A Survey of Point Cloud Completion

Abstract

Point cloud completion is able to estimate the complete point cloud starting from the missing point cloud, which obtains higher quality point cloud data for widely used in remote sensing 3-D modeling, medical imaging, robot vision, etc. The challenge of point clouds mainly lies in the disordered and unstructured nature, which makes point cloud completion difficult. Point cloud completion research can be broadly categorized into traditional approaches and deep learning-based methods. Recently, intensive research has primarily focused on deep learning-based methods, given robustness and efficiency in processing the substantial missing data encountered in complex real world scenes. In addition, deep learning-based methods have higher generalization performance. To stimulate future research, this survey presents a comprehensive review of existing traditional and deep learning-based 3-D point cloud completion methods. This review conducts extensive examinations of each stage of the process, providing a compilation of famous datasets, metrics, and their respective characteristics. In addition, the impacts of subsequent downstream application tasks with or without completion are discussed, followed by some potential future issues in point cloud completion.