Abstract—Modern **spaceborne** synthetic aperture radar (SAR) sensors, such as TerraSAR-X/TanDEM-X **and** COSMO-SkyMed, can deliver very high resolution (VHR) data beyond the inher- ent spatial scales **of** buildings. Processing these VHR data with advanced interferometric techniques, such as SAR tomography (**TomoSAR**), allows for the generation **of** four-dimensional **point** **clouds**, containing not only the 3-D positions **of** the scatterer location but also the estimates **of** seasonal/temporal deformation on the scale **of** centimeters or even millimeters, making them very attractive for generating dynamic city models **from** space. Motivated by these chances, the authors have earlier proposed ap- proaches that demonstrated first attempts toward **reconstruction** **of** **building** facades **from** this class **of** data. The approaches work well when high density **of** facade points exists, **and** the full shape **of** the **building** could be reconstructed if data are available **from** multiple views, e.g., **from** both ascending **and** descending orbits. However, there are cases when no or only few facade points are available. This usually happens for lower height buildings **and** renders the **detection** **of** facade points/regions very challenging. Moreover, problems related to the visibility **of** facades mainly facing toward the azimuth direction (i.e., facades orthogonally ori- ented to the flight direction) can also cause difficulties in deriving the complete structure **of** individual buildings. These problems motivated us to reconstruct full 2-D/3-D **shapes** **of** buildings via exploitation **of** roof points. In this paper, we present a novel **and** complete data-driven framework for the **automatic** (parametric) **reconstruction** **of** 2-D/3-D **building** **shapes** (or footprints) using unstructured **TomoSAR** **point** **clouds** particularly generated **from** one viewing angle only. The proposed approach is illustrated **and** validated by examples using **TomoSAR** **point** **clouds** generated using TerraSAR-X high-resolution spotlight data stacks acquired **from** ascending orbit covering two different test areas, with one containing simple moderate-sized buildings in Las Vegas, USA **and** the other containing relatively complex **building** structures in Berlin, Germany.

Mehr anzeigen
19 Mehr lesen

With data provided by modern meter-resolution SAR sensors **and** advanced multi-pass interferometric techniques such as tomographic SAR inversion (**TomoSAR**), it is now possible to generate 4-D (space-time) **point** **clouds** **of** the illuminated area with **point** density **of** approx. 1 million points/km 2 . However, due to side looking geometry, these **point** **clouds** exhibit much higher density **of** points on **building** façades in contrast to nadir looking LiDAR geometry (typically used for object **reconstruction**). Moreover, temporally incoherent objects such as trees cannot be reconstructed **from** multi-pass **spaceborne** SAR image stacks **and** provide moderate 3-D positioning accuracy in the order **of** 1m as compared to airborne LiDAR systems (around 0.1m). Despite **of** these special considerations, object **reconstruction** **from** these high quality **point** **clouds** can greatly support the **reconstruction** **of** dynamic city models that could be potentially used to monitor **and** visualize the dynamics **of** urban infrastructure in very high level **of** details. Motivated by these chances, earlier approaches have been proposed to reconstruct **building** façades **from** this class **of** data. E.g., experimental results provided in (Zhu, 2014) **and** (Shahzad, 2014) over smaller **and** larger areas demonstrate that façade **reconstruction** is an appropriate first step to detect **and** reconstruct **building** shape when dense points on the façade are available. In particular, when data **from** multiple views e.g., **from** both ascending **and** descending orbits, are available, the full shape **of** buildings can be reconstructed using extracted façade points. However, there are cases when no or only few

