An efficient algorithm for feature-based 3D point cloud correspondence search

Abstract

Searching correspondences between 3D point Clouds is computationally expensive for two reasons: the complexity of geometricbased feature extraction operations and the large search space. To tackle this challenging problem, we propose a novel and efficient 3D point cloud matching algorithm. Our algorithm is inspired by PatchMatch [1], which is designed for correspondence search between 2D images. However, PatchMatch relies on the natural scanline order of 2D images to propagate good solutions across the images, whereas such an order does not exist for 3D point clouds. Hence, unlike PatchMatch which conducts search at different pixels sequentially under the scanline order, our algorithm searches the best correspondences for different 3D points in parallel using a variant of the Artificial Bee Colony (ABC) [2] algorithm and propagates good solutions found at one point to its k-nearest neighbors. In addition, noticed that correspondences found using geometricbased features extracted at individual points alone can be prone to noise, we add a novel smooth term to the objective function. Experiments on multiple datasets show that the new smooth term can effectively suppress matching noises and the ABC-based parallel search can significantly reduce the computational time compared to brute-force search.