A Hybrid Spatial Index for Massive Point Cloud Data Management and Visualization

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1 bs_bs_banner Research Article Transactions in GIS, 2014, 18(S1): A Hybrid Spatial Index for Massive Point Cloud Data Management and Visualization Jiansi Yang* and Xianfeng Huang *School of Urban Design, Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University Abstract With the wide use of laser scanning technology, point cloud data collected from airborne sensors and terrestrial sensors are often integrated to depict a complete scenario from the top and ground views, even though points from different platforms and sensors have quite different densities. These massive point clouds with various structures create many problems for both data management and visualization. In this article, a hybrid spatial index method is proposed and implemented to manage and visualize integrated point cloud data from airborne and terrestrial scanners. This hybrid spatial index structure combines an extended quad-tree model at the global level to manage large area airborne sensor data, with a 3-D R-tree to organize high density local area terrestrial point clouds. These massive point clouds from different platforms have diverse densities, but this hybrid spatial index system has the capability to organize the data adaptively and query efficiently, satisfying the requirements for fast visualization. Experiments using point cloud data collected from the Dunhuang area were conducted to evaluate the efficiency of our proposed method. 1 Introduction Laser scanning technologies have been widely used in diverse fields and applications, such as surveying and mapping, electric power system programming, transportation planning, and forestry, surveying, and city planning, etc. (Huang 2013; Kim and Sohn 2013; Vosselman 2012). The effective organization and management of point cloud data, however, are a major obstacle for data processing and distribution. Given the ever increasing production of massive airborne and terrestrial laser scanning (TLS) point clouds, there are some limitations to the present methods for massive data visualization, querying, and updating, especially when the mixed point clouds are collected with extreme density differences from airborne and ground platforms. The currently existing commercial software for point cloud data processing was mostly developed by hardware manufacturers (Dassot et al. 2011; Krelling et al. 2012), for processing their own data structures and files with relatively powerful analysis functions (Cai and Miklavcic 2011; Pfeifer et al. 2013; Bi et al. 2014). These data operations, though, are strictly limited by the internal memory and file levels and are unable to visualize large point clouds. Recent research work shows that successful management and visualization of billions of points is attainable with octrees, but the test data are limited to terrestrial laser scanning data, rather than point clouds of mixed types (Elseberg et al. 2013). A review of the literature reveals that at present there are few software products with the capability to manage and visualize massive airborne and terrestrial laser scanning point cloud data. Address for correspondence: Xianfeng Huang, State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Luoyu Road No. 129, China. huangxf@whu.edu.cn Acknowledgements: This work is supported by the National Science Foundation of China (Grant No ) John Wiley & Sons Ltd doi: /tgis.12094

2 98 J Yang and X Huang Spatial indexing is the core technique for organization and management of massive laser scanning data. Because of its simplicity in operation and high efficiency in coding and decoding, the quad-tree model, with its pyramid image structure, is widely used in various types of data indexing and management. The quad-tree is frequently used to encode a spatial block in a pyramidal spatial structure for Geographic Information Systems (GIS) to index vector feature data (Lindstrom and Pascucci 2001; Kothuri et al. 2002). But the mechanism of the quad-tree index designed for organizing a GIS feature database is not suitable for organizing point cloud data. In the vector data organization, features are assigned to a corresponding level of a quadtree by their size and position. On the other hand, when dealing with point clouds, a quad-tree will largely degrade into a grid index. In order to process and index points, some variations of quad-tree were designed; the point quad-tree is one which splits the space at the position of each single point, but this method still has limitations. Concurrently the density of an irregular point cloud is not evenly distributed; needing a high quality spatial index to satisfy the high speed data retrieval requirements for visualization. In addition, the size of data varies at each node; some nodes may have a large amount of data, leading to low efficiency during data display and analysis absent appropriate indexes. The storage and management of laser scanning data has been an active research focus, especially regarding spatial indexing and data organization (Shi and Qi 2012). The common spatial indexes used for point clouds are the grid, quad-tree, octree, KD-tree, and R-tree indexes (Ma and Wang 2011; Gong et al. 2012; Han et al. 2012; Schön et al. 2012). But current studies show that these methods still face the following problems: 1. Many of the large point cloud organization and management systems are built on a single file. Although some of them are based on databases, they still have difficulties in organizing hybrid point cloud data collected from multiple data sources, particularly hybrid point cloud data from airborne and terrestrial laser scanners. 2. The capabilities for massive point cloud data management are insufficient. The current data organization and management methods have their limitations stemming from the large data volume and point number. 3. The spatial distribution characteristics of the point cloud data are not fully considered in spatial index designs. For different applications, the geometric structure of a point cloud will represent the shape of the corresponding natural objects. Diverse types of spatial structure must be fully considered in a spatial index design, so as to satisfy the different requirements for improved spatial index query efficiency. Addressing these problems, a hybrid spatial index method is proposed based on an extended quad-tree and 3D R-tree structure. The extended quad-tree contains a traditional quad-tree, which builds a hierarchical structure to manage large area airborne point clouds, and an R-Tree at the node of the quad-tree to organize and manage the local area airborne point cloud and dense terrestrial point cloud data. The article is organized as follows. In Section 2, the basic principles and implementation of quad-trees and local R-trees to build the spatial index for point cloud of mixed types are introduced. Section 3 presents the experiments used to evaluate our method, in terms of efficiency and visualization effects. Finally, conclusions are drawn in Section 4. 2 Hybrid Index Combining Quad-trees and Local R-trees for Massive Point Clouds We propose a hybrid structure for indexing large point clouds. The original point cloud data are dealt with using two kinds of strategies according to the level of visualized detail. One

