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:: COMPETING 3D PRIORS FOR OBJECT EXTRACTION IN REMOTE SENSING DATA Konstantinos Karantzalos and Nikos Paragios

Document type:: Monograph

Structure type:: Chapter

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 128 (a) Prior Building Models (<£>i j): i determines the shape of the footprint and j the roof type (b) The family Iq j which has a rectangular footprint (i = 1). (c) Building’s main height h m and roofs height h r (x,y) Figure 2: Hierarchical Grammar-Based 3D Prior Models. The case of Building Modeling: Building’s footprint is determined implicitly from the Eid- h m and h r (x,y) are recovered for ev ery point {E$d) and thus all the different type of roofs j are mod eled. most appropriate model and then determine the optimal set of parameters aiming to recover scene’s geometry (Figure 1). The proposed objective function consists of two segmentation terms that guide the selection of the most appropriate typology and a third DEM-driven term which is being conditioned on the typol ogy. Such a prior-based recognition process can segment both rural and urban regions (similarly to (Matei et al., 2008)) but is able, as well, to overcome detection errors caused by the mislead ing low-level information (like shadows or occlusions), which is a common scenario in remote sensing data. Our goal was to develop a single generic framework (with no step-by-step procedures) that is able to efficiently account for multiple 3D building extraction, no matter if their number or shape is a priori familiar or not. In addition, since usually for most sites multiple aerial images are missing, our goal was to provide a solution even with the minimum available data, like a single panchromatic image and an elevation map (produced either with classical photogrammetric multi-view stereo techniques ei ther from LIDAR or INSAR sensors), contrary to approaches that were designed to process multiple aerial images or multispectral information and cadastral maps (like in (Suveg and Vosselman, 2004),(Rottensteiner et al., 2007),(Sohn and Dowman, 2007)), data which much ease scene’s classification. Doing multiview stereo, using simple geometric representations like 3D lines and planes or merging data from ground sensors was not our interest here. Moreover, contrary to (Zebedin et al., 2008), the proposed, here, variational framework does not require as an input dense height data, dense image matching processes and a priori given 3D line segments or a rough segmentation. 2 MODELING TERRAIN OBJECTS WITH 3D PRIORS Numerous 3D model-based approaches have been proposed in lit erature. Statistical approaches (Paragios et al., 2005), aim to de scribe variations between the different prior models by measuring the distribution of the parameter space. These models are capable to model building with rather repeating structure and of limited complexity. In order to overcome this limitation, methods using generic, parametric, polyhedral and structural models have been considered (Jaynes et al., 2003),(Kim and Nevatia, 2004),(Su veg and Vosselman, 2004),(Dick et al., 2004),(Wilczkowiak et al., 2005),(Forlani et al., 2006),(Lafarge et al., 2007). The main strength of these models is their expressional power in terms of complex architectures. On the other hand, inference between the models and observations is rather challenging due to the impor tant dimension of the search space. Consequently, these models can only be considered in a small number. More recently, proce dural modeling of architectures was introduced and vision-based reconstruction in (Muller et al., 2007) using mostly facade views. Such a method recovers 3D using an L-system grammar (Muller et al., 2006) that is a powerful and elegant tool for content cre ation. Despite the promising potentials of such an approach, one can claim that the inferential step that involves the derivation of models parameters is still a challenging problem, especially when the grammar is related with the building detection procedure. Hierarchical representations are a natural selection to address com plexity while at the same time recover representations of accept able resolution. Focusing on buildings, our models involve two components, the type of footprint and the type of roof (Figure 2). Firstly, we structure our prior models space by ascribing the same pointer i to all models that belong to the family with the same footprint. Thus, all buildings that can be modeled with a rectangular footprint are having the same index value i. Then, for every family (i.e. every i) the different types of building tops (roofs) are modeled by the pointer j (Figure 2b) Under this hierar chy <E>i,j, the priors database can model from simple to very com plex building types and can be easily enriched with more complex structures. Such a formulation is desirously generic but forms a huge search space. Therefore, appropriate attention is to be paid when structuring the search step. Given the set of footprint priors, we assume that the observed building is a homographic transformation of the footprint. Given, the variation of the expressiveness of the grammar, and the de grees of freedom of the transformation, we can now focus on the 3D aspect of the model. In such a context, only building’s main height hm and building’s roof height h r (x, y) at every point need to be recovered. The proposed typology for such a task is shown in Figure 2. It refers to the rectangular case but all the other families can respectively be defined. More complex footprints, with usually more than one roof types, are decomposed to sim pler parts which can, therefore, similarly recovered. Given an im age J(x, y) at domain (bounded) il E i? 2 and an elevation map 7i(x, y) -which can be seen both as an image or as a triangulated point cloud- let us denote by h rn the main building's height and by P m the horizontal building’s plane at that height. We proceed by modeling all building roofs (flat, shed, gable, etc.) as a combi nation of four inclined planes. We denote by Pi, P2, P3 and P4 these four roof planes and by , U2, u>3 and u>4, respectively, the four angles between the horizontal plane h m and each inclined plane (Figure 2). Every point in the roof rests strictly on one of these inclined planes and its distance with the horizontal plane is the minimum compared with the ones formed by the other three planes. With such a grammar-based description the five unknown param eters to be recovered are: the main height h m (which has a con stant value for every building) and the four angles u. In this way all -but two- types of buildings tops/roofs can be modeled. For example, if all angles are different we have a totally dissymmetric roof (Figure 2b - $1.5), if two opposite angle are zero we have a

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