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\title{Attribute Assignment to Point Cloud Data and its Usage}
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             \author[1]{Yoichi  Fujita}

             \author[2]{Yuji  Hoshino}

             \author[3]{Seigo  Ogata}

             \author[4]{Ichiro  Kobayashi}

             \affil[1]{  Kumamoto University}

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\date{\small \em Received: 8 December 2014 Accepted: 5 January 2015 Published: 15 January 2015}

\maketitle


\begin{abstract}
        


In recent years, with the development of laser measurement technology, utilization of point cloud data is progressing. However, since point cloud data does not contain attribute information, the usability of the data is low. It is possible to consider that by assigning attributes to the nonattribute point cloud data, this can lead to the usage of point cloud data in each phase of life cycle of construction: design, construction, and maintenance. Therefore, in this paper, the authors have proposed an attribute assignment method for point cloud data. In addition, the authors proposed the way to use attributed point cloud data, the usage as objects, data linkage, and visualization by using the attribute assignment method. Point cloud data of a dam was used as a case study for the proposed method and the usage.

\end{abstract}


\keywords{point cloud data, attribute assignment, usage of point cloud data, visualization, data linkage.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
ith the development of Global Positioning System (GPS) and 3D laser scanners, fixed laser survey equipment and MMS (Mobile Mapping System) have been devised and are currently deployed for actual data acquisition. However, the acquired data is used only as point data (e.g. as management information) and as the conversion to 2D drawing. In recent years, the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) has announced the implementation of CIM (Civil Information Management/Modeling)-which is a civil version of BIM (Building Information Management)-in loads of major projects around Japan while establishing the field of architectural design. CIM has applied the concept of sharing, using, and developing 3D models in each phase of the life cycle of construction-planning, design, construction, and maintenance. CIM is primarily aimed at improving the construction production process. The use of 3D data in the design phase is expected to advance rapidly.\par
Thus far, the authors have conducted numerous research studies regarding the use of point cloud data. The proposals include the utilization of point cloud data in road space using editing method and attribute assignment to point cloud data. This development allows for a number of possibilities in using the point cloud data at each stage of the life cycle of construction solely. Other researchers also proposed the use of point cloud data. For instance, Mizoguchi et al. has proposed evaluation method for scaling of bridge piers using point cloud data. What's more, Yun, H et al. has proposed a method for pavement maintenance using the data derived from MMS. Therefore, it can be presumed that the utilization of point cloud data is smoothly progressing.\par
In the present paper, the authors have proposed an attribute assignment method for point cloud data in order to expand the range of point cloud data utilization. Additionally, the authors have shown how to use the attributed point cloud data. By assigning attributes to point cloud data, it is possible to use point cloud data as objects, data linkage, and visualization. There are hence various possibilities in using point cloud data, not limited to road space only. In chapter 2, the authors described the significance of assigning attributes to point cloud data. In chapter 3, the authors illustrated the attribute assignment method and the usage of attributed point cloud data. In chapter 4, the author measured the usage of attributed point cloud data using point cloud data of dam as case study. The results are subsequently discussed. 
\section[{II.}]{II.} 
\section[{Attributes of Point Cloud a) The significance of attribute assignment}]{Attributes of Point Cloud a) The significance of attribute assignment}\par
When point cloud data is given attributes, its use has become more intensive. For example, consultation and examination of a project becomes possible by giving attributes to an unnecessary place in the reconstruction site, removing the data from point cloud data, and adding new structures. Furthermore, by classifying each structure existing in the area surrounding the roads, point cloud data are then deployed in relocation and removal plans. The attributed point cloud data containing additional informationvolume, damage, pictures, documents -can be used in the management of the existing structure. Specifically, it is possible to acquire the data of the existing structure using laser scanner equipment. Since the point cloud data of the structures are directly available, the data is deemed practical in existent structure management. Such use leads to reduction in labor and costs of CAD 3D modeling. 
