3D Point Cloud Registration using A-KAZE Features and Graph Optimization

C, Dayananda Kumar N.; Suresh, K. V.; Dinesh, R.

doi:10.1109/iccsp.2019.8697917

Cited by 2 publications

(1 citation statement)

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“…With the flourishing development of three-dimensional scanning technology and information acquisition techniques, point cloud data, as a digital reflection of the real world, has become a core data form in various fields such as manufacturing, cultural heritage preservation, urban planning, and geological exploration [1]. However, during the acquisition of these data, various factors such as human factors, equipment errors, and environmental changes often introduce noise into the obtained three-dimensional data.…”

Section: Introductionmentioning

confidence: 99%

The implementation method of point cloud denoising filter based on KinectV2

Lin,

Wang,

Xiong

2024

J. Phys.: Conf. Ser.

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In the process of collecting data from point clouds, some factors, such as precision equipment, operator experience, environmental conditions, as well as characteristic diffraction effects of electromagnetic waves, changes in surface characteristics of test objects, and the impact of integration processes, are involved data and records that may display some point noise in the data point cloud. In addition to noise caused by measurement errors, practical applications can be affected by external interference and obstacles, which causes deviating values in the data point cloud to differ from the measurement entity. A 3D scanner, represented by the depth of the camera, is used to collect data. To eliminate these noises and anomalies, it is necessary to filter the initial points of the cloud. In this article, we suggest using statistical filtering algorithms to perform statistical analysis on each point community, remove points that do not meet predetermined standards, and remove outliers. To verify the effectiveness of the algorithm, it is compared with other filtering algorithms. The experimental results show that the filter algorithm proposed in this thesis has better effects on outlier points and can obtain the general shape properties of the point cloud.

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Section: Introductionmentioning

confidence: 99%