A fast and robust local descriptor for 3D point cloud registration

Jiaqi Yang, Zhiguo Cao, Qian Zhang

Research output: Contribution to journalArticlepeer-review

212 Scopus citations

Abstract

This paper proposes a novel local feature descriptor, called a local feature statistics histogram (LFSH), for efficient 3D point cloud registration. An LFSH forms a comprehensive description of local shape geometries by encoding their statistical properties on local depth, point density, and angles between normals. The sub-features in the LFSH descriptor are low-dimensional and quite efficient to compute. In addition, an optimized sample consensus (OSAC) algorithm is developed to iteratively estimate the optimum transformation from point correspondences. OSAC can handle the challenging cases of matching highly self-similar models. Based on the proposed LFSH and OSAC, a coarse-to-fine algorithm can be formed for 3D point cloud registration. Experiments and comparisons with the state-of-the-art descriptors demonstrate that LFSH is highly discriminative, robust, and significantly faster than other descriptors. Meanwhile, the proposed coarse-to-fine registration algorithm is demonstrated to be robust to common nuisances, including noise and varying point cloud resolutions, and can achieve high accuracy on both model data and scene data.

Original languageEnglish
Pages (from-to)163-179
Number of pages17
JournalInformation Sciences
Volume346-347
DOIs
StatePublished - 10 Jun 2016
Externally publishedYes

Keywords

  • 3D point cloud registration
  • Feature matching
  • Local feature descriptor
  • Point correspondences
  • Self-similar models

Fingerprint

Dive into the research topics of 'A fast and robust local descriptor for 3D point cloud registration'. Together they form a unique fingerprint.

Cite this