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This paper presents a novel randomized algorithm for robust point cloudregistration without correspondences. Most existing registration approachesrequire a set of putative correspondences obtained by extracting invariantdescriptors. However, such descriptors could become unreliable in noisy andcontaminated settings. In these settings, methods that directly handle inputpoint sets are preferable. Without correspondences, however, conventionalrandomized techniques require a very large number of samples in order to reachsatisfactory solutions. In this paper, we propose a novel approach to addressthis problem. In particular, our work enables the use of randomized methods forpoint cloud registration without the need of putative correspondences. Byconsidering point cloud alignment as a special instance of graph matching andemploying an efficient semi-definite relaxation, we propose a novel samplingmechanism, in which the size of the sampled subsets can be larger-than-minimal.Our tight relaxation scheme enables fast rejection of the outliers in thesampled sets, resulting in high-quality hypotheses. We conduct extensiveexperiments to demonstrate that our approach outperforms other state-of-the-artmethods. Importantly, our proposed method serves as a generic framework whichcan be extended to problems with known correspondences.

SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences