NDLI: Energy minimisation-based multi-class multi-instance geometric primitives extraction from 3D point clouds

Content Provider	IET Digital Library
Author	Wang, Liang Yan, Biying Duan, Fuqing Lu, Ke
Abstract	Geometric primitives contained in three-dimensional (3D) point clouds can provide the meaningful and concise abstraction of 3D data, which plays a vital role in improving 3D vision-based intelligent applications. However, how to efficiently and robustly extract multiple geometric primitives from point clouds is still a challenge, especially when multiple instances of multiple classes of geometric primitives are present. In this study, a novel energy minimisation-based algorithm for multi-class multi-instance geometric primitives extraction from the 3D point cloud is proposed. First, an improved sampling strategy is proposed to generate model hypotheses. Then, an improved strategy to establish the neighbourhood is proposed to help construct and optimise an energy function for points labelling. After that, hypotheses and parameters of models are refined. Iterate this process until the energy does not decrease. Finally, models of multi-class multi-instance geometric primitives are simultaneously and robustly extracted from the 3D point cloud. In comparison with the state-of-the-art methods, it can automatically determine the classes and numbers of geometric primitives in the 3D point cloud. Experimental results with synthetic and real data validate the proposed algorithm.
Starting Page	2660
Ending Page	2667
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 12, Oct (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1625
Journal	IET Image Processing
Publisher Date	2020-06-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	3D Point Cloud 3D Vision-based Intelligent Applications Computational Geometry Computer Vision Computer Vision And Image Processing Technique Electrical/electronic Equipment Energy Minimisation Energy Utilisation Feature Extraction Graphics Technique Image Recognition Interpolation And Function Approximation Iterative Method Minimisation Multiclass Multiinstance Geometric Primitives Extraction Numerical Analysis Numerical Approximation And Analysis Optimisation Technique Performance Evaluation And Testing Power Aware Computing Three-dimensional Point Clouds
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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