During the past several years, point density covering topographic objects with airborne lidar (Light Detection And Ranging) technology has been greatly improved. This achievement provides an improved ability for reconstructing more complicated building roof structures; more specifically, those comprising various model primitives horizontally and/or vertically. However, the technology for automatically reconstructing such a complicated structure is thus far poorly understood and is currently based on employing a limited number of pre-specified building primitives. This paper addresses this limitation by introducing a new technique of modeling 3D building objects using a data-driven approach whereby densely collecting low-level modeling cues from lidar data are used in the modeling process. The core of the proposed method is to globally reconstruct geometric topology between adjacent linear features by adopting a BSP (Binary Space Partitioning) tree. The proposed algorithm consists of four steps: (a) detecting individual buildings from lidar data, (b) clustering laser points by height and planar similarity, (c) extracting rectilinear lines, and (d) planar partitioning and merging for the generation of polyhedral models. This paper demonstrates the efficacy of the algorithm for creating complex models of building rooftops in 3D space from airborne lidar data.
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSINGN o v e m b e r 2 0 0 8 1425 Gunho Sohn and Vincent Tao are with the GeoICT Lab,
Line simplification is an important function in GIS and cartography and is widely used in commercial GIS software packages. Most line simplification algorithms require the user to supply a tolerance value, which is used to determine the extent to which simplification is to be applied.. All simplification algorithms induce positional errors in the data set, because they produce a discrepancy between the original line and its simplified version. The amount of this error depends on both the tolerance value and the shape of the line. This is the reason that many researchers have focused on measuring the geometric characteristics (or complexity) of lines. Using one tolerance value for all lines in the data set results in different positional errors for different lines. What is usually important for the user, is to maintain a specific level of quality, and not the tolerance value itself. The question is, 'how does one specify a tolerance value for each line based on the user specified level of accuracy'? This paper presents a solution to solve this problem. In this approach, the user supplies the target level for desired accuracy and the simplification tolerance value is calculated accordingly.
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