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Histograms of Gaussian normal distribution for 3D feature matching in cluttered scenes
Zhou, Wei1,3,4; Ma, Caiwen2; Yao, Tong1,3; Chang, Peng5; Zhang, Qi3; Kuijper, Arjan4
Department光电测量技术实验室
2019-04
Source PublicationVISUAL COMPUTER
ISSN0178-2789;1432-2315
Volume35Issue:4Pages:489-505
Contribution Rank1
Abstract

3D feature descriptors provide essential information to find given models in captured scenes. In practical applications, these scenes often contain clutter. This imposes severe challenges on the 3D object recognition leading to feature mismatches between scenes and models. As such errors are not fully addressed by the existing methods, 3D feature matching still remains a largely unsolved problem. We therefore propose our Histograms of Gaussian Normal Distribution (HGND) for capturing salient feature information on a local reference frame (LRF) that enables us to solve this problem. We define a LRF on each local surface patch by using the eigenvectors of the scatter matrix. Different from the traditional local LRF-based methods, our HGND descriptor is based on the combination of geometrical and spatial information without calculating the distribution of every point and its geometrical information in a local domain. This makes it both simple and efficient. We encode the HGND descriptors in a histogram by the geometrical projected distribution of the normal vectors. These vectors are based on the spatial distribution of the points. We use three public benchmarks, the Bologna, the UWA and the Ca' Foscari Venezia dataset, to evaluate the speed, robustness, and descriptiveness of our approach. Our experiments demonstrate that the HGND is fast and obtains a more reliable matching rate than state-of-the-art approaches in cluttered situations.

KeywordLocal surface patch Local reference frame Local feature descriptor Point cloud
DOI10.1007/s00371-018-1478-x
Indexed BySCI
Language英语
WOS IDWOS:000463672800003
PublisherSPRINGER
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31376
Collection光电测量技术实验室
Corresponding AuthorZhou, Wei
Affiliation1.Xian Inst Opt & Precis Mech CAS, Xian 710119, Shaanxi, Peoples R China
2.Xian Inst Opt & Precis Mech CAS, Signal & Informat Proc, Xian 710119, Shaanxi, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Tech Univ Darmstadt, Fraunhofer IGD, D-64283 Darmstadt, Germany
5.Northeastern Univ, Elect & Comp Engn, Boston, MA 02115 USA
Recommended Citation
GB/T 7714
Zhou, Wei,Ma, Caiwen,Yao, Tong,et al. Histograms of Gaussian normal distribution for 3D feature matching in cluttered scenes[J]. VISUAL COMPUTER,2019,35(4):489-505.
APA Zhou, Wei,Ma, Caiwen,Yao, Tong,Chang, Peng,Zhang, Qi,&Kuijper, Arjan.(2019).Histograms of Gaussian normal distribution for 3D feature matching in cluttered scenes.VISUAL COMPUTER,35(4),489-505.
MLA Zhou, Wei,et al."Histograms of Gaussian normal distribution for 3D feature matching in cluttered scenes".VISUAL COMPUTER 35.4(2019):489-505.
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