Local-to-Global Signature Descriptor for 3D Object Recognition

Hadji, Isma; DeSouza, Guilherme N.

doi:10.1007/978-3-319-16628-5_41

Cited by 7 publications

(1 citation statement)

References 15 publications

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“…Global-based features are often used in 3D object recognition and 3D object categorization, while some global descriptors can be used for pose estimation of the objects because they contain local descriptors, such as Viewpoint Feature Histogram (VFH) and Local-to-Global Signature (LGS) descriptor. Moreover, global-based methods are less computation compared with the local features and better to describe the whole point cloud but are affected generally by occlusions and clutter [143]. Hybrid-based descriptors incorporate local and global descriptors together with most of the advantages from them.…”

Section: Feature-based Methodsmentioning

confidence: 99%

A Tutorial Review on Point Cloud Registrations: Principle, Classification, Comparison, and Technology Challenges

Wang

Zhang

2021

Mathematical Problems in Engineering

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A point cloud as a collection of points is poised to bring about a revolution in acquiring and generating three-dimensional (3D) surface information of an object in 3D reconstruction, industrial inspection, and robotic manipulation. In this revolution, the most challenging but imperative process is point could registration, i.e., obtaining a spatial transformation that aligns and matches two point clouds acquired in two different coordinates. In this survey paper, we present the overview and basic principles, give systematical classification and comparison of various methods, and address existing technical problems in point cloud registration. This review attempts to serve as a tutorial to academic researchers and engineers outside this field and to promote discussion of a unified vision of point cloud registration. The goal is to help readers quickly get into the problems of their interests related to point could registration and to provide them with insights and guidance in finding out appropriate strategies and solutions.

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Section: Feature-based Methodsmentioning

confidence: 99%