Mehr anzeigen
Recent advances in very high resolution synthetic aperture radar (SAR) imagery **and** its key attributes—self-illumination **and** all-weather capability—have attracted the attention **of** many remote sensing analysts in the characterization **of** urban environments. Various techniques have been developed that make use **of** SAR imagery for **building** **detection** **and** recon- struction. Complex **building** **shapes** surrounded by roads **and** other structures make **building** **detection** a challenging problem. One possible solution is to discriminate buildings **from** other objects using the **building** height **and** width measurements extracted **from** SAR imagery [6]. The key issue is then the **building** height retrieval. For this purpose, various methods have been developed, including using sound electromagnetic models [7], layover [8] or shadow analysis [9] **and** simulation- based methods [10]. In [11], an approach particularly suited for the **detection** **and** extraction **of** large buildings based on information acquired **from** interferometric SAR (InSAR) data is proposed. Stochastic model-based **and** low level feature-based approaches for extracting **and** reconstructing buildings **from** a single SAR intensity image are presented in [12] **and** [13], respectively. Wang et al. [14] presented an approach for build- ing extraction **from** high-resolution single-aspect polarimetric SAR data. Since, in urban areas, the structures are densely packed, the appearance **of** one particular **building** is dependent on the viewing angle **of** the sensor. Using a single-view SAR image, it is difficult to detect buildings that have no orientation component in the sensor’s azimuth direction [15]. To overcome this limit, multiview SAR acquisitions are required. In [16], an approach for estimating **building** dimensions using multi- view SAR images is presented. Bolter **and** Leberl [17] **and** Thiele et al. [18] proposed methods for **building** **reconstruction** based on multiview InSAR data. **Building** **reconstruction** in context to stereoscopic SAR radargrammetric **and** multiview polarimetric SAR acquisitions has also been used in [19] **and** [20], respectively.

Mehr anzeigen
12 Mehr lesen

Matei et al., 2008]. Sun **and** Salvaggio [2013] create segment boundaries by overlying a **2D** grid to their segmented **point** cloud: Each grid edge connecting an empty **and** an occupied grid cell is chosen as border edge. Very similarly, Zhou **and** Neumann [2008] define boundaries by tracing the closest LiDAR points to those edges. Rottensteiner [2003] define separation boundary lines between adjacent segments **from** the Delaunay triangulation: Differently segmented points connected by triangulation edges are boundary points, **and** the corresponding Voronoi edges form the boundary. Dorninger **and** Pfeifer [2008], Kada **and** Wichmann [2012] **and** Sampath **and** Shan [2007] use a modified convex hull approach called alpha **shapes**, in which each next boundary vertix is determined only **from** the local neighborhood **of** the previous vertex. If the local neighborhood is determined by a fixed radius, alpha **shapes** produce only satisfactory results if the **point** density is regular. Therefore, Sampath **and** Shan [2007] define the neighborhood with a rectangle whose extents **and** orientation depend on the along-track **and** across-track LiDAR sampling characterisitics. [Wang **and** Shan, 2009] identify unconnected boundary points by creating the convex hull **of** each point’s local neighborhood. If the **point** is a vertex **of** this convex hull, it is chosen as a **building** boundary vertex. Lafarge **and** Mallet [2012] determine each boundary **point** based on its distance to the line fitted through its neighborhood.

Mehr anzeigen
75 Mehr lesen

With improved sensor resolution **and** advanced multi-pass interferometric techniques such as SAR tomographic inversion (**TomoSAR**), it is now possible to reconstruct both shape **and** motion **of** urban infrastructures. These sophisticated techniques not only opens up new possibilities to monitor **and** visualize the dynamics **of** urban infrastructure in very high level **of** details but also allows us to take a step further towards generation **of** 4D (space-time) or even higher dimensional dynamic city models that can potentially incorporate temporal (motion) behaviour along with the **3D** information. Motivated by these chances, this paper presents a post processing approach that systematically allows **automatic** **reconstruction** **of** **building** façades **from** 4D **point** cloud generated **from** tomographic SAR processing **and** put the particular focus on robust **reconstruction** **of** large areas. The approach is modular **and** consists **of** extracting facade points via **point** density estimation procedure based on directional window approach. Segmentation **of** facades into individual segments is then carried out using an unsupervised clustering procedure combining both the density-based clustering **and** the mean-shift algorithm. Subsequently, points **of** individual facade segments are identified as belonging to flat or curved surface **and** general 1st **and** 2nd order polynomials are used to model the facade geometry. Finally, intersection points **of** the adjacent façades describing the vertex points are determined to complete the **reconstruction** process. The proposed approach is illustrated **and** validated by examples using **TomoSAR** **point** **clouds** over the city **of** Las Vegas generated **from** a stack **of** TerraSAR- X high resolution spotlight images.

