The point pair feature (PPF) is widely used for 6D pose estimation. In this paper, we propose an efficient 6D pose estimation method based on the PPF framework. We introduce a well-targeted down-sampling strategy that focuses more on edge area for efficient feature extraction of complex geometry. A pose hypothesis validation approach is proposed to resolve the symmetric ambiguity by calculating edge matching degree. We perform evaluations on two challenging datasets and one real-world collected dataset, demonstrating the superiority of our method on pose estimation of geometrically complex, occluded, symmetrical objects. We further validate our method by applying it to simulated punctures.
@article{arxiv.2209.08266,
title = {6DOF Pose Estimation of a 3D Rigid Object based on Edge-enhanced Point Pair Features},
author = {Chenyi Liu and Fei Chen and Lu Deng and Renjiao Yi and Lintao Zheng and Chenyang Zhu and Jia Wang and Kai Xu},
journal= {arXiv preprint arXiv:2209.08266},
year = {2022}
}