For the purpose of 3D keypoint matching, a Local Reference Frame (LRF), a local coordinate system of the keypoint, is one important information source for achieving repeatable feature descriptions and accurate pose estimations. We propose a robust LRF for two main point cloud disturbances: density differences and partial occlusions. To generate LRFs that are robust to such disturbances, we employ two strategies: normalizing the effects of point cloud density by approximating the surface area in the local region and using the dominant orientation of a normal vector around the keypoint. Experiments confirm that the proposed method has higher repeatability than state-of-the-art methods with respect to density differences and partial occlusions. It was also confirmed that the method enhances the reliability of keypoint matching.
CITATION STYLE
Akizuki, S., & Hashimoto, M. (2015). DPN-LRF: A local reference frame for robustly handling density differences and partial occlusions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9474, pp. 878–887). Springer Verlag. https://doi.org/10.1007/978-3-319-27857-5_78
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