\section[{i. Position information}]{i. Position information}\par
By setting the values for each of the position information (x, y, z), it is possible to extract the planes along the three axes. For example, gradient tints diagram can be created by coloring the elevation values as seen in figure  {\ref 1}. For this data, it is possible to extract the data that has the same elevation with z value by setting the threshold value of z value. The shape analysis of the structure for the relative position between the dots can be performed. On top, extraction is possible by visually specifying the range and the location.\par
ii. Reflection intensity When the threshold value to the reflection intensity of the measured data is set, structures can be classified depending on types of materials. Figure \hyperref[fig_1]{2} is an example, where the outer wall of a building and the white line inside of point cloud data are extracted. A portion of road surface can also be extracted as seen in figure \hyperref[fig_1]{2 b}). 
\section[{iii. Color information}]{iii. Color information}\par
Classification of colors can be made by setting the threshold value and combining the three elements (r, g, b) of color information from the measured data. Figure  {\ref 3} exemplifies red point extraction from point cloud data. Figure  {\ref 3 b}) shows how the objects containing red color are extracted. Furthermore, When RGB color information is converted into HSV color, it is possible to identify the artifact and structures. 
\section[{c) The partition of point cloud data}]{c) The partition of point cloud data}\par
As described in the previous section, the partition and classification of the point cloud data can be performed by analyzing the attributes of the point data. Additionally, the editor allowing the partition of point cloud data has been developed in the previous studies. With this editor, it becomes possible to split point cloud data into arbitrary shapes. Further, the development of editing software for point cloud data has been encouraged. Such progress leads to the possibility of structure partition and classification based on point cloud data. When the attributes are assigned to the partitioned point cloud data, the utilization of point cloud data is widely increased. In this paper, the authors showed the method in assigning the attributes to the partitioned and classified point cloud data. 
\section[{III.}]{III.} 
\section[{Attribute Assignment a) Method}]{Attribute Assignment a) Method}\par
In this section, the authors portrayed the attribute assignment method to the partitioned point cloud data. ?? represents the whole point cloud data, and ?? ?? represents each point of the point cloud data.\par
The formula of the whole point cloud data is as follows:?? = \{?? ?? , ?? ?? , ? , ?? ?? \} (?? = 1,2, ? , ??)\par
The formula for each point ?? ?? is as follows: b) The attributes of point cloud data In the analysis of numerical information of the point data, it becomes possible to classify and determine the point cloud data as a "group". The authors described the analysis of typical information of point data as shown below:  Figure \hyperref[fig_2]{4} shows the partition of point cloud data. ?? ?? is a portion of point cloud data. ?? ?? represents each point of point cloud data. The formula for the partition is as follows:?? ?? = [ ??, ??, ?? , ??, ð??"ð??" , ??] t ?? (?? = 1,2, ?\textbf{,}?? ?? = \{?? ?? , ?? ?? , ? , ?? ?? \} (?? = 1,2, ? , ??) (?? = 1,2, ? , ??)\par
The formula for ?? ?? is shown as follows:?? ?? = [ ??, ??, ?? , ??, ð??"ð??" , ??] t\par
?? (?? = 1,2, ? , ??) Therefore, the whole point cloud data ?? becomes as follows:?? = \{?? ?? , ?? ?? , ? , ?? ?? \} (?? = 1,2, ? , ??)\par
Next, the ID of ?? ?? is ?? ?? , and the formula is as follows:?? ?? = ?? (?? = 1,2, ? , ??)\par
As a result, the formula of ?? ?? is shown using ID(?? ?? ) and each point of ?? ?? as follows:?? ?? = [?? ?? , ?? ?? ] t ???? (?? = 1,2, ? , ??) (?? = 1,2, ? , ??)\par
the formula is as follows:ð??"ð??" ?? = [??, ??, ??, ? ] t ?? (?? = 1,2, ? , ??)\par
The formula for ?? ?? which is the assigned attribute information containing ID(?? ?? )and parameter of attribute information ð??"ð??" ?? is as follows:?? ?? = [?? ?? , ð??"ð??" ?? ] t ?? (?? = 1,2, ? , ??)\par
The attribute information?? ?? is assigned to the partition of point cloud data ?? ?? as shown in figure  {\ref 5}.\par
The attributed point cloud data is set as ??? ?? . By using the partitioned point cloud data ?? ?? and the attribute information????, the attribute assignment equation is represented as follows:??? ?? = ? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ? ? ? ?? ?? ?? ?? ? ? (?? = 1,2, ? , ??)\textbf{(1)}\par
Thus, it is possible to assign attributes to the partition ?? ?? of point cloud data.  
