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Building reconstruction by target based graph matching on incomplete laser data: analysis and limitations.

Sander Oude Elberink¹, George Vosselman

¹International Institute for Geo-Information Science and Earth Observation, Hengelosestraat 99, P.O. Box 6, 7500 AA Enschede, The Netherlands.

Sensors (Basel, Switzerland)

|March 29, 2012

Summary

This study introduces a target-based graph matching approach for 3D building reconstruction using airborne laser scanner (ALS) data. It effectively handles incomplete data, identifying areas for automated reconstruction and highlighting limitations.

Keywords:

building reconstruction incomplete data laser scanner data target graph matching

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Area of Science:

Geomatics
Computer Vision
3D Reconstruction

Background:

Airborne laser scanner (ALS) data density is increasing, demanding more sophisticated derived products.
Automated 3D city model generation, particularly for roof, road, and terrain surfaces, faces challenges due to discrepancies between algorithmic assumptions and real-world data.

Purpose of the Study:

To propose a novel target-based graph matching approach for automated building reconstruction.
To handle both complete and incomplete airborne laser scanner data.
To automatically detect areas that cannot be reconstructed and identify reasons for exclusion.

Main Methods:

A target-based graph matching algorithm was developed to analyze topological relationships in ALS data.
The approach processes complete and incomplete laser data, generating match results and quality parameters.
Four datasets were analyzed to evaluate the quality of reconstructed roofs and identify reconstruction limitations.

Main Results:

The proposed method successfully reconstructs buildings from complete ALS data and identifies areas with incomplete data.
Quality parameters provide insights into model fit and unused data.
Reasons for excluding segments from automatic reconstruction, such as data gaps and target object limitations, were identified.

Conclusions:

The target-based graph matching approach offers a robust method for automated building reconstruction from ALS data, even with data incompleteness.
Identifying reconstruction limitations provides a basis for future improvements, such as incorporating likelihood functions for topological relations.