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Domain adaptation learning for 3D point clouds： A survey

  • role： First author第一作者
  • Affiliation：

    College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China

  • Email：fanwenhui99@qq.com
  • Introduction：范文辉， 研究方向为三维点云处理。E-mail： fanwenhui99@qq.com
FAN Wenhui1,  
  • Affiliation：

    College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China

LIN Xi1,  
  • Affiliation：

    College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China

LUO Huan1,  
  • Affiliation：

    College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China

GUO Wenzhong1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China

  • Email：why.scholar@gmail.com
  • Introduction：汪汉云，研究方向为三维点云智能处理与理解。E-mail： why.scholar@gmail.com
WANG Hanyun2*,  
  • Affiliation：

    School of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China

DAI Chenguang2

resumen

Three-dimensional (3D) point cloud data have been widely used in many fields, such as autonomous driving, robotics, and high-precision mapping. At present, the state-of-the-art deep learning-based methods for 3D point cloud processing are mainly supervised learning methods. The performance of these methods depends heavily on large-scale, high-quality annotated datasets. However, annotating a large-scale, high-quality, category-diverse, and scenario-rich dataset is time-consuming and labor-intensive. In particular, obtaining sufficiently large numbers of samples for model optimization is also quite difficult in some special cases. In addition, 3D point cloud processing models trained on a single device in a special environment are difficult to generalize to different devices and environments. Their generalizability to various devices and environments is limited. Thus, how to reduce dependencies on high-quality annotated 3D point cloud datasets and how to improve the generalizability of current point cloud processing models are important research topics. In recent years, various kinds of impressive and elaborate technologies, such as meta-learning, few-shot learning, transfer learning, self-supervised learning, semisupervised learning, and weakly supervised learning, have been proposed to solve this problem. As an important research branch of transfer learning, domain adaptive learning aims to eliminate differences in feature distributions across domains and promote the generalization ability of deep learning models, thereby providing a novel solution to address this problem effectively. The academic community has conducted preliminary research on domain adaptive learning for point cloud processing. However, the domain adaptive learning field for point clouds still requires in-depth and effective exploration. Consequently, this study systematically summarizes and classifies recent 3D point cloud domain adaptive learning methods into five categories: adversarial learning, cross-modal learning, pseudo-label learning, data alignment, and other kinds of methods. First, we present the mathematical definition of the domain adaptive learning task and depict the chronological overview of the development of different domain adaptive learning methods to provide readers with a clear understanding. Second, we present the general solution for each category of domain adaptive learning methods and summarize the advantages and disadvantages of the current methods for each category. Third, we compare the performance of current methods on three-point cloud processing tasks, including 3D shape classification, 3D object detection, and 3D semantic segmentation. For each task, we also summarize the commonly used datasets and evaluation metrics for an intuitional comparison. Finally, we conclude the advantages and disadvantages of these five categories of methods and discuss future research directions about the 3D point cloud domain adaptive learning.

palabra clave

remote sensing;3D point cloud;domain adaption learning;adversarial learning;cross-modal learning;pseudo-label learning;data alignment

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