3 Massive Point Cloud Management and Visualization 99 Pyramid quad-tree structure (a) (b) F A C G H R-tree of the red square B H (c) D E D H G G G A B D C F D (d) Figure 1 Schematic plot for hybrid spatial index based on a quad-tree and a local R-tree indexes point clouds by interpolating elevation from the point cloud data to a regular grid in way similar to a DEM. Another keeps the data structure of the original point cloud, but assigns the points to a regular grid. One grid therefore can contain more than a thousand points, but they are interpolated into an elevation value of the grid. Two methods are used to index the two kinds of data generated from a point cloud. The quad-tree is used to index the grid DEM on an upper layer, the local R-Tree is employed to index the point cloud data in a grid, which contains the point cloud data inside the bottom square of a leaf node. Figure 1 shows the hierarchical structure of the hybrid spatial index. The upper regions are represented by grid cell values of the quad-tree. The point cloud data in leaf nodes is indexed by R-trees. In this way, the spatial index can deal with point clouds for diverse densities and volume. In the visualization procedure, the data in the nodes on the upper level are first loaded according to the visible frustum, and then the lower level nodes of the quad-tree, and finally the points inside the R-tree. 2.1 Pyramid Quad-tree to Index the Grid DEM for Retrieving the Low Resolution Data The point clouds from airborne and terrestrial laser scanners share similar characteristics in many respects, but have quite different geometric shapes and densities, which influence the final data organization. We put airborne and terrestrial point cloud data into the same data management system, but their differences cannot be neglected. The point cloud of airborne sensor is deemed as 2.5 dimensional data, but the data collected by terrestrial laser systems are truly three dimensional points. Indeed, during the data import procedure, we treat them according to their sensor types, which means that the user must tell the system what sensor type the point cloud data comes from.

