Recognition of Point Sets Objects in Realistic Scenes
Joint Authors
Gao, Ruizhen
Zhang, Jingjun
Li, Xiaohui
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-04
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Telecommunications Engineering
Abstract EN
With the emergence of new intelligent sensing technologies such as 3D scanners and stereo vision, high-quality point clouds have become very convenient and lower cost.
The research of 3D object recognition based on point clouds has also received widespread attention.
Point clouds are an important type of geometric data structure.
Because of its irregular format, many researchers convert this data into regular three-dimensional voxel grids or image collections.
However, this can lead to unnecessary bulk of data and cause problems.
In this paper, we consider the problem of recognizing objects in realistic senses.
We first use Euclidean distance clustering method to segment objects in realistic scenes.
Then we use a deep learning network structure to directly extract features of the point cloud data to recognize the objects.
Theoretically, this network structure shows strong performance.
In experiment, there is an accuracy rate of 98.8% on the training set, and the accuracy rate in the experimental test set can reach 89.7%.
The experimental results show that the network structure in this paper can accurately identify and classify point cloud objects in realistic scenes and maintain a certain accuracy when the number of point clouds is small, which is very robust.
American Psychological Association (APA)
Gao, Ruizhen& Li, Xiaohui& Zhang, Jingjun. 2020. Recognition of Point Sets Objects in Realistic Scenes. Mobile Information Systems،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1192397
Modern Language Association (MLA)
Gao, Ruizhen…[et al.]. Recognition of Point Sets Objects in Realistic Scenes. Mobile Information Systems No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1192397
American Medical Association (AMA)
Gao, Ruizhen& Li, Xiaohui& Zhang, Jingjun. Recognition of Point Sets Objects in Realistic Scenes. Mobile Information Systems. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1192397
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1192397