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:: REFINING BUILDING FACADE MODELS WITH IMAGES Shi Pu and George Vosselman

Document type:: Monograph

Structure type:: Chapter

In: Stilla U, Rottensteiner F, Paparoditls N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 217 REFINING BUILDING FACADE MODELS WITH IMAGES Shi Pu and George Vosselman International Institute for Geo-Information Science and Earth Observation (ITC) Hengelosestraat 99, P.O. Box 6, 7500 AA Enschede, The Netherlands spu@itc.nl, vosselman@itc.nl Commission III/4 KEY WORDS: building reconstruction, data fusion, image interpretation ABSTRACT: Laser data and optical data have a complementary nature to 3D features’ extraction. Building reconstruction by fusion of the two data sources can reduce the complexity of approaches from either side. In this paper we present a model refinement method, which uses the strong lines extracted from close-range images to improve building models reconstructed from terrestrial laser point clouds. First, model edges are projected from model space to image space. Then significant line features are extracted from an image with Canny edge detector and Hough transformation. Each model edge is then compared with its neighboring image lines to determine the best match. Finally the model edges are updated according to their corresponding image lines. The refinement process not only fixes certain geometry errors of the original models, but also adapts the models to the image data, so that more accurate texturing is achieved. 1 INTRODUCTION The technique of automated building facade reconstruction is use ful to various applications. For urban planning, building facade models provide important references to the city scenes from the street level. For historical building documentation, a large num ber of valuable structures are contained on the facades, which should be recorded and reconstructed. For all virtual reality ap plications with users’ view on the street, such as virtual tourism and computer games, the accuracy and/or realistic level of the building facade models are vital to successfully simulate an ur ban environment. A number of approaches (Dick et al., 2001, Schindler and Bauer, 2003, Frueh et al., 2005, Pollefeys et al., 2008) are available for reconstructing building facades automatically or semi-automatically. Close range image and terrestrial laser point cloud are the com monly used input data. Image based building reconstruction has been researched for years. From multiple 2D images captured from different positions, 3D coordinates of the image features (lines for example) can be calculated. Although acquisition of images is cheap and easy, the difficulties of image understand ing make it still difficult to automate the reconstruction using only images. Laser altimetry has been used more and more in recent years for automated building reconstruction. This can be explained by the explicit and accurate 3D information provided by laser point clouds. Researches (Vosselman. 2002, Frueh et al., 2004, Brenner, 2005) suggest that the laser data and images are complementary to each other, and efficient integration of the two data types will lead to a more accurate and reliable extraction of three dimensional features. In the previous work we presented a knowledge based building facade reconstruction approach, which extracts semantic facade features from terrestrial laser point clouds and combines the fea ture polygons to water-tight polyhedron models (Pu and Vossel man, 2009). Some modeling errors still exist, and some of them can hardly be corrected by further exploiting the laser data. In this paper, we present a model refinement method which uses strong line features extracted from images to improve the build ing facade models generated from only terrestrial laser points. The refinement not only fixes the models’ geometry errors, but also solves inconsistencies between laser and image data, so that a more accurate texturing can be achieved. This paper is organized as follows. Section 2 gives an overview of the presented method. Section 3 provides the context research of building reconstruction from terrestrial laser scanning. Section 4 explains the preprocessing steps such as perspective conversion and spatial resection, to make images usable for refining mod els. Section 5 elaborates the image processing algorithms used for significant line extraction and the matching and refinement strategies. Experiments on three test cases are discussed in sec tion 6. Some conclusions and outlooks are drawn in the final section. 2 METHOD OVERVIEW A building facade model may contain various errors. For a model reconstructed from terrestrial laser points, the model edges may have certain offset with their actual positions. These errors are caused by gaps in laser points and the limitations of laser data based reconstruction algorithms. Edges are delineated accurately in images. After registering to the model space, image lines can provide excellent reference from which the model edge errors can be fixed. Another necessity of this refinement is to solve the in consistencies between the laser space and the image space, so that accurate texturing can be achieved. Before starting the refinement, a 2D image needs to be referenced to the 3D model space, a problem often referred as spatial resec tion in photogrammetry. We use the standard resection solution of collinearity equations, which requires minimum three image points with their coordinates in model space. To find significant line features from an image, we first detect edges using the Canny algorithm (Canny, 1986), then apply the Hough transform to fur ther extract strong line features from edges. Then model edges are projected to the image space and matched with the image lines. The best match is determined by the geometric properties of candidates and the geometric relations between candidates and the model edge. Finally each model edge with successful match ing is projected to the matched image line accordingly, and model edges without any matching are also adjusted to maintain a well shape. Figure 1 gives a flowchart of the refinement process.

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