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

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 99 Figure 4. Example of a “missed” wall, (a) 10 rays per camera: the current wall is “missed” by several cameras (red dots); (b) 50 rays per camera: the current wall is seen by all the cameras (black dots) (a) 3D view (b) Cameras seeing the (c) Cameras selected with red wall the 2D approach Figure 5. Example of incomplete camera selection 4. 3D Z-BUFFERING 4.1 Principle The second approach is 3D-based and relies on a z-buffer technique. Each camera is analysed in turn. A set of candidate walls is first associated to the current camera as in described in section 3, using distance, half-plane and backface culling criteria. The camera is then associated to a label image identifying the walls seen by the camera, and a depth image indicating the distance from the camera centre to the walls. Finally, after all the cameras have been processed, each wall can be associated to the list of cameras that can view it. 4.2 Test results In order to reduce computing time, the distance and label images are sub-sampled at coarser resolutions. The tests were performed at a sampling resolution of 5, 10 and 20 pixels. They are shown in Table 2. It is important to note that the algorithm was not optimised and the graphical card not used. An example of depth image is shown in Figure 6. Z-buffer resolution Image size (hxw) Total # of visible walls Avg # of cam. per wall Computing time 5 pixels 216x384 2310 (20.2%) 4.40 61min58s 10 pixels 108x192 2249 (19.7%) 4.41 52min36s 20 pixels 54x96 2186 (19.2%) 4.35 43min54s Table 2. Results of 3D ray tracing Figure 6. Example of a depth image 4.3 Discussion Using this approach, 50% more walls can be textured. In particular, all facades located behind other buildings can now be textured, whereas they were discarded with the 2D approach. In the example of Figure lOd, the circled area shows an example of a high building visible from the current camera but only selected with the 3D approach. As the measures are very dense, even small walls, walls distant from the path or wall aligned with the path can theoretically be textured. In return, many selected walls are only partly visible, and would actually have a very poor texture quality. It is important to introduce a contribution culling technique, in order to discard wall images inappropriate for texture mapping. In the current implementation, another drawback of the method is the computing time. Using a hardware implementation directly into the Graphical Processing Unit of the graphic card should solve this problem. A hierarchical z-buffer technique could also be investigated (Greene, 1993). Finally, the selection process must be entirely completed before being able to further process a façade, which might not be compatible with a large-scale production process. 5. 3D RAY TRACING 5.1 Principle The last approach combines the main advantages of the two previous ones: speed and use of 3D information. It is a 3D extension of the 2D approach based on ray tracing. However, the analysis is performed wall-by-wall rather than camera-by camera. Given a wall, a set of candidate cameras is selected using a method similar to the one described in section 3:

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