Going Further with Point Pair Features

Stefan Hinterstoisser; Vincent Lepetit; Naresh Rajkumar; Kurt Konolige

doi:10.1007/978-3-319-46487-9_51

Going Further with Point Pair Features

Computer Vision and Pattern Recognition 2017-11-15 v1

Authors: Stefan Hinterstoisser , Vincent Lepetit , Naresh Rajkumar , Kurt Konolige

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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.

Keywords

point cloud point cloud registration feature selection

Cite

@article{arxiv.1711.04061,
  title  = {Going Further with Point Pair Features},
  author = {Stefan Hinterstoisser and Vincent Lepetit and Naresh Rajkumar and Kurt Konolige},
  journal= {arXiv preprint arXiv:1711.04061},
  year   = {2017}
}

Comments

Corrected post-print of manuscript accepted to the European Conference on Computer Vision (ECCV) 2016; https://link.springer.com/chapter/10.1007/978-3-319-46487-9_51

Going Further with Point Pair Features

Abstract

Keywords

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