4 100 J Yang and X Huang At first, the space of the point cloud data is divided into a regular grid according to the point density and relevant rules, e.g. 2 4 m depending on the density of terrestrial point clouds. The grids are on the bottom layer of the DEM pyramid, playing the role of leaf nodes in the quad-tree to contain airborne data, but the root node of the R-tree which stores the terrestrial point cloud is also in this grid. All the points are stored into grids on the bottom layer. An elevation value for each grid is interpolated from inside the grid. The purpose of data interpolation is to generate a hierarchical structure that we can visualize from the top down without loading all the point data. In the data interpolation procedure, three factors are considered: (1) the elevation of each point; (2) the weight of each point; and (3) the intensity. Adding these factors, including the intensity, to interpolation helps to match the main elevation that most points represent. The classic inverse distance weighting (IDW) method was used to interpolate the points and intensity, respectively. Actually, the big differences in point densities between airborne and terrestrial point clouds do not change the two kinds of point cloud too much during data integration. To satisfy large area visualization and data organization needs, two key attributes are defined in a grid. One interpolates values from point cloud data to represent the average elevation in the grid. The other is a pointer to a corresponding local R-tree of the grid. Since the densities of the point cloud are highly variable, the number of points in a grid cell varies with the point density differences. In special cases, some cells have little or even no points, while others may have hundreds of thousands of points (see Figure 2). A pyramid based on quad-tree is generated to express the levels of detail 1,2,..., n, with level 1 being the least-detailed leaf nodes, which is the bottom grids. Then, a re-sampling process is applied to the upper layer to construct the pyramid. All the points in the four grids of the lower layer are re-sampled into a grid in the upper layer. In this way, a pyramid point cloud structure is generated. The upper nodes are different from the bottom leaf nodes of the quad-tree. The former only contains the stored elevation values. A visualization app will only read pyramid nodes at the appropriate level of detail, but never the original point cloud when the visual resolution is quite low. When we zoom into the point cloud to reach high resolution visualization, the original points are loaded by the local R-Tree index. 2.2 Local 3-D R-Tree to Index the Original Point Cloud for Retrieving the High Resolution Data The discussion in Section 2.1 explains how the point cloud is split into the nodes of the quadtree. The data block in the leaf node of the quad-tree is then organized using a local R-tree or KD-tree. Thus, the specific number of data blocks can be determined by the allocated space and time complexity for applications, which reduces the depth of the tree, making the R-tree processing speed faster. Since the space is divided into a number of blocks, reducing the amount of data in each local tree index, data updating after insertion or deletion will only be executed in the corresponding local tree index. Point cloud data collected from airborne sensors measuring the surface points from above can be deemed 2.5 dimensional data; thus, the simple tree structure index methods such as quardtree and KD-tree, are a workable solution. But, for a large point cloud collected from a TLS system, a KD-tree will have too many layers and consequently not be suitable for efficient management or high speed visualization. We introduce a 3D R-tree to manage and index large 3D terrestrial point clouds. Although a point cloud from a TLS system is discrete and irregular, it can still accurately reflect the detailed surface structure of measured objects. Thus, the

5 Massive Point Cloud Management and Visualization 101 Figure 2 Schematic plot showing data block based on grid index and encoding re-sampling method for the generation of a hierarchical data structure must differ from an airborne point cloud. In consideration of the fact that a terrestrial point cloud is dense enough to be deemed a surface, we can fit the surface into small facets within the error range and then simplify the data to build a hierarchical structure based on these small facets. This concept of facet with a given size is somewhat similar to the splat used in Bettio et al. (2009), it greatly improves the rendering quality during the data loading period. Continuing with this facet concept, a stratagem for 3D R-tree construction therefore includes building a 3D R-tree index based on the minimum fitted plane by using the dynamic balance programming method and creating a LoD model by fusing the neighboring facets with similar directions. First, the point cloud data is divided into blocks, as discussed in the previous section. In the interim the surfaces are divided into small facets based on plane fitting and are assigned thresholds. After that, a 3D spatial R-tree index is built by using the small facets. Finally, a LoD model is constructed based on the shape of the grouped facets inside a boundary box. Bottom-up processing is implemented to construct the 3D R-tree. We continually split the fitted plane using points in a grid, until the grid cannot be split again. At this juncture the fitting process is not a real plane fitting algorithm. We must find a plane and a point that can

6 102 J Yang and X Huang Node ID Node A ribute Number of sub nodes Pointer to sub nodes Envelop of this node Main direc on of this facet (a) The middle node information in a local 3D R-tree table Node ID Pointer to the point cloud data Node A ribute Number of points Envelop of this node Main direc on of this node Point No. X Y Z Other a r. (b) The leaf node information in a local 3D R-tree table Figure 3 The detailed information in the local 3D R-tree table: (a) leaf node; (b) middle node. The leaf node of the 3D R-tree point to the point cloud data represent the main direction of the point group but fitting a plane using a big number of points is time-consuming and unstable. We introduce a voting algorithm (Oude and Vosselman 2011) to estimate the plane parameters of points in an R-tree node; the direction of each point in a point cloud is estimated by fitting to a tiny plane using itself and the neighboring points within a threshold such as twice the average point distance. Next, the node s direction and center point are estimated by a voting algorithm. All the points in this node vote. Then, they determine the main direction by comparing the number of points in each direction. The barycenter of those points which have a direction similar to the final main direction will be estimated and used as the center point. This fitting method saves computing time. Meanwhile, the envelope and normalized normal vector of the plane are computed. The residuals of all the points for the fitted plane are computed. If the residual is less than a given threshold, it is a leaf node of the local 3D R-tree. The plane can approximately represent a block. If the residuals are larger than the given threshold, the block is divided further into smaller blocks until the fitting error is less than the given threshold. At this time, the blocks are considered middle nodes in the local 3D R-tree. All envelopes and directions of the plane of the nodes are computed and stored in the node table in a local 3D R-tree (Figure 3). A local R-tree in a grid of a quad-tree is generated by using top-bottom disassembling processing. Using these procedures, a hierarchical data structure cannot only meet the data summarizing requirement, but also presents good aggregation properties. In our 3D R-tree index structure, some improvements to the traditional R-tree were considered: 1. Each grid (leaf node) of the quad-tree has a local 3D R-tree; the leaf node of the quad-tree is the root node of the local R-tree. 2. Besides the bounding boxes for the data block at each node, the normal vectors for bestfitting facets are also stored in the node. When loading a point data block from a large number of nodes, the direction of the normal vector is added in the data query operation. 3. A node attribute is recorded as a column in the node table in the local 3D R-tree, which contains middle and leaf nodes. If the attribute is a middle node, its pointers point to the sub-nodes of the node table of the local R-tree. If the attribute is a leaf node, its pointers will point to the points of the original point cloud table.

