  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.
