CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: 3D BUILDING RECONSTRUCTION FROM LIDAR BASED ON A CELL DECOMPOSITION APPROACH Martin Kada, Laurence McKinle

Document type:: Monograph

Structure type:: Chapter

Concepts, Algorithms, and Evaluation In our approach, we assume that the majority of residential houses have either one main section or multiple connected sections, with additional smaller extensions, and that a partition thereof can be properly derived from the outline polygon. Once such a partition is found, a general geometrical description of the roof can be constructed by assigning a parameterized standard shape to each section. However, the difficulty to generate correct facade and roof shapes from a partition increases with the number, shape and arrangement of its elements. We therefore generate a set of non-overlapping, mostly quadrilateral shaped polygons that together approximate the original footprint (cp. Figure 2). Other ground shapes may also occur, but those primitives are then restricted to only bear certain roof shapes. Cell Decomposition As referred to in (Foley et. al, 1996), a spatial partitioning representation in solid modelling, where solids are decomposed into nonintersecting, typically parameterized primitives, is called cell decomposition. Serving as the basis for the building reconstruction process, we first of all generate such a partition for each building footprint. As mentioned above, this is done solely from information found in the building’s outline. The big challenge herein is to avoid decomposing the area in too many small cells, for which it becomes increasingly difficult to reconstruct a well-shaped roof, especially if the building outline is very detailed and consists of many short line sections (see Figure 4). So instead of using all the available lines from the outline polygon and infinitely extend them to split the footprint, an adequate subset must be found that results in a set of primitives that together reflects well the characteristic shape of the building. However, the resulting outline will not be identical to the original one, but rather be a generalization thereof. So to best resemble the outline, the set of decomposition lines should approximate well the original points and line segments. Figure 2. Building footprint and its decomposition into cells. The roof is then reconstructed by determining a shape for each cell from the LIDAR points with regard to the neighbour cells (cp. Figure 3). After identifying the points inside a cell, the normal vectors from the local regression planes of the points are tested against all possible shapes. Here, only the orientation is used to speed up comparing the many shapes we support. The one that best fits is then chosen and its parameters estimated from the 3D point coordinates. Cells whose neighbour configurations suggest comer-, t- and cross-junctions are examined again and replaced if a junction shape can be fitted according to the neighbour shapes and parameters. Figure 4. Cell decomposition of a building footprint using all line segments of the outline and only an averaged subset. Our algorithm for generating cell decompositions from given outlines has been thoroughly described in the context of 3D building generalization (see e.g. (Kada, 2007)). But instead of generating 3D decomposition planes from the facade polygons of a 3D building model, the 2D decomposition lines are now generated from the 2D outline. In a nutshell, the line segments are grouped into subsets of “parallel” lines that are pair wise a maximum distance away from each other. This is the generalization distance, which means in this context, that the cells resulting from the footprint partitioning will not have sides that are shorter than this length. Line segments are considered parallel if the angle between their directions is below an angle threshold. This allows for a better generalization of connected line segments and therefore helps to keep the number of generated cells low. For each subset of line segments, the associated decomposition line is computed by averaging the line equations of its elements. Short line segments of arbitrary direction, but whose endpoints are both closer to the decomposition line than the parallel line segments, are associated with this subset, but will not contribute to the averaging of this or any other decomposition line. For example, the green line segments on the left side of Figure 5 are considered parallel under the chosen angle threshold of 15 degrees. The added perpendicular distance of any two endpoints

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