Nach oben pdf From LIDAR Point Clouds to 3D Building Models

From LIDAR Point Clouds to 3D Building Models

From LIDAR Point Clouds to 3D Building Models

In this Chapter a new method is proposed for generating 3D building models on different levels of detail (LOD). The proposed work flow is presented in Figure 6.1. The 3D models on differ- ent LOD follow the standard definition of the City Geography Markup Language (CityGML) described in (Kolbe et al., 2005). CityGML defines five LOD for multi-scale modeling: LOD0 – Regional model consisting of the 2.5D Digital Terrain Model (DTM), LOD1 – Building block model without roof structures, LOD2 – Building model including roof structures, LOD3 – Building model including architectural details, LOD4 – Building model including the interior. Algorithms for producing the first three levels of the LOD are explained in this chapter. Accord- ing to the above categorization, the first LOD corresponds to the digital terrain model (DTM). The non-ground regions are filtered using geodesic reconstruction to produce the DTM from LIDAR DSM (Arefi and Hahn, 2005; Arefi et al., 2007b). The LOD1 consists of a 3D representation of buildings using prismatic models, i.e., the building roof is approximated by a horizontal plane. Two techniques are implemented for the approximation of the detected building outline: hierarchical fitting of Minimum Bounding Rectangles and RANSAC-based straight line fitting and merging (Arefi et al., 2007a). For the third level of detail (LOD2), a projection-based approach is proposed resulting in a building model with roof structures. The algorithm is fast, because 2D data are analyzed instead of 3D data, i.e., lines are extracted rather than planes. The algorithm begins with extracting the building ridge lines thought to represent building parts. According to the location and orientation of each ridge line one para- metric model is generated. The models of the building parts are merged to form the overall building model.
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Reconstructing 2-D/3-D Building Shapes From Spaceborne Tomographic SAR Point Clouds

Reconstructing 2-D/3-D Building Shapes From Spaceborne Tomographic SAR Point Clouds

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
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Comparison between two generic 3D building reconstruction algorithms: point cloud based vs. image processing based

Comparison between two generic 3D building reconstruction algorithms: point cloud based vs. image processing based

This paper compares two generic approaches for the reconstruction of buildings. Synthesized and real oblique and vertical aerial imagery is transformed on the one hand into a dense photogrammetric 3D point cloud and on the other hand into photogrammetric 2.5D surface models depicting a scene from different cardinal directions. One approach evaluates the 3D point cloud statistically in order to extract the hull of structures, while the other approach makes use of salient line segments in 2.5D surface models, so that the hull of 3D structures can be recovered. With orders of magnitudes more analyzed 3D points, the point cloud based approach is an order of magnitude more accurate for the synthetic dataset compared to the lower dimensioned, but therefor orders of magnitude faster, image processing based approach. For real world data the difference in accuracy between both approaches is not significant anymore. In both cases the reconstructed polyhedra supply information about their inherent semantic and can be used for subsequent and more differentiated semantic annotations through exploitation of texture information.
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Extracting Semantically Annotated 3D Building Models with
Textures from Oblique Aerial Imagery

Extracting Semantically Annotated 3D Building Models with Textures from Oblique Aerial Imagery

of two Gaussian functions to reduce the noise without affecting high-frequency details (Tomasi and Manduchi, 1998). Further- more, under the assumption that city buildings appear as ele- vated regions surrounded by lower ground, those DSM pixels that form the terrain are identified and removed by repeatedly moving windows of varying size over the DSM bitmaps as described in (Mayer, 2004). This process that basically creates a digital eleva- tion model also eliminates most of the vegetation that is likely to disturb any upcoming processing steps and provides an estimate for the ground height around the buildings as a by-product. After preprocessing the remaining DSM pixels are transformed into genuine 3D points and merged into a single point cloud. The resulting high-resolution samples are reduced to limit the mem- ory consumption and projected onto the XY plane subdivided by a regular grid. The spacing of the grid needs to be adjusted empirically with respect to the residual noise of the points. For each grid cell its density, spatial distribution and characteristic height histogram is computed and evaluated to identify potential fac¸ade pieces (Linkiewicz, 2012). On the set of cell elements C = {p i = (x i , y i )} that is considered a part of the fac¸ade the
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Reconstruction of building façades using spaceborne multiview TomoSAR point clouds

