Going Further with Point Pair Features
Abstract
Point Pair Features is a widely used method to detect 3D objects in point clouds, however they are prone to fail in presence of sensor noise and background clutter. We introduce novel sampling and voting schemes that significantly reduces the influence of clutter and sensor noise. Our experiments show that with our improvements, PPFs become competitive against state-of-the-art methods as it outperforms them on several objects from challenging benchmarks, at a low computational cost.
Cite
Text
Hinterstoisser et al. "Going Further with Point Pair Features." European Conference on Computer Vision, 2016. doi:10.1007/978-3-319-46487-9_51Markdown
[Hinterstoisser et al. "Going Further with Point Pair Features." European Conference on Computer Vision, 2016.](https://mlanthology.org/eccv/2016/hinterstoisser2016eccv-going/) doi:10.1007/978-3-319-46487-9_51BibTeX
@inproceedings{hinterstoisser2016eccv-going,
title = {{Going Further with Point Pair Features}},
author = {Hinterstoisser, Stefan and Lepetit, Vincent and Rajkumar, Naresh and Konolige, Kurt},
booktitle = {European Conference on Computer Vision},
year = {2016},
pages = {834-848},
doi = {10.1007/978-3-319-46487-9_51},
url = {https://mlanthology.org/eccv/2016/hinterstoisser2016eccv-going/}
}