A Comprehensive Survey and Taxonomy on Point Cloud Registration Based on Deep Learning
<|reference_start|>A Comprehensive Survey and Taxonomy on Point Cloud Registration Based on Deep Learning: 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 https://github.com/yxzhang15/PCR.<|reference_end|>
@article{zhang2024a,
    title={A Comprehensive Survey and Taxonomy on Point Cloud Registration Based on
  Deep Learning},
    author={Yu-Xin Zhang, Jie Gui, Xiaofeng Cong, Xin Gong, Wenbing Tao},
    journal={arXiv preprint arXiv:2404.13830},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13830},
    primaryClass={cs.CV}
}