

We would be happy to receive comments or bugs reports etc. on the use of this software.

Executable (version 1.01, 30 March 2007): console version (0.97M), and MFC version (0.94M)                           

Acknowledgements This research is supported by the National University of Singapore under grants R-252-000-216-112 and R-252-000-254-112.

The authors can be contacted at: limchiwa @ gmail.com,  and   tants @ comp.nus.edu.sg

©3 December 2006, 30 March 2007, Computer Graphics Research Laboratory,  School of Computing, National University of Singapore.