7 Massive Point Cloud Management and Visualization 103 There are two kinds of tables. One is the 3D R-tree table and other one is the original point cloud table. The relationship is established by the pointers of the leaf nodes of a R-tree table pointing to the points of the original point cloud table (see Figure 3b). To improve the query efficiency, a small group of points is stored in one binary block in the 3D R-tree file. Since each point of point cloud has the same storage size, defined by a structure, we use an array structure to store a group of points by a connected space. In our implementation, we conduct searches directly on the file system to speed up the data storage and query operation. Each R-tree of the final leaf node of the quad-tree is stored in a single file. For data from a large area, many files are needed since each R-tree needs a file to store the data, slowing down the efficiency of file system considerably when there are too many files in a folder. To solve this problem, we encode the files by names and assign files to sub-folders according to the quardtree node ID. In the R-tree file, the beginning part of the file stores the index information and position of each data block, so we can quickly locate the point cloud data of a node in a R-tree. 3 Experiments 3.1 Data Management and Visualization The experimental data, consisting of airborne and terrestrial point cloud data, was collected in the Dunhuang district, including Dunhuang city, as well as the surrounding desert and the Mogao Grottoes areas. The entire area is located at about 40 N, 94 E. The data was acquired in two flight trips using the ALS60 II scanner. The height above the ground was about 600 m The point cloud collected is more than 20 GB with an average point space of 0.4 m, covering 280 km 2 in the Dunhuang Mogao Grottoes area. The TLS data taken in the surrounding Mogao Grottoes contains more than one billion points with an average point distance of 0.02 m. The computer we used in our experiments is a DELL workstation, which has a Win7 64 bit system with an Intel i5 3.2 GHz CPU, 16 GB memory and a 1 TB hard disk. The traditional management software for laser scanning data was not adequate for such a large amount of data. It takes two to three minutes to load and display all the data using traditional methods. However, using the hybrid indexing method incorporating the quad-tree and R-tree proposed in this article, such a large data set can be loaded at one time and shown smoothly in detail as a general view in two to three seconds. As shown in Figure 4, the right side shows images from the MAP WORLD system, an online map visualization and data distribution system similar to Google Earth. The left side shows the corresponding point cloud data. Delving further into the land surface details, the terrestrial laser scanning data was loaded and displayed, and a 3D R-tree index was built. We first divided the terrestrial point cloud data into blocks, using the data dividing method based on facet estimation. The result is shown in Figure 5. After this processing, the data sets that can fit into a plane are shown as a plane bounding box, otherwise the data blocks are shown as points. Block results using a facet bounding box are shown in Figure 6. A six layer R-tree pyramid structure is constructed with the basic data from the facet bounding box shown in Figure 6 using a 3D R-tree pyramid building method, as shown in Figure 7. It takes only one second for data used in the previous example to be shown in a general view using our method, while taking more than 10 seconds using other methods. The 3D

