Closest-point problems simplified on the RAM
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
How to cover a point set with a V-shape of minimum width
WADS'11 Proceedings of the 12th international conference on Algorithms and data structures
Surface reconstruction technology from dense scattered points based on grid
HPCA'09 Proceedings of the Second international conference on High Performance Computing and Applications
How to cover a point set with a V-shape of minimum width
Computational Geometry: Theory and Applications
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We use projective clustering to design and implement a fast surface reconstruction algorithm for point clouds that also works well for sharp edges and corners. Our method relies on two new approximation algorithms developed and implemented for the first time, namely, fast projective clustering and parallel dynamic nearest neighbor searching based on shifted quad-trees. Also, our implementation is one of the first for this problem with any kind of guarantees (for a very restricted type of manifolds). Our algorithm is easy to parallelize and is external-memory friendly. Finally we provide a method for combining increasingly more complex fitters in a cascade which allows planar regions of the point cloud to be quickly processed while spending more time on high curvature areas including sharp features. In the domain of normal estimation, our method is faster and more accurate than previous systems on a large number of point clouds.