Typical data sources for reconstructing 3-D building models in- clude optical images (airborne or spaceborne), airborne LiDAR pointclouds, terrestrial LiDAR pointclouds 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
A complete buildingmodelreconstruction needs data collected from both air and ground. The former often has sparse coverage on building façades, while the latter usually is unable to observe the building rooftops. Attempting to solve the missing data issues in buildingreconstructionfrom single data source, we describe an approach for complete buildingreconstruction that integrates airborne LiDAR data and ground smartphone imagery. First, by taking advantages of GPS and digital compass information embedded in the image metadata of smartphones, we are able to find airborne LiDAR pointclouds for the corresponding buildings in the images. In the next step, Structure-from-Motion and dense multi-view stereo algorithms are applied to generate buildingpoint cloud from multiple ground images. The third step extracts building outlines respectively from the LiDAR point cloud and the ground image point cloud. An automated correspondence between these two sets of building outlines allows us to achieve a precise registration and combination of the two pointclouds, which ultimately results in a complete and full resolution buildingmodel. The developed approach overcomes the problem of sparse points on building façades in airborne LiDAR and the deficiency of rooftops in ground images such that the merits of both datasets are utilized.
The first part of the algorithm is responsible for the detection of the building parts within the measured point cloud P ts. Here- with, the descriptive location and shape parameters of each build- ing part is determined. These parts build a set of nodes N odes. This identification step is the core of section 3.1. The results of the interpreted dataset from the first part are employed to incorpo- rate the observed building part in the model derived or predicted so far. A predicted tree is deduced based on a reasoning pro- cess as described in Loch-Dehbi and Pl¨umer (2015). If there is no tree available, a new model is constructed otherwise. In both cases, the model consists in a parse tree. It describes the taxon- omy and partonomy of a given fac¸ade reflecting semantics of each building part with respect to an a-priori learned weighted attribute
In recent years, oblique photography technology has been widely used in 3d building modeling. With the advantage of having more facade information, oblique airborne imagery makes 3D buildingmodel more complete and realistic. In addition, the Multi-View Stereo (MVS) technology currently can provide dense, accurate pointclouds and high quality 3D meshes. There are increasing photogrammetric softwares, such as Smart3DCapture by Acute3D and Photoscan by Agisoft, providing the automatic workflow to turn photos into high resolution 3D models. Although textured meshes have good visualization effect, it’s also necessary to simplify massive surface meshes of complex, large-scale scenes, and construct 3d semantic city models for advanced applications such as urban planning, indoor/outdoor pedestrian navigation, or cultural heritage.
The ground surface reconstruction module transforms a 3D point cloud previously labelled as ground ( illustrated in Figure 2 (c) ), into a continuous and scalable surface representation. The pro- posed framework is composed by several steps which are illus- trated in Figure 1 and described through the following sections. First, the 3D point cloud representing the ground is triangulated in the (x, y) plane using a constraint Delaunay algorithm which provides points connectivity. Then, we apply a mesh cleaning process to eliminate long triangles. In order to provide a continu- ous and regular surface model of the road, we apply the Sinc Win- dowed (Taubin et al., 1996) smoothing algorithm which elimi- nates high frequencies, while preserving sharp depth features and avoiding surface shrinkage. In a final step, a progressive dec- imator (Schroeder et al., 1992),(Hoppe, 1996) is applied to the smoothed mesh in order to cope with scalability constraints when performing surface reconstruction over large scale distances. It provides surface representation with low memory usage, enabling efficient data transmission and visualization. In addition, the dec- imation procedure enables progressive rendering in order to deal with real-time constraints imposed by driving simulation engines.
The (decimated) mesh models are commonly used to model buildings and city scenes. Frueh et al. (2005) generate textured facade meshes of cities. Hähnel et al. (2003) create compact models for indoor and outdoor environment from mobile laser scanning data. Marton et al. (2009) quickly create triangle models in real time for indoor scene frommodel laser scanning data. Nie et al. (2013) model large and scale scene from consumer depth camera. Wahl et al. (2008) first detect semantic shapes and use them as constraints in the simplification to derive city models from digital surface models (DSM). Zhou and Neumann use 2.5D dual constraints to recover vertical walls and create simplified roof models while reserving sharp features (Zhou and Neumann, 2010; Zhou and Neumann, 2011). Lafarge and Alliez (2013) create and simplify mesh models from the structured points which are created from the noisy point cloud and regular distributed points on the detected planes and ridges. Our new building modelling method is also data-driven as those methods. We do not recalculate the positions of the point cloud, but only derive the building structures based on the point segments. Therefore the proposed algorithm is much more computationally efficient. The node positions of model polygons are not computed based on their neighbouring points, but the plane equations of the roof segments, therefore are globally optimized.
