@article{Liu2024, 
author = {Chenyi Liu and Fei Chen and Lu Deng and Renjiao Yi and Lintao Zheng and Chenyang Zhu and Jia Wang and Kai Xu},
title = {6DOF pose estimation of a 3D rigid object based on edge-enhanced point pair features},
year = {2024},
journal = {Computational Visual Media},
volume = {10},
number = {1},
pages = {61-77},
keywords = {object recognition, pose estimation, 3D point cloud, point pair feature (PPF)},
url = {https://www.sciopen.com/article/10.1007/s41095-022-0308-2},
doi = {10.1007/s41095-022-0308-2},
abstract = {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 on edge areas for efficient feature extraction for complex geometry. A pose hypothesis validation approach is proposed to resolve ambiguity due to symmetry by calculating the edge matching degree. We perform evaluations on two challenging datasets and one real-world collected dataset, demonstrating the superiority of our method for pose estimation for geometrically complex, occluded, symmetrical objects. We further validate our method by applying it to simulated punctures.}
}