Mehr anzeigen
This paper presents a novel workflow for data-driven **building** **reconstruction** **from** Light **Detection** **and** Ranging (LiDAR) **point** **clouds**. The method comprises **building** extraction, a detailed roof segmentation using region growing with adaptive thresholds, segment bound- ary creation, **and** a structural **3D** **building** **reconstruction** approach using adaptive 2.5D Dual Contouring. First, a **2D**-grid is overlain on the segmented **point** cloud. Second, in each grid cell **3D** vertices **of** the **building** model are estimated **from** the corresponding LiDAR points. Then, the number **of** **3D** vertices is reduced in a quad-tree collapsing procedure, **and** the remaining vertices are connected according to their adjacency in the grid. Roof segments are represented by a Triangular Irregular Network (TIN) **and** are connected to each other by common vertices or - at height discrepancies - by vertical walls. Resulting **3D** **building** models show a very high accuracy **and** level **of** detail, including roof superstructures such as dormers. The workflow is tested **and** evaluated for two data sets, using the evaluation method **and** test data **of** the “ISPRS Test Project on Urban Classification **and** **3D** **Building** **Reconstruction**” (Rottensteiner et al., 2012). Results show that the proposed method is comparable with the state **of** the art approaches, **and** outperforms them regarding undersegmentation **and** completeness **of** the scene **reconstruction**.

Mehr anzeigen
algorithms, **3D** objects are reconstructed by surface fitting in the segmented **building** regions [10]. Numerous methods are employed for **building** roof segmentation **and** **reconstruction** such as unsupervised clustering approaches [11], region growing algorithms [12] **and** graph based matching techniques [13]. These techniques, however, cannot be directly applied to **TomoSAR** **point** **clouds** due to different object contents illuminated by the side looking SAR. In this paper, we propose an approach for **building** façade **detection** **and** **reconstruction** **from** **TomoSAR** **point** **clouds**. The proposed approach is illustrated **and** validated by examples using **TomoSAR** **point** **clouds** generated **from** a stack **of** 25 TerraSAR-X high spotlight images reported in [6]. Our test **building** is the Bellagio hotel in Las Vegas. Fig. 1 (b) shows the TerraSAR-X mean intensity map **of** the area **of** interest while Fig. 1 (a) is the corresponding optical image. Fig. 1 (c) gives an overview **of** the input **TomoSAR** **point** **clouds** in UTM coordinates.

Mehr anzeigen
world coordinates enable the generation **of** high quality **TomoSAR** **point** **clouds**, containing not only the **3D** positions **of** the scatterer location but also estimates **of** seasonal/temporal deformation, that are very attractive for generating 4-D city models **from** space. However there are some special considerations associated to these **point** **clouds** that are worth to mention [1]: 1) **TomoSAR** **point** **clouds** deliver moderate **3D** positioning accuracy on the order **of** 1 m; 2) few number **of** images **and** limited orbit spread render the location error **of** **TomoSAR** points highly anisotropic, with an elevation error typically one or two orders **of** magnitude higher than in range **and** azimuth [2]; 3) Due to the coherent imaging nature, temporally incoherent objects such as trees cannot be reconstructed **from** multipass **spaceborne** SAR image stacks; **and** 4) **TomoSAR** **point** **clouds** possess much higher density **of** points on the **building** façades due to side looking SAR geometry enabling systematic **reconstruction** **of** buildings footprint via façade points analysis. As depicted over smaller **and** larger areas in [1] **and** [3], façade **reconstruction** turns out to be an appropriate first step to detect **and** reconstruct **building** shape **from** these **point** **clouds** when dense points on the façade are available. Especially, when data **from** multiple views e.g., **from** both ascending **and** descending orbits, are available, the