Pointclouds are one of the new and upcoming means for rep- resenting volumetric media in immersive communications. They may be considered as a collection of points (x, y, z) in 3D space with attributes such as color, normals, transparency, specularity, etc. The pointclouds are said to be voxelized when points are constrained to lie in a regular 3D grid and assume integer coordinate values. The points within such grid are called voxels and may be occupied or not. Dynamic pointclouds depict a sequence of such clouds over time and, like video for images, may display movement. Each point cloud within this temporal sequence constitutes a frame.
field based on the VIIRS imager was developed and evalu- ated with respect to MODIS in this study. The tripled mi- crophysical resolution with respect to MODIS allows obtain- ing new insights for cloud–aerosol interactions, especially at the smallest cloud scales, because the VIIRS imager can resolve the small convective elements that are sub-pixel for MODIS cloud products. Examples are given for new insights into ship tracks in marine stratocumulus, pollution tracks frompoint and diffused sources in stratocumulus and cu- mulus clouds over land, deep tropical convection in pristine air mass over ocean and land, tropical clouds that develop in smoke from forest fires and in heavy pollution haze over densely populated regions in southeastern Asia, and for pyro- cumulonimbus clouds.
Abstract—Object grasping is a task that humans do without major concerns. This results from self learning and by observing of other skilled humans doing such task with previous informa- tion. However, grasping novel objects in unknown positions for a robot is a complex task which encounters many problems, such as sub-optimal performance rates and the time consumption. In this paper we present a method that complements the state-of-the-art grasping algorithms with two segmentation steps, the first one which removes the largest planar surface in the point cloud of the world before the grasp detector receives them and the second one that complements this segmentation with another segmentation that calculates where the object is located and segments the point cloud by executing a crop around the object. The proposed method significantly improves the grasping success rate (100% improvement over the baseline approach) and simultaneously is able to reduce the time consumption by 23%.
Shapovalov et al. (2013) classified pointclouds of indoor scenes, building a graphical model on point cloud segments. They con- sider long range dependencies by so-called structural links, also based on spatial directions such as the vertical, the direction to the sensor or the direction to the nearest wall. In an indoor sce- nario, walls can be detected using heuristics (Shapovalov et al., 2013). However, such approaches do not carry over to airborne data easily. CRF were also used by Lim and Suter (2007) for the point-wise classification of terrestrial LiDAR data. The authors coped with the computational complexity by adaptive point re- duction. In further work they first segmented the points and then classified the resulting superpixels. The authors also considered both a local and a regional neighbourhood. Introducing multiple scales into a CRF represented by long range links between super- pixels improved the classification accuracy by 5 % to 10 % (Lim and Suter, 2009). This result shows the importance of consider- ing larger regions instead of only a very local neighbourhood of each 3D point for a correct classification. An alternative to long range edges, which might lead to a huge computational burden if points are to be classified individually, is the computation of multi-scale features, enabling a better classification of points with locally similar features. Although belonging to different objects, the variation of the regional neighbourhood can support the dis- crimination between the object types, and hence lead to a correct labelling.
The OBIA approach for 3D land cover classification consists of three steps. The first step is multi-resolution segmentation based on intensity layers of multispectral pointclouds. Image objects are created via multi-resolution segmentation integrating large scale parameter for vegetation segmentation. To road and building, image objects are segmented with small scale parameter from the non-vegetation objects. To bare soil and lawn, image objects are segmented with large scale parameter from unclassified objects. Because different features have different boundary characteristic, we select different scale parameters to separate imagery objects. The second step is image objects classification of multispectral pointclouds. The objects classes typically discerned 3D land cover classification in study area have nine different classes from bottom to up: water bodies, bare soil, lawn, road, building, low vegetation, medium vegetation, high vegetation and power line. The objects classes are attributed with a high-dimensional feature space, such as object vegetation index feature (pseudo NDVI, ratio of green), point cloud-related attribute (intensity, elevation, number of points, return number, class), and object statistics feature (brightness, area). Using these feature indexes, point cloud objects can be classified and linked to a hierarchical network, and a rule set of knowledge decision tree is created for classification. The last step is to assess the validity of classification results. Accuracy assessment is performed by comparing randomly distributed sampled points in reference imagery from Google map with the classification results.