Reconstruction of building façades using spaceborne multiview TomoSAR point clouds

Once the façade model parameters are estimated, the final step is to describe the overall shape of the building footprint by further identifying adjacent façades pairs and determining the intersection of the façade surfaces. The adjacency of facades is usually described by an adjacency matrix that is built up via connectivity analysis. Identified adjacent façade segments are then used to determine the vertex points (i.e., façade intersection lines in 3D). They are found by computing the intersection points between any adjacent façade pair. Since polynomial models are used for façade parameter estimation, the problem of finding vertex points boils down to find the intersection point between the two polynomials corresponding to the two adjacent façades. The computed vertex points and the estimated model parameters are then used to finally reconstruct the 3D model of the buildings façades.
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Fast Initial Pose Estimation of Spacecraft from LiDAR Point Cloud Data

Fast Initial Pose Estimation of Spacecraft from LiDAR Point Cloud Data

The problem of estimating the pose of an object from a scan point cloud is equivalent with calculating the trans- formation between two point clouds representing the ob- ject from different perspectives. This task is referred to as range image registration. Usually, the term is associ- ated with constructing three-dimensional models of real objects based on range image data which give a partial view of the object from different viewpoints. Following the categorization of [SMFF07], fine and coarse registra- tion methods can be distinguished. The former calculate very accurate solutions based on initial transformations (e.g. ICP). The latter do not need any initial informa- tion and achieve a lower accuracy. The coarse type is exactly what is needed for pose initialization. Common methods include spin image, principal component anal- ysis, genetic algorithms and RANSAC-based DARCES [SMFF07]. However, at least without any adaptation, these methods are only of limited suitability in the con- text of spacecraft relative navigation.
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Determination of intensity-based stochastic models for terrestrial laser scanners utilising 3D-point clouds

Determination of intensity-based stochastic models for terrestrial laser scanners utilising 3D-point clouds

Figure 9 illustrates the results of the verification process. Therefore, 16 datasets from Wujanz et al. [18] were used featuring planar panels that were captured under random survey configurations and varying radiometric properties. The corresponding data was then processed in a least squares adjustment using three different stochastic models yielding in different weights for individual ranges and, consequently, different empirical standard deviations s 0 . The horizontal axis shows intensity values on a logarithmic scale. The vertical green lines highlight the smallest and largest intensity values that were captured in the experiments for the generation of the stochastic models. The vertical axis graphs the empirical standard deviations s 0 that stem from the plane adjustments. The first model was generated based on repetitive range measurements according to Wujanz et al. [18] while the corresponding results are depicted by blue circles. For the red circles the stochastic model generated in Section 2.1 was used, while the green circles stem from Section 2.2. All green and red circles fall into the specified interval of 0.7 < s 0 < 1.3. Hence, this outcome verifies the two suggested procedures for the generation of intensity-based stochastic models based on capturing 3D-point clouds instead of repeated range measurements. Note that the empirical standard deviations drift apart for higher intensities while a higher degree of conformity was apparent for the remaining parts.
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First Prismatic Building Model Reconstruction from TomoSAR Points Clouds

