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Description
A new automatic and adaptive algorithm for edge extraction from a random point cloud was developed and presented herein. The proposed algorithm was tested using real measurement data. The developed algorithm is able to realistically reduce the amount of redundant data and correctly extract stable edges representing the geometric structures of a studied object without losing im-portant data and information. The new algorithm automatically self-adapts to the received data. It does not require any pre-setting or initial parameters. The detection threshold is also adaptively selected based on the acquired data.
Dataset file
Point Cloud Feature Extraction.zip
20.1 MB,
S3 ETag
ac1da4051ef0ccc6ed39cdf9a2c03c3a-1,
downloads: 55
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File details
- License:
-
open in new tab
CC BYAttribution - File embargo:
- 2021-05-14
- Raw data:
- Data contained in dataset was not processed.
- Software:
- Matlab
Details
- Year of publication:
- 2021
- Verification date:
- 2021-05-19
- Creation date:
- 2020
- Dataset language:
- English
- Fields of science:
-
- Civil engineering and transport (Engineering and Technology)
- environmental engineering, mining and energy (Engineering and Technology)
- DOI:
- DOI ID 10.34808/szar-a523 open in new tab
- Series:
- Verified by:
- Gdańsk University of Technology
Keywords
References
- publication A New Adaptive Method for the Extraction of Steel Design Structures from an Integrated Point Cloud
- laboratory Pracownia Fotogrametrii i Teledetekcji Niskiego Pułapu
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