A Graph-Based Approach for 3D Building Model Reconstruction from Airborne LiDAR Point Clouds

Abstract

3D building model reconstruction is of great importance for environmental and urban applications. Airborne light detection and ranging (LiDAR) is a very useful data source for acquiring detailed geometric and topological information of building objects. In this study, we employed a graph-based method based on hierarchical structure analysis of building contours derived from LiDAR data to reconstruct urban building models. The proposed approach first uses a graph theory-based localized contour tree method to represent the topological structure of buildings, then separates the buildings into different parts by analyzing their topological relationships, and finally reconstructs the building model by integrating all the individual models established through the bipartite graph matching process. Our approach provides a more complete topological and geometrical description of building contours than existing approaches. We evaluated the proposed method by applying it to the Lujiazui region in Shanghai, China, a complex and large urban scene with various types of buildings. The results revealed that complex buildings could be reconstructed successfully with a mean modeling error of 0.32 m. Our proposed method offers a promising solution for 3D building model reconstruction from airborne LiDAR point clouds.

Sixty-Year Changes in Residential Landscapes in Beijing: A Perspective from Both the Horizontal (2D) and Vertical (3D) Dimensions

“…Recent advances in Light Detecting and Ranging (LiDAR) and unmanned aerial vehicle technology greatly increase the availability of vertical dimensional data in an affordable way [52][53][54][55]. These advances will greatly facilitate the quantitative analysis of the vertical structure of the urban landscapes and their social and ecological impacts.…”

A New Stereo Pair Disparity Index (SPDI) for Detecting Built-Up Areas from High-Resolution Stereo Imagery

“…The performance gain was low, since building heights were not used for built-up area detection. Terrain slopes and building heights were calculated from LiDAR data [33,34] or open DSMs (e.g., ALOS World 3D-30m [35]), and then used to improve the built-up area detection results. The performance gain was higher due to the use of building heights.…”

“…The SPDI does not suffer from the influence of within-class spectral variation and between-class spectral confusion in remotely sensing imagery [10], which degrades the performance of built-up area detection when using planar texture, shape, and spectral features (e.g., Gabor [22]). The SPDI calculation considers the disparity gradient unlike terrain slopes, building heights or nDSM [34]. Moreover, the SPDI calculation needs no orthorectification processing on raw images of stereo imagery.…”

An Enhanced Algorithm for Concurrent Recognition of Rail Tracks and Power Cables from Terrestrial and Airborne LiDAR Point Clouds

“…Estefanik et al [9] create digital terrain model (DTM) from airborne LiDAR data. Jochem et al [10] and Wu et al [11] extract buildings from LiDAR data of urban environments. Yu et al [12] identify road features from mobile terrestrial LiDAR datasets.…”