\section[{a. Movement}]{a. Movement}\par
By moving the partitioned part of individual locations to arbitrary locations, it is possible to check position relationship between the partitioned data and the interference. For example, it is possible to move only the sign as shown in figure  {\ref 6 a}). 
\section[{b. Hidden and removal}]{b. Hidden and removal}\par
It is possible to validate the changes in the current state when an object disappears because only the partitioned part is removed or hidden. As seen in figure  {\ref 6 b}), it is possible to confirm the situation due to the removal of the sign only. The structure can be hidden and moved along the time axis. This makes it possible to check the behavior of a structure over the time. For instance, in the case of a removal project, it is possible to show the progress of the removal process of the structure. Also, it is possible to check a series of behaviors such as loading of a removed structure to the trailer as shown in figure \hyperref[fig_6]{7}. 
\section[{b. Confirmation of surrounding environment}]{b. Confirmation of surrounding environment}\par
Point cloud data can reproduce the targeted site with high accuracy. Due to this fact, it is possible to determine the environment of the site during the consultation and examination of the model space. In construction, when a structure is removed, its immediate environment will accordingly change. For example, there will be additional locations such as the location for heavy equipment and construction location. It is possible to confirm the location of interference-street lights and utility poles-with CAD-created new structures by giving attributes to existent structures in the surrounding when checking the construction process. Particularly, it is possible to check the interference between the removal part and the trees by giving attributes to the surrounding as shown in figure \hyperref[fig_7]{8}.\par
iii. Deand visualization a. Data linkage Photos, pdf files, and documents such as Excel can be linked to point cloud data as attribute information. For example, figure \hyperref[fig_8]{9}      
\section[{Case Study}]{Case Study}\par
The point cloud data of a dam derived from fixed laser survey equipment is used as the case study in this paper. The number of point is approximately 50 million. In addition, it is the first dam removal project in Japan. Therefore, the authors applied the proposed method with the aim to perceive the usage of attributed point cloud data as objects, data linkage, and visualization through this case study. 
\section[{Global Journal of C omp uter S cience and T echnology}]{Global Journal of C omp uter S cience and T echnology}\par
Volume XV Issue II Version I Year ( ) 
\section[{2015}]{2015}\par
B the sign and is visualized as illustrated in figure \hyperref[fig_9]{11}. Figure \hyperref[fig_1]{12} depicts color classification based on assigned numerical information of the status of the structure. It is therefore possible to visually grasp the state of the structure solely based on point cloud data. Indeed, it is possible to create simulation from assigned attribute information. For example, by simulating the conditions of bad pavement areas and heavy traffic areas, calculation of repairing priority can be performed. First of all, using numerical value base maps and point cloud data of the dam in figure  {\ref 13}, the authors performed the reproduction and determination of the current state and attribute assignment to the body of the dam. 
\section[{a) Attribute assignment}]{a) Attribute assignment}\par
Figure \hyperref[fig_2]{14} a) shows the partition of measured point cloud data of the dam body according to its removal parts. Figure \hyperref[fig_2]{14 b}) shows the attribute information in Excel, and by using equation (1), the attribute information is assigned to the dam body as shown in figure \hyperref[fig_2]{14 c}). Figure \hyperref[fig_2]{14 c}) illustrates the attribute assignment of the dam body according to removal plans in which each part has different attributes. For that reason, different colors are given to the parts according to the assigned attributes. The time required in editing the point cloud data in this current case study took about 2 hours, and the time required in assigning attributes to the point cloud data took about 5 minutes. By contrast, it took 2 days creating CAD objects. Therefore, it was possible to edit the point cloud data in a very short amount of time. 
\section[{b) Used as objects}]{b) Used as objects}\par
By using the attributed point cloud data of the removal parts in figure \hyperref[fig_2]{14 c}), it is possible to check the results of the dam removal plan as shown in figure  {\ref 15}. Figure  {\ref 15 a}) shows the planning drawing paper of the removal plan. Figure  {\ref 15 b}) show the confirmation of removal plan based on point cloud data. As shown in figure  {\ref 15}, confirmation of the removal plan on the drawing paper can be obtained in the same way as confirmation on point cloud data. It tends to be impossible to check the process from the drawing paper; however, point cloud data makes it possible to determine the changes in moving the removal parts and the confirmation of removal process from arbitrary directions. 
