Investigating Keypoint Repeatability for 3D Correspondence Estimation in Cluttered Scenes

In 3D object recognition, local feature-based recognition is known to be robust against occlusion and clutter. Local feature estimation requires feature correspondences, including feature extraction and matching. Feature extraction is normally a two-stage process that estimates keypoints and keypoint descriptors, and existing studies show repeatability to be a good indicator of keypoint feature detector robustness. However, the impact of keypoint repeatability on feature correspondence estimation and overall feature matching accuracy has not yet been studied. In this paper, local features are extracted at both regular and repeatable 3D keypoints using leading keypoint detectors combined with the SHOT descriptor to estimate a set of correspondences. When using a keypoint detector of high repeatability, experimental results show improved feature matching accuracy and reduced computational requirements for the feature description and matching, and overall correspondence estimation process.