First Prismatic Building Model Reconstruction from TomoSAR Points Clouds

Typical data sources for reconstructing 3-D building models in- clude optical images (airborne or spaceborne), airborne LiDAR point clouds, terrestrial LiDAR point clouds and close range im- ages. In addition to them, recent advances in very high resolution synthetic aperture radar imaging together with its key attributes such as self-illumination and all-weather capability, have also at- tracted attention of many remote sensing analysts in character- izing urban objects such as buildings. However, SAR projects a 3-D scene onto two native coordinates i.e., “range” and “az- imuth”. In order to fully localize a point in 3-D, advanced in- terferometric SAR (InSAR) techniques are required that process stack(s) of complex-valued SAR images to retrieve the lost third dimension (i.e., the “elevation” coordinate). Among other InSAR methods, SAR tomography (TomoSAR) is the ultimate way of 3- D SAR imaging. By exploiting stack(s) of SAR images taken from slightly different positions, it builds up a synthetic aperture in elevation that enables the retrieval of precise 3-D position of dominant scatterers within one azimuth-range SAR image pixel. TomoSAR processing of very high resolution data of urban ar- eas provided by modern satellites (e.g., TerraSAR-X, TanDEM-X
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3D-Building Reconstruction with Different Height Levels
from Airborne LiDAR Data

3D-Building Reconstruction with Different Height Levels from Airborne LiDAR Data

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]).
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Reconstruction of Building Footprints using Spaceborne TOMOSAR Point Clouds

Reconstruction of Building Footprints using Spaceborne TOMOSAR Point Clouds

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.
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Direct geo-referencing of 3D point clouds with 3D positioning sensors

Direct geo-referencing of 3D point clouds with 3D positioning sensors

Station dependent effects remain, such as observation noise, hardware delays, multipath, or antenna PCCs. These effects have to be considered with appropriate correction models by the user. PCCs result from the fact that the actual phase measurements are related to the electrical phase centre of the receiver antenna while within the adjustment, measurements are modelled with respect to the geometrically well defined Antenna Reference Point (ARP). This difference is described by the PCC (Fig. 2.3). An example stereographic plot of absolute PCC for a standard rover antenna (Javad GrAnt G3T) is shown in Fig. 2.4. The PCCs are obtained by abso- lute robot calibration (named the Hannover concept (Wübbena et al., 2000)) at the Institut für Erdmessung (IfE), Leibniz Universität Hannover, Germany. Further determination approaches are calibration in an anechoic chamber (Zeimetz and Kuhlmann, 2008) and relative calibration with respect to a Nullantenna (Mader, 1999). While GNSS signals are right-hand circular polarised electro-magnetic waves (cf. Fig. 2.5) the relative orien- tation of receiver and satellite antennas has to be taken into account, as described in, e. g., Wu et al. (1993). The polarisation of the satellite signal is determined by the electric field vector E = [ E X E Y ] T which is
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Fast Probabilistic Fusion of 3D Point Clouds via Occupancy Grids for Scene Classification

Fast Probabilistic Fusion of 3D Point Clouds via Occupancy Grids for Scene Classification

ence. Many approaches have been reported over the last decades. Sophisticated classification algorithms, e.g., support vector ma- chines (SVM) and random forests (RF), data modeling methods, e.g., hierarchical models, and graphical models such as condi- tional random fields (CRF), are well studied. Overviews are given in (Schindler, 2012) and (Vosselman, 2013). (Guo et al., 2011) present an urban scene classification on airborne LiDAR and mul- tispectral imagery studying the relevance of different features of multi-source data. An RF classifier is employed for feature evalu- ation. (Niemeyer et al., 2013) proposes a contextual classification of airborne LiDAR point clouds. An RF classifier is integrated into a CRF model and multi-scale features are employed. Recent work includes (Schmidt et al., 2014), in which full wave- form LiDAR is used to classify a mixed area of land and water body. Again, a framework combining RF and CRF is employed for classification and feature analysis. (Hoberg et al., 2015) presents a multi-scale classification of satellite imagery based also on a CRF model and extends the latter to multi-temporal classification. Concerning the use of more detailed 3D geome- try, (Zhang et al., 2014) presents roof type classification based on aerial LiDAR point clouds.
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Regularized 3d modeling from noisy building reconstructions

