SDRSAC: Semidefinite-Based Randomized Approach for
Robust Point Cloud Registration without Correspondences
Abstract
This paper presents a novel randomized algorithm for
robust point cloud registration without correspondences.
Most existing registration approaches require a set of putative correspondences obtained by extracting invariant descriptors. However, such descriptors could become unreliable in noisy and contaminated settings. In these settings,
methods that directly handle input point sets are preferable.
Without correspondences, however, conventional randomized techniques require a very large number of samples in
order to reach satisfactory solutions. In this paper, we propose a novel approach to address this problem. In particular, our work enables the use of randomized methods for
point cloud registration without the need of putative correspondences. By considering point cloud alignment as a
special instance of graph matching and employing an effi-
cient semi-definite relaxation, we propose a novel sampling
mechanism, in which the size of the sampled subsets can be
larger-than-minimal. Our tight relaxation scheme enables
fast rejection of the outliers in the sampled sets, resulting
in high quality hypotheses. We conduct extensive experiments to demonstrate that our approach outperforms other
state-of-the-art methods. Importantly, our proposed method
serves as a generic framework which can be extended to
problems with known correspondences. 1