Mehr anzeigen
In recent years, by introducing the LiDAR system **and** using the **3D** geo-referenced data, different methods have been proposed for **3D** **building** modelling. In recent years, several methods have been proposed for **3D** **building** **reconstruction**. The algorithms comprise methods that only employ LIDAR **point** cloud for model generation while some others use additional data sources such as aerial or satellite imagery. (Ma, 2006) proposed a methodology **of** **3D** **building** model **reconstruction** will be examined based on the integration **of** aerial photographs **and** LIDAR data. The methodology is comprised **of** two elements. The first one is to reconstruct **3D** **building** models **from** LIDAR data. Rough **building** models are the outcome **of** this step. The second element is to refine the rough models with information derived **from** aerial photographs. Cheng et al. proposed an approach by integrating aerial imagery **and** LiDAR data to reconstruct **3D** **building** models (Cheng, Gong, Li, & Liu, 2011). In this approach, an algorithm for determination **of** principal orientations **of** a **building** was introduced **and** **3D** boundary segments were then determined by incorporating LiDAR data **and** the **2D** segments extracted **from** images, a strategy including **automatic** recovery **of** lost boundaries was finally used for **3D** **building** model **reconstruction**. The focus **of** this study is to improve the quality **of** **building** boundaries, not **building** roofs. (Satari, 2012) proposed a multi-resolution hybrid approach for the **reconstruction** **of** **building** models **from** LiDAR data. The **detection** **of** the main roof planes is obtained

Mehr anzeigen
The Hough transform is a feature extraction technique used in image analysis, computer vision, **and** digital image processing (Shapiro u. Stockman [2001]). It estimates the parameters **of** a shape **from** its points. The purpose **of** the technique is to find imperfect instances **of** objects within a certain class **of** **shapes** by a voting procedure. This voting procedure is carried out in a parameter space, **from** which object candidates are obtained as local maxima in a so-called accumulator space that is explicitly constructed by the algorithm for computing the Hough transform. It can be used to detect lines, circles **and** other primitive **shapes** if their parametric equation is known. In principle, it works by mapping every **point** in the data to a manifold in the parameter space. This manifold describes all possible variants **of** the parametrized primitive. Making the parametrizing simpler or limit the parameter space speed up the algorithm. This is especially true for **3D** shape **detection**, where for example to detect a plane using the plane equation ax+by+cz+d=0 requires **3D** Hough space, which will quickly occupy large space **of** memory **and** performance since all possible planes in every transformed **point** **clouds** need to be examined. A plane can also be fitted based on normalized normal vectors using only two **of** the Euler angles **and** distance **from** origin, α, β **and** d. There is no need to the third Euler angle since the information when transforming around the axis in redundant (Hulik u. a. [2014]).

Mehr anzeigen
103 Mehr lesen

Moreover, the reconstructed façades could remain either incomplete or break into more than one segment due to the following reasons: 1) Higher **building** structures present nearby can partly (or fully) occlude the façades **of** lower buildings; 2) Due to the geometrical shape, only very few points are available at some parts **of** **building** façades. Computed vertex points are therefore first categorized into two types: First type consists **of** vertices that are computed **from** the intersection **of** two adjacent façades, while the second type consists **of** the other vertices representing “open” endpoints. Reconstructed façades are later refined by inserting additional segments between the broken regions **and** extend those façades that remain incomplete by statistically analyzing **and** matching the local height distribution **of** the nearest open endpoint vertices.