Regularized 3d modeling from noisy building reconstructions

In our experiments, we show that our pipeline can create geometrically regularized 3D models from buildings which finally consist of smooth and clean surfaces instead of a noisy mesh, as in the input data. It can handle input meshes with different quality and input density, where especially on meshed point clouds including some errors (e.g., a point cloud created from SfM) our novel, line-based smoothness term improves the quality of the resulting 3D model com- pared to the approach presented in [ 6 ] (which will be de- noted as mesh-based smoothing method in this section). Fi- nally, we deliver textured output models, which can be vari- ably regularized and contain smooth surfaces where meshed point clouds, in contrast, contain a lot of noise and clutter.
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Geocoding Error Correction for InSAR Point Clouds

Geocoding Error Correction for InSAR Point Clouds

value is then determined at the location of the aforementioned point through spatial and temporal interpolation of the TEC maps [ 27 , 31 , 32 ]. Subsequently, the vertical TEC value at the point is projected onto the line of sight of the SAR satellite using a proper mapping function [ 31 ]. The path delay caused by troposphere is computed through the 4D integration of numerical weather data from European Centre for Medium-Range Weather Forecasts (ECMWF). The method extracts the dataset from a local database, converts it to a conventional geographic coordinate system, performs a 3D interpolation for defined integration points and eventually integrates the refractivity index along the integration path in the slant-range direction from the point of interest to the satellite [ 15 , 26 ]. The geodynamic effects are removed based on the state-of-the-art models according to the International Earth Rotation and Reference Systems Service (IERS) guidelines [ 33 ]. The effects are reported in the horizontal and the radial coordinate components, which are transformed to the radar timing coordinate system [ 9 ]. The coordinate transformation residuals between object and sensor coordinate systems are compensated for by taking into account the plate tectonics effect and referring the observation to a reference epoch [ 26 ]. Finally, the geometric calibration constants in range and azimuth are updated based on the most recent studies concerning long-term corner reflector experiments [ 34 ]. For this purpose, all the aforementioned effects are initially mitigated using the described methods and then the median of offsets between the expected position of the corner reflector, surveyed with GNSS, and its corresponding position in a time series of SAR images is considered as new re-calibration constants [ 34 ]. All of the mentioned corrections are computed for a coarse grid and are further interpolated for targets inside the grid [ 35 ]. The corrections are subtracted from the annotated SAR measurements to achieve absolute t az and τ rg . For more information regarding SAR imaging geodesy and its operational implementation, the reader is referred to [ 9 , 26 , 34 , 35 ].
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Façade structure reconstruction using spaceborne TomoSAR point clouds

Façade structure reconstruction using spaceborne TomoSAR point clouds

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.
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Facade Reconstruction Using Multiview Spaceborne TomoSAR Point Clouds

Facade Reconstruction Using Multiview Spaceborne TomoSAR Point Clouds

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.
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CHALLENGES IN FUSION OF HETEROGENEOUS POINT CLOUDS

CHALLENGES IN FUSION OF HETEROGENEOUS POINT CLOUDS

Different platforms and sensors are used to derive 3d models of urban scenes. 3d reconstruction from satellite and aerial images are used to derive sparse models mainly showing ground and roof surfaces of entire cities. In contrast to such sparse models, 3d reconstructions from UAV or ground images are much denser and show building facades and street furniture as traffic signs and garbage bins. Furthermore, point clouds may also get acquired with LiDAR sensors. Point clouds do not only differ in the viewpoints, but also in their scales and point densities. Consequently, the fusion of such heterogeneous point clouds is highly challenging. Regarding urban scenes, another challenge is the occurence of only a few parallel planes where it is difficult to find the correct rotation parameters. We discuss the limitations of the general fusion methodology based on an initial alignment step followed by a local coregistration using ICP and present strategies to overcome them.
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RoIFusion: 3D object detection from LiDAR and vision

