Integrating topographic features and patch matching into point cloud restoration for terrain modelling

ABSTRACT

Point clouds are widely used in Earth surface research but usually exhibit gaps of missing data. Previous point cloud restoration methods used in terrain modelling have not fully considered complex terrain characteristics, which can be summarised as the controlling role of topographic features in shaping terrain surfaces and the inherent similarities observed among these surfaces. This work introduces a novel method that integrates Topographic Features and Patch Matching (TFPM) into point cloud restoration processes for terrain modelling. The method mainly contains three steps. First, identifying gap boundary points. Second, topographic feature points are extracted and subsequently interpolated into the identified gaps. Third, searching other parts of the raw point cloud for patches resembling the gaps, and the identified patches are used as templates to restore the point cloud. The proposed method is benchmarked against three state-of-the-art point cloud restoration methods. The experimental results demonstrate that the TFPM method consistently exhibits superior accuracy in terrain modelling and analysis, as evidenced by low values of the root mean square error, average elevation difference, and average slope difference. This work endeavours to incorporate topographic features into point cloud restoration processes and can benefit future research related to terrain modelling and analysis.

KEYWORDS:

• Point clouds
• point cloud restoration
• topographic features
• patch matching
• terrain modelling

Acknowledgments

The authors would take to thank editors and two anonymous reviewers for the useful comments on the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The test data that supports this work is available in ‘figshare’ repository with the private link ‘https://figshare.com/s/d1e7ccb5d56793ed5e89’.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant [41971333, 41930102, 42371407]; Priority Academic Programme Development of Jiangsu Higher Education Institutions under Grant [164320H116]; The Priority Academic Program Development of Jiangsu Higher Education Institutions and the Deep-time Digital Earth (DDE) Big Science Program.