8 104 J Yang and X Huang Figure 4 A global view of the point cloud data for the Dunhuang area. The data in (b) at the Mogao Grottoes contains about one billion points R-tree index efficiently displays data block details in several milliseconds. Using the 3D R-tree pyramid structure of the point cloud, a R-tree index dispatches and displays point clouds, significantly improving efficiency. Considering that the point cloud is often used with registered imagery, we add the color information to each point as additional information. In comparison with the standard LAS point, in our method the additional color information uses three bytes to store the RGB value of each point, because this benefits the visualization results. In the data resampling period, the

9 Massive Point Cloud Management and Visualization 105 Figure 5 Block result based on point number in voxel and plane estimation Figure 6 Block result using facets bounding box method color of upper level is assigned by the weighted color information from its closest neighboring points. This creates a hierarchical colored point cloud. After loading the data, to improve the visualization quality and speed up the rendering procedure, some drawing skills must be applied, such as the display list in OpenGL. In our experiment, we first created a display list using the upper layer point cloud. The other data was rendered in idle time without user intervention. Meanwhile, the display list was created. Constructing the time of display list will cost time, but only during idle time, and therefore does not influence the user operation. Figure 8 shows the result of our visualization of point cloud data consisting of 120 million points for a wooden building named Nine Layer Pagoda and the surrounding cliff facade point cloud for the Mogao Grottoes. 3.2 Efficiency Test To evaluate data storage and query efficiency, we conducted an experiment and recorded the time consumption in data storage and query operations respectively, using the test data from

10 106 J Yang and X Huang (a) The 5th layer node (root node) (b) The 4th layer node (c) The 3th layer node (d) The 2nd layer node (e) The 1st layer node (f) The 0th layer node (data node) Figure 7 General view of the 3D R-tree pyramid structure Figure 8 Visualization of mixed point cloud, colored by height Dunhuang. In addition, we added a point cloud from a mobile laser scanning system to our experiments to evaluate the efficiency and adaptability of this method. The mobile laser scanning data was collected with the RIEGL WMX-250 system, which can collect time-stamped images and dense point clouds with a measurement rate of up to 600,000 Hz and 200 scan lines per second. The point cloud, located in a metropolitan area in east south Asia, has

11 Massive Point Cloud Management and Visualization 107 Table 1 Store and query efficiency of three types of point cloud compared with Oracle SDO_PC Point cloud type Avg. point dist (m) Store time(s) Query time oracle (s) Store time our (s) Query time our (s) Airborne Terrestrial Mobile altogether 640 million points, with a density of about 0.04 m. For comparison purposes, we also conducted and experimentally tested the efficiency of storage and querying using an Oracle database. The most recent version of the Oracle database provides a point cloud module, named SDO_PC, to manage massive point clouds. Using the interface function provided by the Oracle system, we can import or query a point cloud into the database. The database server and client are both installed on the same computer. The computer we used in this experiment is the same as in previous experiments. Table 1 shows the time used for data storing and querying. From the time consumption for data importation and querying, we can see that our method took much less time for both storing and query procedures. This might be because we applied a file read-write operation to store and query, while the Oracle database is built on a very complex mechanism to guarantee system stability. As seen in the table, the Oracle database spends almost the same amount of time on all the three different data types, while our method spends less time for airborne data storage. From our analysis of the literature, the Oracle point cloud database likely employs only a R-tree to organize massive point clouds. This will limit efficiency in multiple source point cloud organization. But the Oracle database is a very stable commercial system and supports multiple-user writing, querying operations, and even a roll-back transaction operation. 4 Conclusions A novel method of managing and organizing massive airborne and terrestrial point cloud data, to cope with the requirements for management and fast query of massive point clouds collected from multiple platforms, was introduced and discussed. A hybrid spatial index combining quad-trees and 3D R-trees was proposed and implemented. A quad-tree is used as an external index to build a pyramid structure for large scenarios. The 3D R-tree is used to construct local indexes to accelerate terrestrial point cloud data dispatch and visualization efficiency. Experimental results demonstrate the speed, effectiveness, and efficiency of the proposed hybrid index. As with all other data organization methods, there are advantages and disadvantages with our method. One disadvantage of our method is that the data storage is implemented using the Windows file system, creating some problems in future concurrent queries when two users visit the same file at the same time. In addition, the plane parameter estimation of each node in R-tree is sensitive to noisy data, leading to norm vector errors in forest areas and places with much vegetation, which might influence the R-tree structure. Nonetheless, the need for organizing a massive point cloud is increasing. In the surveying and mapping industry, point clouds are deemed to be intermediate products. They are

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