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 efficient 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.

本文提出了一种无需对应的鲁棒点云配准随机算法，并将其视为图匹配的特例，通过使用有效的半定松弛和新颖的采样机制，在采样大小大于最小值的前提下，实现了对野值的快速拒绝，从而获得高质量的假设。通过实验，证明了该方法的有效性。