Estimating the rigid transformation with 6 degrees of freedom based on a
putative 3D correspondence set is a crucial procedure in point cloud
registration. Existing correspondence identification methods usually lead to
large outlier ratios ($>$ 95 $\%$ is common), underscoring the significance of
robust registration methods. Many researchers turn to parameter search-based
strategies (e.g., Branch-and-Bround) for robust registration. Although related
methods show high robustness, their efficiency is limited to the
high-dimensional search space. This paper proposes a heuristics-guided
parameter search strategy to accelerate the search while maintaining high
robustness. We first sample some correspondences (i.e., heuristics) and then
just need to sequentially search the feasible regions that make each sample an
inlier. Our strategy largely reduces the search space and can guarantee
accuracy with only a few inlier samples, therefore enjoying an excellent
trade-off between efficiency and robustness. Since directly parameterizing the
6-dimensional nonlinear feasible region for efficient search is intractable, we
construct a three-stage decomposition pipeline to reparameterize the feasible
region, resulting in three lower-dimensional sub-problems that are easily
solvable via our strategy. Besides reducing the searching dimension, our
decomposition enables the leverage of 1-dimensional interval stabbing at all
three stages for searching acceleration. Moreover, we propose a valid sampling
strategy to guarantee our sampling effectiveness, and a compatibility
verification setup to further accelerate our search. Extensive experiments on
both simulated and real-world datasets demonstrate that our approach exhibits
comparable robustness with state-of-the-art methods while achieving a
significant efficiency boost.

通过启发式参数搜索策略，该论文提出了一种在点云配准中加速搜索而又保持高鲁棒性的方法，通过对可行域的三阶分解和一维区间刺探的利用，降低了搜索维度，并展示了与最先进方法相当的鲁棒性和显著的效率提升。

基于启发式引导的参数搜索的高效强鲁棒点云配准

Efficient and Robust Point Cloud Registration via Heuristics-guided  Parameter Search

Many machine learning methods assume that the training and test data follow
the same distribution. However, in the real world, this assumption is very
often violated. In particular, the phenomenon that the marginal distribution of
the data changes is called covariate shift, one of the most important research
topics in machine learning. We show that the well-known family of covariate
shift adaptation methods is unified in the framework of information geometry.
Furthermore, we show that parameter search for geometrically generalized
covariate shift adaptation method can be achieved efficiently. Numerical
experiments show that our generalization can achieve better performance than
the existing methods it encompasses.

本文研究了机器学习中的一个重要研究主题：协变量转移。我们表明这个领域的方法都可以归为信息几何的范畴，并且我们提出的方法可以更高效地进行参数搜索和更好地适应数据，结果比现有方法好。