\section[{d) Visualization of the attribute information}]{d) Visualization of the attribute information}\par
The result of changing the display date of the assigned attribute information is shown in figure \hyperref[fig_6]{17}. The parts are colored according to the removal schedule of every year and are visualized in order to visually determine the assigned attribute information. With such visualization in figure \hyperref[fig_6]{17}, it is possible to determine the sequential order and the position in removing the parts at a glance. 
\section[{e) Discussion}]{e) Discussion}\par
In this case study, the authors showed the attribute assignment to the body of the dam and the usage of attributed point cloud data. The case study shows three possibilities: 1) usage of point cloud data as objects, 2) confirmation and display of assigned attribute information on point cloud data, and 3) the usage of simulating and determining changes in attribute information visually. With these three possibilities, the utilization of point cloud data has This progress thus leads to the use of point cloud data in managing structures in maintenance phase. Such advancement can be also considered as labor reserves due to the sole use of point cloud data without the need to create CAD objects. In this case study, attribute assignment is not limited to the body of dam only, but to its immediate environment such as typography, trees, and etc. Since point cloud data can reproduce the current state of the structure while containing the position information, it can lead to the usage in threedimensional Geographic Information System (GIS) by giving attributes to the point cloud data. There is hence a possibility in using point cloud data in managing more advanced structures and features. Unsolved difficulties in assigning attributes still remain, though. In reassigning attributes and re-edit point cloud data, repartition and re-classification is required, resulting in labor problems.\par
V. 
\section[{Conclusion}]{Conclusion}\par
In this paper, the authors proposed an attribute assignment method to point cloud data and described the usage of attributed point cloud data. In the case study, attributes were assigned to the removal parts of point cloud data of the dam, and the proposed usage was verified. In chapter 2, the authors described the attributes of point cloud data and the significance of attribute assignment. In chapter 3, the authors proposed a method in assigning attributes to point cloud data and described possibilities for usage of attributed point cloud data. In chapter 4, the measured point cloud data of the dam was partitioned into arbitrary shapes. Deploying the attribute assignment method proposed in chapter 3, the authors were able to assign the parts with different attributes. Finally, the authors examined the possibilities of visualization and display of information and the usage of point cloud data as objects using the attributed point cloud data.\par
For future prospects, the authors plan to reply to the problems mentioned in the discussion section.\par
VI.\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
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\noindent\textbf{2}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Fig. 2 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Fig. 4 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-5.png}
\caption{\label{fig_3}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{56}\includegraphics[]{image-6.png}
\caption{\label{fig_4}Fig. 5 . 6 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-7.png}
\caption{\label{fig_5}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7}\includegraphics[]{image-8.png}
\caption{\label{fig_6}Fig. 7 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{8}\includegraphics[]{image-9.png}
\caption{\label{fig_7}Fig. 8 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{9}\includegraphics[]{image-10.png}
\caption{\label{fig_8}Fig. 9 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{11}\includegraphics[]{image-11.png}
\caption{\label{fig_9}Fig. 11 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-12.png}
\caption{\label{fig_10}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-13.png}
\caption{\label{figure13}}\end{figure}
 			\footnote{© 2015 Global Journals Inc. (US)} 		 		\backmatter   			 
\subsection[{Acknowledgement}]{Acknowledgement}\par
The authors would like to express their gratitude to ITOCHU Techno-Solutions Corporation, Informatics Autodesk Co., Ltd, and Kumamoto Prefecture Enterprise Bureau for providing precious data to this study. 			  			 			 			  				\begin{bibitemlist}{1}
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