2019/01/16 by Bao Zhao, Zhao, Bao, Xiaobo Chen +5
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Pose and Action Recognition #Robotics and Sensor-Based Localization #cs.CV
paper · pdf · doi:10.48550/arxiv.1901.05104
arxiv created 2019/01/16 · openalex publication_date 2019/01/16 · arxiv updated 2019/01/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
3D local feature extraction and matching is the basis for solving many tasks in the area of computer vision, such as 3D registration, modeling, recognition and retrieval. However, this process commonly draws into false correspondences, due to noise, limited features, occlusion, incomplete surface and etc. In order to estimate accurate transformation based on these corrupted correspondences, numerous transformation estimation techniques have been proposed. However, the merits, demerits and appropriate application for these methods are unclear owing to that no comprehensive evaluation for the performance of these methods has been conducted. This paper evaluates eleven state-of-the-art transformation estimation proposals on both descriptor based and synthetic correspondences. On descriptor based correspondences, several evaluation items (including the performance on different datasets, robustness to different overlap ratios and the performance of these technique combined with Iterative Closest Point (ICP), different local features and LRF/A techniques) of these methods are tested on four popular datasets acquired with different devices. On synthetic correspondences, the robustness of these methods to varying percentages of correct correspondences (PCC) is evaluated. In addition, we also evaluate the efficiencies of these methods. Finally, the merits, demerits and application guidance of these tested transformation estimation methods are summarized.