On efficient 3D object retrieval

Due to the growth of the 3D technology, digital 3D models represented in the form of point clouds have attracted a lot of attention from both industry and academia. In this paper, due to a variety of applications, we study a fundamental problem called the 3D object retrieval, which is to find a set of 3D point clouds stored in a database that are similar to a given query 3D point cloud. To the best of our knowledge, solving the problem of 3D object retrieval efficiently remains unexplored in the research community. In this paper, we propose a framework called C \(_2\) $_2^{}$ O to find the answer efficiently with the help of an index built on the database. In most of our experiments, C \(_2\) $_2^{}$ O performs up to 2 orders of magnitude faster than all adapted algorithms in the literature. In particular, when the database size scales up to 100 million points, C \(_2\) $_2^{}$ O answers the 3D object retrieval within 10 s but all adapted exact algorithms need more than 1000 s.