A fast all nearest neighbor algorithm for applications involving large point-clouds

13 years 4 months ago

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Algorithms that use point-cloud models make heavy use of the neighborhoods of the points. These neighborhoods are used to compute the surface normals for each point, molliﬁcation, and noise removal. All of these primitive operations require the seemingly repetitive process of ﬁnding the k nearest neighbors (kNNs) of each point. These algorithms are primarily designed to run in main memory. However, rapid advances in scanning technologies have made available point-cloud models that are too large to ﬁt in the main memory of a computer. This calls for more efﬁcient methods of computing the kNNs of a large collection of points many of which are already in close proximity. A fast kNN algorithm is presented that makes use of the locality of successive points whose k nearest neighbors are sought to reduce signiﬁcantly the time needed to compute the neighborhood needed for the primitive operation as well as enable it to operate in an environment where the data is on disk. Results of...

Jagan Sankaranarayanan, Hanan Samet, Amitabh Varsh

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