A comprehensive survey on point cloud registration (2103.02690v2)

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 builds a new benchmark to evaluate the state-of-the-art registration algorithms in solving cross-source challenges. Besides, this survey summarizes the benchmark data sets and discusses point cloud registration applications across various domains. Finally, this survey proposes potential research directions in this rapidly growing field.

A Comprehensive Survey on Point Cloud Registration

The paper "A comprehensive survey on point cloud registration," authored by Xiaoshui Huang, Guofeng Mei, Jian Zhang, and Rana Abbas, offers an in-depth survey of the methodologies and challenges associated with point cloud registration—a fundamental task in computer vision. Registration refers to estimating the transformation between two point clouds, enabling their alignment into a unified coordinate system. This task is crucial in applications such as 3D reconstruction, localization, and pose estimation, particularly with the proliferation of 3D sensors like LiDAR and Kinect.

Key Insights and Methodologies

The survey systematically categorizes existing techniques into optimization-based and deep learning methods. Optimization-based approaches include ICP variations, graph-based methods, GMMs, and semi-definite programming relaxations. Such methods have traditionally been grounded in minimizing geometric projection errors, often through iterative processes that refine correspondences and transformations.

With the surge in 3D deep learning, notable progress in feature learning and end-to-end registration models has been made. Feature-learning methods extract distinctive features from point clouds to facilitate correspondence search, while end-to-end learning methods integrate feature extraction and transformation estimation into a unified framework. Despite their success, a key unresolved issue is bridging the gap between optimization-based and learning-based approaches.

Cross-Source Registration Challenges

The paper highlights cross-source registration as an emerging area of interest, fueled by advances in heterogeneous 3D sensors. Challenges include handling data from different acquisition methodologies, such as high-density Kinect data versus sparse LiDAR scans, each with unique noise characteristics and scale differences. The authors address the lack of comprehensive reviews and standardized benchmarks for cross-source registration, a gap this survey aims to fill by introducing a new benchmark and presenting preliminary comparative results.

Implications and Future Directions

The survey underscores the practical and theoretical significance of point cloud registration in diverse domains, from autonomous driving to mining. By constructing city-scale 3D maps or monitoring underground environments, innovations in registration technologies can enable more precise and efficient data integration. The authors propose potential research directions, including improving robustness to noise and developing real-time algorithms, which are crucial for advancing applications like robotics and augmented reality.

The authors call for further synergy between optimization techniques and deep learning to leverage robust mathematical foundations with new data-driven insights. Such integration could yield registration methods that offer enhanced accuracy, robustness, and computational efficiency. As technology evolves, the demand for robust point cloud registration is expected to grow, heralding novel interdisciplinary research and industrial applications.

Conclusion

The paper presents a cogent overview of point cloud registration techniques, delineating advancements and identifying future research pathways. It serves as a valuable resource for experienced researchers seeking to navigate the complex landscape of point cloud registration and its burgeoning applications across various fields. Through rigorous analysis and benchmarking, this survey lays the groundwork for enhanced methodologies that could reshape the landscape of 3D data processing and its myriad applications.