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 AUTOMATED SELECTION OF TERRESTRIAL IMAGES FROM SEQUENCES FOR THE TEXTURE MAPPING OF 3D CITY MODELS Sébastien Bénitez and Caroline Baillard SIRADEL, 3 allée Adolphe Bobierre CS 24343, 35043 Rennes, France sben i tez @ siradel .com KEY WORDS: Building, Texture, Image, Sequences, Terrestrial, Automation. ABSTRACT: The final purpose of this study is to texture map existing 3D building models using calibrated images acquired with a terrestrial vehicle. This paper focuses on the preliminary step of automated selection of texture images from a sequence. Although not particularly complex, this step is particularly important for large-scale facade mapping where thousands of images might be available. Three methods inspired from well-know computer graphics techniques are compared: one is 2D-based and relies on the analysis of a 2D map; the two other methods use the information provided by a 3D vector database describing buildings. The 2D approach is satisfactory in most cases, but facades located behind low buildings cannot be textured. The 3D approaches provide more exhaustive wall textures. In particular, a wall-by-wall analysis based on 3D ray tracing is a good compromise to achieve a relevant selection whilst limiting computation. 1. INTRODUCTION With the development of faster computers and more accurate sensors (cameras and lasers), the automatic and large-scale production of a virtual 3D world very close to ground truth has become realistic. Several research laboratories around the world have been working on this issue for some years. Früh and Zakhor have proposed a method for automatically producing 3D city models using a land-based mobile mapping system equipped with lasers and cameras; the laser points are registered with an existing Digital Elevation Model or vector map, then merged with aerial LIDAR data (Früh and Zakhor, 2003; Früh and Zakhor, 2004). At the French National Geographical Institute (IGN), the mobile mapping system Stereopolis has been designed for capturing various kinds of information in urban areas, including laser points and texture images of building facades (Bentrah et ai, 2004). The CAOR laboratory from ENSMP has also been working on a mobile system named LARA-3D for the acquisition of 3D models in urban areas (Brun et ai, 2007; Goulette et ai, 2007), based on laser point clouds, a fish-eye camera, and possibly an external Digital Elevation Model. Recently, a number of private companies have commercialized their own mobile mapping systems for 3D city modeling, like StreetMapper or TITAN for instance (Hunter, 2009; Mrstik et al., 2009). The purpose of such systems is often the 3D modeling as well as the texture mapping of the 3D models. In this study we are interested in texturing existing 3D building models by mapping terrestrial images onto the provided façade planes. As a part of the mapping strategy, one first needs to determine which images each façade can be seen from. It is particularly important for large-scale facade texture mapping where thousands of images can be available. Every single image can be relevant for the final texturing stage. There are few references on this issue. In (Pénard et ai, 2005) a 2D map is used to extract the main building facades and the corresponding images. All the images viewing at least a part of a façade are selected. In (Haala, 2004), a panoramic camera is used and a single image is sufficient to provide texture for many façades. Given a façade, the best view is the one providing the highest resolution. It is selected by analyzing the orientations and distances of the building facades in relation to the camera stations. In (Aliène, 2008), a façade is represented by a mesh. Each face of the mesh is associated to one input view by minimizing an energy function combining the total number of texels representing the mesh in the images, and the color continuity between two neighbouring faces. In our study, only two triangles per facade are available, and a façade texture generally consists of a mixture of 4 to 12 input views. The following mapping strategy has been chosen for texturing a given façade: Pre-selecting a set of relevant input images, from which the façade can be seen; Merging these images into a single texture image; Registering the texture image with the existing façade 3D model. This paper only focuses on the first stage. The purpose of this operation is to select a set of potentially useful images based on purely geometrical criteria. The generation of a seamless texture image without occlusion artifacts will be handled within the second stage. Three possible approaches for the image pre selection are presented and discussed. The first approach is similar to the one used in (Pénard et al., 2005) and relies on the analysis of a 2D map. The two other methods use the information provided by a 3D vector database describing buildings. All methods are based on standard techniques commonly used in computer graphics for visibility computations, namely the ray-tracing and z-buffering techniques (Strasser, 1974). These two techniques have now been used for decades and are very well known in the computer graphics community. They can easily be optimized and accelerated via a hardware implementation. This paper is organized as follows. Section 2 presents the test data set used for this study. Sections 3, 4 and 5 detail the three selection methods. The results and perspectives are discussed in section 6. 2. TEST DATASET The test area is a part of the historical center of the city of Rennes in France. It is 1 km 2 wide and corresponds to the densest part of the city. Existing 3D building models were provided with an absolute accuracy around lm. It contains 1475 buildings consisting of 11408 walls. The texture image database associated to the area was simulated via a virtual path created through the streets. A point was created every 5 meters along this path. Each point is associated to two cameras facing the left and the right sides of the path. The camera centers are located at 2.3 meters above the ground in order to simulate a vehicle height. The internal and external parameters of the cameras are approximately known. The path is about 4.9 kilometers long,

Access restriction

Copyright

fullscreen: CMRT09

Monograph

Chapter

Table of contents

Cite and reuse

Monograph

Chapter

Image

Image fragment

Citation links

Citation recommendation