the earthquake, banning the construction inside the limits established, under the penalty of demolishing those illegal structures (§1). This had the purpose of preventing the same fast construction done before the earthquake, giving time for the engineers to delineate a new plan for the city. Secondly, and a continuous mention in the document, was that all the buildings must respect the elevated views drawn by Casa do Risco, with very restrictive permissions for additional features applied on the façade walls (§8). Moreover, the building ’s maximum height was limited considering the top of Terreiro do Paço buildings the reference that could not be exceeded (§14). Thus, the original two story buildings idea was left aside, as previously said, being decreed that the number maximum of stories was limited by the total height of the buildings. In other words, the commercial areas at the ground floor would have a clearance height of 16 spans and, the remaining height would be equally divided. In practice, this resulted in a four floors buildings typology above ground as can be perceived in Figure 3.4. Also in this point, the type of windows was stipulated accordingly to the façade orientation and the floor. The first floor of all buildings facing the principal streets should have balcony windows, and the ones facing the secondary should have “regular windows” as well as the remaining ones of the other floors and façades. This law also stated the instructions for the streets and sewage construction (§15). The principal streets width was stipulated at 60 spans following the instructions given by Manuel da Maia in his dissertation. The main sewage duct – cloaca - should also be constructed beneath the major streets with 10 spans width and 14 spans of height. The landlords would also be responsible for the construction and maintenance of the streets (as well as the sewage duct) that confines with their buildings (§16). The secondary streets were also referred, stipulating their width in 40 spans, 20 for vehicles and 10 in each side for pedestrians (§18). Note that, in the secondary streets a sewage duct would not be constructed.
Kinematic SLAM adds it’s own corrections both to pose fixing and the incorporation of ground truth data. While the underly- ing algorithm for the pre-processing step is the same as for static SLAM, eventually the whole dataset is split up in smallest pos- sible slices where each slice has it’s own modifiable pose as de- scribed in Elseberg et al. (2013). More often than not it will be the case that it is not possible to identify three ground truth positions in a single slice to perform unambiguous data association. Experiments have been carried out on a tunnel dataset that con- tains 3D point cloud data taken using a LIDAR system consisting of two 2D laser scanners arranged in the typical ”X” formation and positioned on top of a car. Apart frompoint and trajectory information, the dataset comes with ground truth information. Ground truth points have been acquired using independent sur- veying methods. The ground truth coordinates point to a bolt head in the centre of a painted circle on the tunnel’s wall. Due to paint having different reflectance than the surrounding wall, the painted circle is an identifiable feature in the dataset. Due to separate pointclouds being acquired by separate scanners and in separate runs, it is possible to compare datasets that should yield the same result and identify any discrepancies. Such comparison has been done and can be examined in Figure 4.
Megastriae.—Parabolae, flares and secondary flares are in accordance with the definition of megastriae proposed by Bucher and Guex (1990), i.e., radial linear elements as- sociated with a discontinuity in shell growth comprising the outer prismatic (opl 1 × opl 2) and the nacreous (ncl 1 × ncl 2) layer (compare Bucher et al. 1996; Bucher 1997). It is questionable whether this strict definition can be always used for radial linear elements associated with an interrup- tion in shell growth that are normally assigned to mega- striae. For example, Drushits and Doguzhaeva (1981) and Sprey (2002) show flares and (juvenile) parabolae that are only formed by the opl 1. Strictly speaking, these sculptural elements cannot be taken as megastriae according to Bucher and Guex (1990). However, the observations of Sprey (2002) may indicate a structural change of parabolae during on- togeny. Parabolae of juveniles are composed of the opl 1 while parabolae of adults are composed of the opl 1 and the ncl 1. This is likely since Sprey’s (2002) and our observa- tions were made in closely related genera: Binatisphinctes and Choffatia. In our opinion, the differences in structure do not necessarily imply a different morphogenesis, i.e., the withdrawal of the mantle edge. Instead, they indicate earlier activity of additional shell-secreting mantle tissue prior to retraction, i.e., formation of the ncl 1. Variations in structure may just indicate a different timing of formation (prolonged or short periods of shell precipitation). Although structurally very similar, the primary and secondary flares represent a difference in time of formation, too, as indicated by their different scale. However, both would be handled equally as megastriae according to Bucher and Guex (1990). In our opinion the strict definition of megastriae excludes a number of related, radial linear sculptures or does not consider morphological or temporal differences. We recom-
There is no native support for pointclouds in Spark but there are libraries and systems that add support for raster and vector data. SpatialSpark (You et al., 2015) implements spatial join and spa- tial query for vector data in Spark and may use an R-tree index to speed up point-in-polygon processing. Magellan 3 is another library for geospatial analytics of vector data using Spark and aims to support geometrical queries and operations efficiently. Geotrellis 4 is a complete system, not just a library, for geographic data processing based on Spark. It is initially intended for raster data but some support for vector data is also available. It can also employ Z-order and Hilbert curve indices.
