Handcrafted Local Feature Descriptor-Based Point Cloud Registration and Its Applications: A Review

作者：Wuyong Tao, Ruisheng Wang, Xianghong Hua, Jingbin Liu, Xijiang Chen, Yufu Zang, Dong Chen, Dong Xu, Bufan Zhao · 发表于：IEEE Transactions on Visualization and Computer Graphics · 年份：2025 · DOI：10.1109/tvcg.2025.3569894 · 被引用次数：11 · 研究领域：Image Processing and 3D Reconstruction、3D Shape Modeling and Analysis、Remote Sensing and LiDAR Applications

Point cloud registration serves as a fundamental problem across multiple fields including computer vision, computer graphics, and remote sensing. While local feature descriptors (LFDs) have long been established as a cornerstone for point cloud registration and the LFD-based approach has been extensively studied, the field has witnessed significant advancements in recent years. Despite these developments, the research community lacks a systematic review to consolidate these contributions, leaving many researchers unaware of recent progress in LFD-based registration. To address this gap, we present a comprehensive review that critically examines both state-of-the-art and widely referenced methods across all subtasks of LFD-based registration. Our work provides: (1) an extensive survey of existing methodologies, (2) in-depth analysis of their respective strengths and limitations, (3) insightful observations and practical recommendations, and (4) a thorough summary of relevant applications and publicly available datasets. This systematic overview offers valuable guidance for researchers pursuing future investigations in this domain.