Traditional documentation capabilities of laser scanning technology can be further exploited for urban modelling through the transformation of resulting point clouds into solid models compatible for computational analysis. This paper introduces such a technique through the combination of an angle criterion and voxelization. As part of that, a k-nearest neighbor (kNN) searching algorithm is implemented using a predefined number of kNN points combined with a maximum radius of the neighborhood, something not previously implemented. From this sample points are categorized as boundary or interior points. Façade features are determined based on underlying vertical and horizontal grid voxels of the feature boundaries by a grid clustering technique. The complete building model involving all full voxels is generated by employing the Flying Voxel method in order to relabel voxels inside openings or outside the facade as empty voxels. Experimental results on 3 different buildings, using 4 distinct sampling densities show successful detection of all openings, reconstruction of all building façades, and automatic filling of all improper holes. The maximum nodal displacement divergence was 1.6% compared to manually generated meshes from measured drawings. This fully automated approach rivals processing times of other techniques with the distinct advantage of extracting more boundary points, especially in less dense data sets (< 175pts/m 2 ), which may enable its more rapid exploitation of aerial laser scanning data and ultimately preclude needing a priori knowledge.
ABSTRACT:In an effort to reconstruct geometric models of building façades from terrestrial laser scanning data directly without either manual intervention or any third party computer-aid design package, a new algorithm is introduced. The algorithm detects building boundaries and features and converts the point cloud data into a solid model appropriate for computational modeling. The algorithm combines a voxel-based technique with a Delaunay triangulation based criterion. In the first phase, the algorithm detects façade boundary points from raw data. The algorithm's second phase creates a solid model using voxels in a quadtree representation.Finally, the algorithm determines whether holes are actual openings or data deficits caused by occlusions and then fills unrealistic openings. The algorithm was applied to the façades of three masonry buildings. For these buildings, the algorithm successfully detected all openings and reconstructs the façade details correctly. Geometric validation of the models against measured drawings showed overall dimensions correct to 1.2%, mostly opening areas to 3%, and simulation results within 5% of those predicted by a CAD-based model.
Aerial Light Detection and Ranging (LiDAR) offers the potential to auto-generate detailed, three-dimensional (3D) models of the built environment in urban settings. Auto-generation is needed as manual generation is not economically feasible for large areas, and yet such models would offer distinct advantages for a wide range of applications from improved noise and pollution prediction to disaster mitigation modeling. Current technology and the dense geometry of urban environments are two major constraints in LiDAR scanning. This paper outlines the difficulties related to effective vertical surface data capture in an urban environment for the purpose of 3D visualization. Further, the traditional point data captured with LiDAR scans is unsuitable for visualization. Therefore, surface reconstruction algorithms must be applied to the data. These algorithms are largely dependent on the uniformity of the resolution in the point data.Principles for geometric optimization of data capture on vertical surfaces, thereby improving resolution uniformity, are presented.
Automated conversion of point cloud data from laser scanning into formats appropriate for structural engineering holds great promise for exploiting increasingly available aerially-and terrestrially-based pixelized data for a wide range of surveying related applications from environmental modeling to disaster management. This paper introduces a point-based voxelization method to automatically transform point cloud data into solid models for computational modeling. The fundamental viability of the technique is visually demonstrated for both aerial and terrestrial data. For aerial and terrestrial data, this was achieved in less than 30 seconds for data sets up to 650k points. In all cases, the solid models converged without any user intervention when processed in a commercial Finite Element Method program.
Airborne Laser Scanning (ALS) was introduced to provide rapid, high resolution scans of landforms for computational processing. More recently, ALS has been adapted for scanning urban areas. The greater complexity of urban scenes, necessitates the development of novel methods to exploit urban ALS to best advantage. This paper presents occlusion images: a novel technique that exploits the geometric complexity of the urban environment to improve visualisation of small details for better feature recognition. The algorithm is based on an inversion of traditional occlusion techniques.
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