Multi-view point cloud registration using affine shape distributions

Abstract

Registration is crucial for the reconstruction of multi-view single plane illumination microscopy. By using fluorescent beads as fiduciary markers, this registration problem can be reduced to the problem of point clouds registration. We present a novel method for registering point clouds across views. This is based on a new local geometric descriptor - affine shape distribution - to represent the random spatial pattern of each point and its neighbourhood. To enhance its robustness and discriminative power against the missing data and outliers, a permutation and voting scheme based on affine shape distributions is developed to establish putative correspondence pairs across views. The underlying affine transformations are estimated based on the putative correspondence pairs via the random sample consensus. The proposed method is evaluated on three types of datasets including 3D random points, benchmark datasets and datasets from multi-view microscopy. Experiments show that the proposed method outperforms the state-of-the-arts when both point sets are contaminated by extremely large amount of outliers. Its robustness against the anisotropic z-stretching is also demonstrated in the registration of multi-view microscopy data.