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:: AUTOMATED SELECTION OF TERRESTRIAL IMAGES FROM SEQUENCES FOR THE TEXTURE MAPPING OF 3D CITY MODELS Sébastien Bénitez and Caroline Baillard

Document type:: Monograph

Structure type:: Chapter

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation and it contains 990 points and 1980 cameras views (see Figure 1). It includes loops, self-intersections and close parallel roads. As a result a building wall can be seen from several locations within the path. Figure 1. Test area, Rennes historical center. The virtual path is depicted in red. 3. 2D RAY TRACING 3.1 Principle The 2D approach is based on ray-tracing. Each camera is analyzed in turn. The walls are represented by 2D segments. For each camera a set of compatible wall segments is pre selected using three criteria (see Figure 2): Distance criterion: the wall is located within a given distance from the camera center. Half-plane criterion: at least one point of the wall segment is located in the half-space in front of the camera Backface culling criterion: the wall is facing the camera. The compatible wall segments define a set of candidate walls that might be visible from the current camera. An example of pre-selection is shown in Figure lOa-b. Figure 2. The three criteria for the selection of candidate walls: (black=pre-selected walls, red=rejected walls) The 2D-tracing technique is then applied to the candidate wall segments. First a beam of 2D lines is defined passing through the camera center point and regularly distributed within the field of view of the camera. Then the closest intersected candidate wall segment is selected as a visible wall. When all the cameras have been processed then each wall can be associated to the list of cameras that can view it. 3.2 Test results The method was tested with various numbers of rays per camera. The distance threshold was arbitrarily set to 150m, distance above which the texture resolution is low enough to be discarded. The computing time includes reading and exporting steps. Numerical results are shown in Table 1. Between 10 and 13% of the walls are detected as visible by the process. Figure 3 shows the evolution of the wall number and the computing time with the number of rays. A qualitative example of selected walls can be found in Figure 10c. Ray # Total # of selected walls Avg # of cameras per wall Computing time 10 1176(10.3%) 4.54 7s 50 1391 (12.1%) 4.95 11s 100 1450(12.7%) 5.04 17s 500 1507 (13.2%) 5.14 50s Table 1. Results of 2D ray tracing 0 100 200 300 400 500 600 Rays # 0) E "■3 U) c V- 3 Q. E o o o 3 CD Figure 3 -Number of visible walls and computing time in relation to the number of rays 3.3 Discussion The variations in the number of selected walls come either from walls located far away of the camera, or from walls almost aligned with the camera center. When the number of rays is small, then many walls are located between two rays and are therefore not selected (see Figure 4). In our configuration, a number of rays around 100 seems to be a good compromise to get a maximum number of relevant images per building wall. The main advantage of the 2D approach is the speed. It is also very simple and quick to implement. However it does not take building heights into account. Yet a low building (garage, shop, etc) may only mask the bottom part of higher buildings located behind it, especially if the camera is located on the top of a vehicle (see examples in Figure 5 and Figure 11 a-c). Therefore it seems very important to make use of 3D information within the selection process.

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