Point cloud registration (PCR) involves determining a rigid transformation
that aligns one point cloud to another. Despite the plethora of outstanding
deep learning (DL)-based registration methods proposed, comprehensive and
systematic studies on DL-based PCR techniques are still lacking. In this paper,
we present a comprehensive survey and taxonomy of recently proposed PCR
methods. Firstly, we conduct a taxonomy of commonly utilized datasets and
evaluation metrics. Secondly, we classify the existing research into two main
categories: supervised and unsupervised registration, providing insights into
the core concepts of various influential PCR models. Finally, we highlight open
challenges and potential directions for future research. A curated collection
of valuable resources is made available at this https URL

本篇综述调查了基于深度学习的点云配准技术，包括数据集和评估指标，对现有的研究进行了分类，并提出未来研究的挑战和方向。

基于深度学习的点云配准的综合调查与分类

A Comprehensive Survey and Taxonomy on Point Cloud Registration Based on  Deep Learning

Registration of point clouds collected from a pair of distant vehicles
provides a comprehensive and accurate 3D view of the driving scenario, which is
vital for driving safety related applications, yet existing literature suffers
from the expensive pose label acquisition and the deficiency to generalize to
new data distributions. In this paper, we propose EYOC, an unsupervised distant
point cloud registration method that adapts to new point cloud distributions on
the fly, requiring no global pose labels. The core idea of EYOC is to train a
feature extractor in a progressive fashion, where in each round, the feature
extractor, trained with near point cloud pairs, can label slightly farther
point cloud pairs, enabling self-supervision on such far point cloud pairs.
This process continues until the derived extractor can be used to register
distant point clouds. Particularly, to enable high-fidelity correspondence
label generation, we devise an effective spatial filtering scheme to select the
most representative correspondences to register a point cloud pair, and then
utilize the aligned point clouds to discover more correct correspondences.
Experiments show that EYOC can achieve comparable performance with
state-of-the-art supervised methods at a lower training cost. Moreover, it
outwits supervised methods regarding generalization performance on new data
distributions.

EYOC 是一种无监督远距离点云注册方法，通过逐步训练特征提取器并利用高保真度对应关系生成和新数据分布的泛化性能，实现了与监督方法相媲美的注册性能。

扩展自身的对应关系：无监督的远程点云逐步距离扩展注册

Extend Your Own Correspondences: Unsupervised Distant Point Cloud  Registration by Progressive Distance Extension

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。