Indexing and searching point clouds

Abstract

Many multimedia objects accept an abstract representation as point sets, or point clouds , in the plane. Searching for objects in a collection is traduced to searching for matching point clouds. In this paper algorithms and data structures are given for indexing and searching point clouds. The indexes are implemented using off-the-shelf, popular, software components. Experimental tests were performed on large databases, including a synthetic database of 10 million point clouds (1000 points per cloud) and the MIR Flickr-1M database, which contains 1 million high-resolution images. The performance of the proposed indexes was evaluated according to: Average search time, construction time, recall@k, memory usage and performance under insertions and deletions. A thorough comparison was performed between the fastest method available in the literature and a repertoire of implementations. The most competitive index is three orders of magnitude faster than the state of the art, in the image database, with recall @ 1 ≥0.989 for 20% insertions and deletions.