Mehr anzeigen
114 Mehr lesen

shaped footprint could be reconstructed. Also, the approach utilizes roof points in determining the complete shape **of** the buildings **and** therefore resolves problems, as mentioned in (Shahzad **and** Zhu, 2015a), related to the visibility **of** the façades mainly pointing towards the azimuth direction **of** the SAR sensor. However, few points still need to be addressed. For instance, the **reconstruction** accuracy is restricted due to less number **of** available points **and** data gaps in the **TomoSAR** **point** cloud. This could be improved by incorporating data **from** other viewing angles **and**/or adding more constraints such as parallelism or using a model based approaches based on a library **of** low level feature sets. Also, we have compared our results to the OSM data which is regularly updated but not yet fully complete. Therefore, a more accurate ground truth would be needed for assessing the exact performance **of** the approach. Nevertheless, this paper presents the first demonstration **of** **automatic** **reconstruction** **of** 2-D/3-D **building** footprints **from** this class **of** data. Moreover, the developed methods are not strictly applicable to **TomoSAR** **point** **clouds** only but are also applicable to work on unstructured 3-D **point** **clouds** generated **from** a different sensor with similar configuration (i.e., oblique geometry) with both low **and** high **point** densities. In the future, we will explore the potential **of** extending the algorithm towards generation **of** automatically reconstructed complete watertight prismatic (or polyhedral) 3-D/4-D **building** models **from** space.

Mehr anzeigen
A considerable amount **of** studies addresses DSM-assisted build- ing footprint extraction. Some **of** them fuse data **from** different sensors, chiefly multispectral images **and** LiDAR-derived DSMs (Matikainen et al., 2010; Hermosilla et al., 2011; Grigillo **and** Kanjir, 2012). Those studies, however, rely on different data sources which may imply difficulties concerning the availability **and** temporal coincidence **of** the data. Exploiting the potentials **of** a single platform could yield a solution. Hence, a number **of** authors directly extracted footprint **shapes** **from** LiDAR **point** **clouds** (Wang et al., 2006; Zhang et al., 2006; Arefi et al., 2008) or nadir RGB imagery (Shorter **and** Kasparis, 2009). Stereo images also provide possibilities for detecting **building** geometries using optical **and** height information derived **from** the same data source (Arefi **and** Reinartz, 2013; Tian et al., 2014) or solely height infor- mation (Weidner, 1997). Photogrammetric techniques have been used to extract three-dimensional line segments for **3D** model generation (Zebedin et al., 2008). These line segments, however, require a good perspective coverage **of** the scene in order to be useful for **building** extraction. We therefore studied the potentials **and** limitations **of** extracting line segments **from** individual im- ages used for DSM generation **and** subsequently verifying them on the basis **of** the DSM.

Mehr anzeigen
Space-borne meter resolution SAR data, together with multi-pass InSAR techniques including persistent scatterer interferometry (PSI) **and** tomographic SAR inversion (**TomoSAR**), allow us to reconstruct the shape **and** undergoing motion **of** individual buildings **and** urban infrastructures [1]-[4]. **TomoSAR** in particular offer tremendous improvement in detailed **reconstruction** **and** monitoring **of** urban areas, especially man-made infrastructures [3]. The rich scatterer information retrieved **from** multiple incidence angles by **TomoSAR** in particular enables us to generate 4D **point** **clouds** **of** the illuminated area with a **point** density comparable to LiDAR. These **point** **clouds** can be potentially used for **building** façade **reconstruction** in urban environment **from** space with few considerations: 1) Side-looking SAR geometry enables **TomoSAR** **point** **clouds** to possess rich façade information; 2) Temporarily incoherent objects, e.g. trees, cannot be reconstructed **from** multi-pass space-borne SAR image stacks [8]; 3) The **TomoSAR** **point** **clouds** have a moderate **3D** positioning accuracy in the order **of** 1m, while (airborne) LiDAR provides accuracy typically in the order **of** 0.1m.