Russ et al.  algorithm applies a Haussdorff distance in range images, the experiment used a data set composed of 200 subjects and 398 images in total. The reported recognition rate was of 98% not presenting any false positive occurrences (erroneously classifying an input). A drawback mentioned in the research is the execution time needed due to the algorithm’s high computational cost. Lee et al. extracted geometrical features (curvature, length, angle) from geometrically local- ized fiducial landmarks. A testing scenario was composed using two sensors for image capturing, the first sensor consist of an structure light sensor (Genex 3D FaceCam) obtaining test images and the second sensor is a full laser scan (Cyberware) to obtain model images (since laser scan provide high detail and shape quality of scanned surfaces). Two classification architectures are presented, the first uses curvature values extracted from landmarks as correspondence and the second applies a SVM into a feature vector. The first classification scenario uses a 20 subjects data set claiming a recognition rate of 95%, while the SVM-based scenario uses a data set composed by 100 subjects claiming a rank-1 recognition rate of 96%.
GapFill by adjusting the existing metabolic network through reversing the directionality of existing reactions; adding transport reactions between compartments; adding exchange fluxes; or by adding a minimum number of reactions from a reference database . MetaFlux is part of the Pathway Tools software for generating FBA models. It uses a multiple gap filling approach based on a mixed integer linear programing (MILP) to suggest reactions to be added from the MetaCyc database, identify biomass metabolites which are required but can not be produced, and choose nutrient and secretion fluxes to be added to the modelfrom a ‘‘try set’’ defined by the user. Model SEED is a web-based resource for the creation of new metabolic models. After a preliminary reconstructionmodel is created in Model SEED, an auto-completion step is performed by using an MILP algorithm that identifies the minimal set of reactions from the SEED reaction database that must be added to fill the gaps present in the network. However, all these approaches are dependent upon an existing reference database of information to resolve these curation issues. Here a new algorithm for facilitating curation of models is presented. The approach integrates a genetic algorithm (GA) with flux balance analysis. The novelty and strength of this GA/Flux Balance Analysis (GAFBA) strategy lies in its ability to both aid in fundamental studies of metabolism and to facilitate curation of genome-scale metabolic networks, rather than functioning solely as a predictive tool. Furthermore, the strategy is independent of a reference database, allowing the researcher to investigate other avenues of curation. However, this approach does not preclude the use of existing databases as one of those sources of information. Rather it provides increased flexibility in evaluating the system of interest. As the quality of curation increases, the evolved model can be used as a predictive tool, but that is a secondary contribution of this approach.
In this paper, we first present a novel hierarchical clustering algorithm named Pairwise Linkage (P-Linkage), which can be used for clustering any dimensional data, and then effectively apply it on 3D unstructured point cloud segmentation. The P-Linkage clustering algorithm first calculates a feature value for each data point, for example, the density for 2D data points and the flatness for 3D pointclouds. Then for each data point a pairwise linkage is created between itself and its closest neighboring point with a greater feature value than its own. The initial clusters can further be discovered by searching along the linkages in a simple way. After that, a cluster merging procedure is applied to obtain the finally refined clustering result, which can be designed for specialized applications. Based on the P-Linkage clustering, we develop an efficient segmentation algorithm for 3D unstructured pointclouds, in which the flatness of the estimated surface of a 3D point is used as its feature value. For each initial cluster a slice is created, then a novel and robust slice merging method is proposed to get the final segmentation result. The proposed P-Linkage clustering and 3D point cloud segmentation algorithms require only one input parameter in advance. Experimental results on different dimensional synthetic data from 2D to 4D sufficiently demonstrate the efficiency and robustness of the proposed P-Linkage clustering algorithm and a large amount of experimental results on the Vehicle-Mounted, Aerial and Stationary Laser Scanner pointclouds illustrate the robustness and efficiency of our proposed 3D point cloud segmentation algorithm.