RoIFusion: 3D object detection from LiDAR and vision

FIGURE 2. RoIFusion framework. As a whole, the proposed architecture contains three parts: (a) a Fused Keypoints Generation (FKG) layer which uses the raw point clouds and the images as input and aggregates the keypoints generated from both the point cloud set abstract (SA) network and the image segmentation network respectively. (b) The keypoints are then used to estimate the center points of the potential objects and the 3D Region of Interests (RoIs), which are projected to the image to obtain the 2D RoIs. (c) The 3D/2D RoI pooling layers are employed to capture the respective local features, which are finally fused together for 3D bounding box prediction. (d) The inputs of the SA module are the 3D points N 1 × 3 and the corresponding features N 1 × F 1 . We simultaneously downsample the points, extract the corresponding deep features in the orange block, and obtain the downsampled 3D points N 1 × 3 and corresponding features N 2 × F 2 . N 1, F1, N2, F2 are the dimension number of the input points/features, output points/features respectively. (e) Feature Propagation (FP) module: after applying a SA module, the number of the points is downsampled from N 1 to N 2 . Successively, the FP module takes layer1 with N 1 points and layer2 with N 2 points as input. After that, the number of points N 2 in the layer2 is firstly interpolated to N 1 , the output of which is then concatenated to layer1 to obtain the layer3 with the number of points N 1 .
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Grammar-guided reconstruction of semantic 3D building models from airborne LiDAR data using half-space modeling

Grammar-guided reconstruction of semantic 3D building models from airborne LiDAR data using half-space modeling

To overcome the huge memory consumption and the approximative accuracy of exhaustive enumeration, adaptive subdivision schemes were introduced. They replace the regular space partitioning by an adaptive space partitioning and make use of a lossless data compression scheme. The compression scheme is based on the observation that neighboring cubes tend to be part of the same class. Instead of keeping each cube separate, neighboring cubes of the same class are treated as a single subdivision of the space. This principle is for example also applied in run-length encoding, which belongs to the group of entropy encoding. A typical representative for this kind of encoding is the well-known Huffman coding (Huffman, 1952). Common representatives of adaptive space subdivision schemes are the octree representation (Jackins and Tanimoto, 1980; Meagher, 1982) and the quadtree representation (Finkel and Bentley, 1974) for the three-dimensional and two-dimensional space respectively. The basic idea of an octree is the recursive subdivision of the space along the coordinate axes into eight octants that are organized in a tree of degree eight. Thereby, the root of an octree represents the whole space and each child node represents an octant of the space defined by its parent node. Depending on the overlap with the solid to be modeled, only those octant nodes that are either entirely within or completely outside the solid compose the leaf nodes of the octree. Consequently, all internal nodes represent octants with a partial solid overlap. The whole solid results from the gluing of the space represented in the leaf nodes of the tree that are entirely within the solid. The properties of octree and quadtree representations are essentially similar to those of the exhaustive enumeration with the exception of reduced storage space. Several variants have been introduced such as Bintree (Tamminen, 1984; Samet and Tamminen, 1985), which recursively divides the space of partially overlapping nodes along a single axis into two equal-sized subspaces, and ATree (Bogdanovich and Samet, 1999), which enables to vary the number of subdivisions for all partial overlapping nodes in each level and allows for unequal-sized subspaces.
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Automated generation of 3D building models from dense point clouds & aerial photos

Automated generation of 3D building models from dense point clouds & aerial photos

errors caused by the incompleteness of data arising, for instance, from missed faces due to insufficient points, occlusion or vegetation clutter. It is said that having increased the point density of modern ALS, the data-driven approach allows to have a more accurate and robust result than that through the model-driven approach (Oude Elberink, 2008). A number of methods for doing the reconstruction, based on either model-driven or data-driven approaches using ALS data have been presented in the literature. When reviewing relevant literature, it is observed that the major problem is the efficient manipulation of the topology and roof primitives. From recent literature, it is seen that roof topology graphs (RTG) are widely used in both data-driven and model-driven approaches, especially for the efficient manipulation of topology and roof primitives (e.g. Verma et al., 2006; Milde et al., 2008; Oude Elberink and Vosselman, 2009), and in many cases, accurate results have been obtained. However, many unsolved problems need to be addressed within the processing chain of building reconstruction. This will be discussed in detail under Section 1.2.
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