Mehr anzeigen
Note that big separation numbers n produce large configuration factors which can lead to erroneous measurements. As a consequence, an enlargement **of** the dipole lengths for Schlumberger, pole-dipole **and** dipole-dipole configurations seems reasonable, denoted with ”:”. Furthermore, it is recommended to shift the electrodes by the dipole length ”:” instead **of** the electrode distance ”.” along the profile. Thus, a reduced number **of** data is gained. However, this can be compensated by applying a larger maximum separation factor. The data points are assigned to a representative x-location, e.g. the midpoint, **and** the separation factor n. Since large n are associated with increased investigation depths, is seems reasonable to plot the data in a pseudo-section with n going down. Remember, that a pseudo-section is just a graphical representation **of** the measured data. However, experienced geophysicists are able to retrieve a concept **of** the earth’s structure without inverting the data.

Mehr anzeigen
160 Mehr lesen

In contrast to model-free exploration methods that focus on autonomy **and** real-time capability in unknown environments, model-based path planning algorithms rely on an available proxy model **of** the environment **and** focus on estimating a subsequent optimal path to maximize the coverage **and** accuracy **of** the object globally [ 10 , 11 , 35 – 38 ]. In contrary to active modeling, these explore-**and**-exploit methods do not receive any feedback **from** the acquired images during the exploitation flight, which demands high attention to the applied heuristics being used for generating the refinement path. The global optimization **of** coverage **and** accuracy, on the other hand, usually leads to larger completeness **and** smoother trajectories compared to model-free methods. Recent work has proposed to extend this procedure by iteratively refining the model **from** several subsequent flights, taking into account the remaining model uncertainty between each flight [ 38 , 39 ]. Furthermore, the execution **of** the optimized path is easy **and** fast for any kind **of** UAV by simply navigating alongside the optimized waypoints. The prior model can either be based on an existing map with height information [ 36 ] or is generated by photogrammetric reconstructions **from** a preceding manual flight at a safe altitude or via standard flight planning methods (e.g., regular grids or circular trajectories) [ 10 , 11 ] **and** is usually expressed by a set **of** discrete **3D** points in a voxel space [ 10 , 11 , 37 , 40 ] or by volumetric surfaces, such as triangulated meshes [ 35 , 36 , 38 , 41 ]. In order to define appropriate views for the optimized trajectory, camera viewpoint hypotheses are either regularly sampled in the free **3D** airspace [ 10 , 37 ] resulting in **3D** camera graphs, or are sparsely sampled in a **2D** view manifold [ 38 ] or in skeleton sets [ 42 ] around the object. Subsequently, an optimization is defined in order to find a connected subset **of** these viewpoint hypotheses to define a suitable path through the camera graph. Alternatively, the locations **of** the **of** regularly sampled viewpoint candidates can be continuously refined during the optimization [ 11 ]. As a means **of** assessing the suitability **of** camera viewpoints for the **reconstruction**, hand-crafted heuristics are usually defined considering the necessities for a successful SfM **and** MVS workflow. These include multi-view requirements [ 35 , 37 , 40 ], ground resolution [ 35 , 41 ], **3D** uncertainty [ 43 ] **and** the coverage **of** the object [ 10 , 11 , 37 ]. Instead **of** using hand-crafted heuristics, several works used machine learning methods to learn heuristics that allow predicting the confidence in the output **of** a MVS without executing it [ 27 , 43 , 44 ].

