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A review of rigid point cloud registration based on deep learning
Lei Chen1, Changzhou Feng1, Yunpeng Ma1
1School of Information Engineering, Tianjin University of Commerce, Tianjin, China.
Frontiers in Neurorobotics
|January 19, 2024
Summary
Deep learning enhances 3D point cloud registration for challenges like noise and low overlap. This review explores deep learning methods, performance improvements, and applications in various fields.
Area of Science:
- Computer Vision
- Geomatics Engineering
- Robotics
Background:
- Traditional 3D point cloud registration methods struggle with real-world data issues like noise, low overlap, and large scales.
- The emergence of deep learning offers promising solutions to overcome these limitations in point cloud processing.
Purpose of the Study:
- To provide a comprehensive review of deep learning-based point cloud registration techniques.
- To categorize and analyze methods for complete and partial overlap scenarios.
- To discuss performance enhancements and application domains.
Main Methods:
- Categorization of deep learning methods into complete and partial overlap registration.
- Analysis of network performance improvements through hardware and software acceleration.
- Exploration of diverse application areas for point cloud registration.
Main Results:
- Deep learning methods effectively address challenges in noise, low overlap, and data scale.
- Both complete and partial overlap registration techniques show distinct characteristics and advantages.
- Performance acceleration strategies for deep learning models are identified.
Conclusions:
- Deep learning-based point cloud registration is a rapidly advancing field with significant potential.
- Further research is needed to address current challenges and explore future directions.
- The review offers insights for researchers and practitioners in 3D data processing.
