用于拟合和识别三维点云中几何基元的 Hough 参数空间的离散化

IF 4.4 2区 数学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Chiara Romanengo, Bianca Falcidieno, Silvia Biasotti
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引用次数: 0

摘要

自 20 世纪 70 年代以来,识别和拟合简单几何图形的研究一直在进行,并提出了各种方法,包括随机方法、参数方法、基于基元的注册技术以及最近的深度学习。Hough 变换因其对噪声和异常值的鲁棒性、处理缺失数据的能力以及对多个模型实例的支持而备受关注。遗憾的是,Hough 变换的一个主要局限是如何正确离散其参数空间,因为增加参数数量或降低采样频率会使其计算成本变得昂贵。我们提出了两种不同的离散方法,以说明如何通过选择适当的参数离散方法来提高拟合和识别质量。与传统方法相比,我们的参数驱动空间离散化方法显著提高了参数识别质量,并通过降低离散化维度减少了计算时间和空间,这在几何基元基准的广泛验证中得到了证明。在城市建筑和 CAD 物体的分割数据集上也进行了初步实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discretisation of the Hough parameter space for fitting and recognising geometric primitives in 3D point clouds

Research in recognising and fitting simple geometric shapes has been ongoing since the 1970s, with various approaches proposed, including stochastic methods, parameter methods, primitive-based registration techniques, and more recently, deep learning. The Hough transform is a method of interest due to its demonstrated robustness to noise and outliers, ability to handle missing data, and support for multiple model instances. Unfortunately, one of the main limitations of the Hough transform is how to properly discretise its parameter space, as increasing their number or decreasing the sampling frequency can make it computationally expensive.

The relationship between the approximation accuracy and the parameter space’s discretisation is investigated to address this. We present two distinct discretisations to illustrate how the fitting and recognition quality can be improved by selecting an appropriate parameter discretisation. Our parameter-driven space discretisation is shown to significantly improve the parameter recognition quality over the classical method and reduce computational time and space by decreasing the discretisation’s dimension, as demonstrated by an extensive validation on a benchmark of geometric primitives. Preliminary experiments are also presented on segmenting datasets from urban buildings and CAD objects.

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来源期刊
Mathematics and Computers in Simulation
Mathematics and Computers in Simulation 数学-计算机:跨学科应用
CiteScore
8.90
自引率
4.30%
发文量
335
审稿时长
54 days
期刊介绍: The aim of the journal is to provide an international forum for the dissemination of up-to-date information in the fields of the mathematics and computers, in particular (but not exclusively) as they apply to the dynamics of systems, their simulation and scientific computation in general. Published material ranges from short, concise research papers to more general tutorial articles. Mathematics and Computers in Simulation, published monthly, is the official organ of IMACS, the International Association for Mathematics and Computers in Simulation (Formerly AICA). This Association, founded in 1955 and legally incorporated in 1956 is a member of FIACC (the Five International Associations Coordinating Committee), together with IFIP, IFAV, IFORS and IMEKO. Topics covered by the journal include mathematical tools in: •The foundations of systems modelling •Numerical analysis and the development of algorithms for simulation They also include considerations about computer hardware for simulation and about special software and compilers. The journal also publishes articles concerned with specific applications of modelling and simulation in science and engineering, with relevant applied mathematics, the general philosophy of systems simulation, and their impact on disciplinary and interdisciplinary research. The journal includes a Book Review section -- and a "News on IMACS" section that contains a Calendar of future Conferences/Events and other information about the Association.
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