Mehr anzeigen
29 Mehr lesen

to efficiently search these representations in a queried image. Typically, an image is transformed to the corresponding feature space, i.e. it is processed into a new im- age represented by the defined features, **and** occurrences with little deviation to the objects representation, regarding a certain metric are picked as matches. Concern- ing the first subtask, works **from** the past two decades like Scale-Invariant Feature Transform (SIFT) (Lowe, 1999), Speeded-Up Robust Features (Bay et al., 2006) or Features **from** Accelerated Segment Test (Rosten **and** Drummond, 2006) considered the **detection** **and** description **of** highly distinctive **and** transformation robust feature points, to represent objects. Representative works like Histogram **of** Oriented Gra- dients (Dalal **and** Triggs, 2005), Local Binary Pattern (LBP) (Ahonen et al., 2006) or Region Covariance (Tuzel et al., 2006) focused on describing feature information **from** the local area **of** **and** around the objects. In recent years, also advanced feature descriptor variants like RootSIFT Principal Component Analysis (RootSIFT-PCA) (Bursuc et al., 2015), Domain-Size Pooling SIFT (Dong **and** Soatto, 2015) or Ro- tation Invariant Co-occurrence among adjacent LBPs (X. Qi et al., 2014) were re- searched, to further enhance matching-performance **and** robustness. For the second subtask, Cascades (Viola **and** Jones, 2001), Efficient Subwindow Search (Lampert et al., 2008) **and** Selective Search (Sande et al., 2011) are key contributions to reduce the object search space by e.g. introducing a compressed image description or by finding a sub-search-space, which maximizes a given score function.

Mehr anzeigen
121 Mehr lesen

A ndreAs s chmitt & t homAs V ögtle , Karlsruhe
Keywords: Terrestrial laser scanner, **Point** **clouds**, **Automatic** surface extraction, **3D** **reconstruction**
Summary: Terrestrial laser scanning has become a standard method for a fast **and** accurate acquisition **of** **3D** objects. While data capture has attained a high level **of** development, the analysis **of** **point** **clouds** is still characterised by a remarkable amount **of** manual interaction. In this article an advanced generic approach for the extraction **of** surface prim- itives is presented. In a first step the **3D** measure- ment domain is subdivided into volume elements (voxels) **and** the centre **of** gravity **of** the interior la- ser points is calculated for each voxel as representa- tive geometric position. Normal vectors are deter- mined for each voxel by means **of** all possible com- binations **of** two vectors to the 26 neighbouring barycentres. If the local surrounding contains plane surface parts, a couple **of** these normal vectors have similar directions. These vectors will be aggregat- ed (mean direction) **and** the number **of** involved vectors (NOV) is stored. For a planar surrounding a clear majority will be obtained. A region growing algorithm extracts plane surfaces by merging adja- cent voxels if their main normal directions are sim- ilar (homogeneity criterion). If two majorities can be observed it is an edge **point**, for three main di- rections a corner **point** can be assumed. These topo- logical points will be stored as a base for the subse- quent **3D** modelling process. First experiences with synthetic **and** real world data **of** buildings have shown the suitability **of** this advanced approach **and** the robustness concerning noise, surface roughness **and** outliers. A disadvantage may be a certain general isation effect.

Mehr anzeigen
10 Mehr lesen

angles are inherently the most prominent features in the slope histogram, the first **and** last 10° are cut off, so that the range is limited to 10° to 80°. Within the histograms for aspect **and** slope, a polynomial function **of** degree 2 is fitted to the peaks. A delta **of** approximately 90° between two identified aspect angles indicates a good outcome, since most façades are built orthogonally. The maxima are found to be the correct orientation for façade **and** roof planes. The subsequent process **of** plane fitting is reduced to two dimensions, since only the position **of** the planes remains unknown. In case **of** aspect planes, namely façades it means, that lines are sweeping through the nadir view at the given aspect angles, trying to find significant clusters **of** line segments. In case **of** slope planes, namely roofs, the viewing angle is fixed to a horizontal view at the given aspect angles. Again lines are sweeping through the view at given slope angles, in order to find significant clusters **of** line segments. The fixation **of** those sweeping lines at clusters marks the position **of** planes at the previously found orientation angles (see last row **of** table 1).

Mehr anzeigen