A comprehensive survey on point cloud registration
Xiaoshui Huang, Guofeng Mei, Jian Zhang, Rana Abbas

TL;DR
This survey comprehensively reviews point cloud registration methods, highlighting recent advances, connections between optimization and deep learning approaches, and introduces a new benchmark for cross-source registration challenges.
Contribution
It provides an extensive overview of both same-source and cross-source registration methods, clarifies their relationships, and establishes a new benchmark dataset for evaluating algorithms.
Findings
Deep learning and optimization methods have improved robustness and efficiency.
Connections between optimization-based and deep learning methods are still unclear.
A new benchmark dataset for cross-source point cloud registration is introduced.
Abstract
Registration is a transformation estimation problem between two point clouds, which has a unique and critical role in numerous computer vision applications. The developments of optimization-based methods and deep learning methods have improved registration robustness and efficiency. Recently, the combinations of optimization-based and deep learning methods have further improved performance. However, the connections between optimization-based and deep learning methods are still unclear. Moreover, with the recent development of 3D sensors and 3D reconstruction techniques, a new research direction emerges to align cross-source point clouds. This survey conducts a comprehensive survey, including both same-source and cross-source registration methods, and summarize the connections between optimization-based and deep learning methods, to provide further research insight. This survey also…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · 3D Shape Modeling and Analysis · 3D Surveying and Cultural Heritage
