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

Point cloud registration is a task to estimate the rigid transformation
between two unaligned scans, which plays an important role in many computer
vision applications. Previous learning-based works commonly focus on supervised
registration, which have limitations in practice. Recently, with the advance of
inexpensive RGB-D sensors, several learning-based works utilize RGB-D data to
achieve unsupervised registration. However, most of existing unsupervised
methods follow a cascaded design or fuse RGB-D data in a unidirectional manner,
which do not fully exploit the complementary information in the RGB-D data. To
leverage the complementary information more effectively, we propose a network
implementing multi-scale bidirectional fusion between RGB images and point
clouds generated from depth images. By bidirectionally fusing visual and
geometric features in multi-scales, more distinctive deep features for
correspondence estimation can be obtained, making our registration more
accurate. Extensive experiments on ScanNet and 3DMatch demonstrate that our
method achieves new state-of-the-art performance. Code will be released at
this https URL

利用 RGB 图像和深度图像生成的点云之间的多尺度双向融合实现了更准确的对应估计，从而使得我们的点云配准方法在 ScanNet 和 3DMatch 数据集上达到了最新的性能水平。

PointMBF: 用于无监督 RGB-D 点云配准的多尺度双向融合网络

PointMBF: A Multi-scale Bidirectional Fusion Network for Unsupervised  RGB-D Point Cloud Registration

Point cloud registration is a key problem for computer vision applied to
robotics, medical imaging, and other applications. This problem involves
finding a rigid transformation from one point cloud into another so that they
align. Iterative Closest Point (ICP) and its variants provide simple and
easily-implemented iterative methods for this task, but these algorithms can
converge to spurious local optima. To address local optima and other
difficulties in the ICP pipeline, we propose a learning-based method, titled
Deep Closest Point (DCP), inspired by recent techniques in computer vision and
natural language processing. Our model consists of three parts: a point cloud
embedding network, an attention-based module combined with a pointer generation
layer, to approximate combinatorial matching, and a differentiable singular
value decomposition (SVD) layer to extract the final rigid transformation. We
train our model end-to-end on the ModelNet40 dataset and show in several
settings that it performs better than ICP, its variants (e.g., Go-ICP, FGR),
and the recently-proposed learning-based method PointNetLK. Beyond providing a
state-of-the-art registration technique, we evaluate the suitability of our
learned features transferred to unseen objects. We also provide preliminary
analysis of our learned model to help understand whether domain-specific and/or
global features facilitate rigid registration.

本文提出了一种基于学习的方法 Deep Closest Point，用于点云配准问题，通过点云嵌入网络、基于注意力机制的组合匹配模块和可微分的奇异值分解层，实现最终的刚性变换。实验证明该方法优于传统算法 Iterative Closest Point 及其变种，以及最近提出的基于学习的方法 PointNetLK，同时能有效迁移学到的特征。