JP7360111B2 - Point cloud data management system - Google Patents

Description

The present invention relates to a system for managing point cloud data obtained by measuring terrestrial features.

BACKGROUND ART Laser surveying devices are mounted on unmanned aerial vehicles (UAVs), automobiles, and the like to measure the shape of the ground surface and terrestrial features. For example, in an MMS (mobile mapping system) using a car, a GPS receiver, a video camera, an IMU, and a laser scanner are mounted on the ceiling of the car, and measurements are taken while the car is driving on a road. The video camera may be either analog or digital.

Through such measurements, it is possible to obtain three-dimensional point cloud data indicating the ground surface and the contours of features. Based on the position obtained by GPS and the scanning direction and distance of the laser scanner, each three-dimensional point group data is assigned a position in absolute coordinates (plane rectangular coordinates, latitude, longitude, altitude, etc.). Further, the ground surface and terrestrial features are imaged by a video camera to generate video data (still image data). This moving image data is recorded in association with a constantly changing imaging position.

Obtaining 3D point cloud data allows us to know the current state of the ground surface and features, which can be useful for maintenance, map creation, etc.

The inventors have proposed that attribute data, including data indicating the area in absolute coordinates, a name, etc., be assigned to features in three-dimensional point cloud data.

However, although the above attribute data has been standardized and the data accumulation has become more abundant, on the other hand, when using the attribute data for maintenance etc., it is difficult to efficiently manage 3D point cloud data. system is desired.

An object of the present invention is to solve the above problems and provide a system that can efficiently manage three-dimensional point group data.

The independently applicable features of the invention are listed below.

(1)(2)(3) A point cloud data management system according to the present invention includes first and second point cloud data server devices, an attribute data server device, and a plurality of terminal devices that can communicate with these server devices. A point cloud data management system comprising: the first point cloud server device records a first three-dimensional point cloud data file from a first direction in absolute coordinates including features; The group server device records a second three-dimensional point cloud data file from a second direction using absolute coordinates that includes the same feature as the feature, and the attribute data server device records the outer shape of the feature. The terminal device obtains the external shape area data of the feature from the attribute data server device, and based on the external shape area data, the first Obtain first three-dimensional point cloud data of the feature corresponding to the first three-dimensional point cloud data file from the point cloud server device, and obtain first three-dimensional point cloud data of the feature corresponding to the second three-dimensional point cloud data file from the second point cloud server device. a three-dimensional point cloud data acquisition means for acquiring second three-dimensional point cloud data of the feature; and a three-dimensional point cloud data acquisition means for obtaining second three-dimensional point cloud data of the feature; and three-dimensional model construction means for constructing a three-dimensional shape.

Therefore, it is possible to easily search and acquire three-dimensional point cloud data about the same feature from a plurality of different directions, and to construct a more complete three-dimensional model.

(4)(5)(6) The point cloud data management system according to the present invention includes first and second point cloud data server devices, an attribute data server device, and a plurality of terminal devices that can communicate with these server devices. In the point cloud data management system, the first point cloud server device records a first three-dimensional point cloud data file at a first date and time in absolute coordinates including features, and the second point cloud The server device records a second three-dimensional point cloud data file at a second date and time based on absolute coordinates that includes the same feature as the feature, and the attribute data server device records a second three-dimensional point cloud data file at a second date and time that includes a feature that is the same as the feature, and the attribute data server device records , records the outer shape area data indicated by absolute coordinates, and the terminal device acquires the outer shape region data of the feature from the attribute data server device, and based on the outer shape region data, the first point group Obtain the first three-dimensional point cloud data of the feature corresponding to the first three-dimensional point cloud data file from the server device, and obtain the first three-dimensional point cloud data of the feature corresponding to the second three-dimensional point cloud data file from the second point cloud server device. A three-dimensional point cloud data acquisition means for acquiring second three-dimensional point cloud data of an object, and a temporal change of the object based on the first and second three-dimensional point cloud data acquired at the first and second dates and times. and a means for acquiring changes over time.

Therefore, it is possible to easily obtain three-dimensional point cloud data for the same feature with different measurement dates and times, and to obtain the secular change of the feature.

(7) The point cloud data management system according to the present invention is characterized in that the first point cloud server device and the second point cloud server device are constructed as one point cloud server device.

(8) The point cloud data management system according to the present invention is characterized in that the attribute data server device is a server device constructed integrally with the point cloud server device.

(9) The point cloud data management method according to the present invention records a first three-dimensional point cloud data file from a first direction in absolute coordinates including a feature, A second three-dimensional point cloud data file from a second direction is recorded, and data on an external area surrounding the external shape of the feature indicated by absolute coordinates is recorded. Obtain the outer shape area data, and based on the outer shape area data, obtain the first 3D point cloud data of the feature corresponding to the first 3D point cloud data file, and save it to the second 3D point cloud data file. The second three-dimensional point cloud data of the corresponding feature is acquired, and the three-dimensional point cloud is synthesized by a computer based on the acquired first and second three-dimensional point cloud data from the first and second directions. It is characterized by obtaining data.

Therefore, it is possible to easily search and acquire three-dimensional point cloud data regarding the same feature from a plurality of different directions, and obtain more accurate three-dimensional point cloud data.

(10) The three-dimensional point cloud data management method according to the present invention records a first three-dimensional point cloud data file at a first date and time using absolute coordinates including features, and A second three-dimensional point cloud data file at a second date and time using coordinates is recorded, and external area data surrounding the external shape of the feature indicated by absolute coordinates is recorded. Obtain the outer shape area data, and based on the outer shape area data, obtain the first 3D point cloud data of the feature included in the first 3D point cloud data file, and add it to the second 3D point cloud data file. The second three-dimensional point cloud data of the included feature is acquired, and the computer calculates the change over time of the feature based on the first and second three-dimensional point cloud data obtained at the first and second dates and times. It is characterized by obtaining.

Therefore, it is possible to easily search and obtain three-dimensional point cloud data regarding the same feature at different dates and times, and to obtain changes in the feature over time.

(11)(12)(13)(14)(15) A feature search system according to the present invention includes a point cloud data server device, an attribute data server device, and a plurality of terminal devices that can communicate with these server devices. a feature search system, wherein the server device measures three-dimensional point cloud data of a plurality of features while moving and captures video data attached with imaging position information of the features; and video data attached with imaging position information of the features. a recording unit for recording attribute data including external shape area data surrounding the external shape of the plurality of features; attribute data transmitting means for transmitting the attribute data of the plurality of features to a terminal device for feature selection by a user; a feature position acquisition unit that receives feature selection information and acquires a feature position corresponding to external shape area data included in the attribute data of the selected feature; video data extraction means for extracting a portion with imaging position information attached and transmitting the extracted portion to a terminal device; A feature attribute data display means for displaying on the display section, a selection information transmitting means for transmitting feature selection information by the user to the server device, and a display section for receiving the extracted video data transmitted from the server device. and a video data reproducing means for reproducing the video data.

Therefore, it is possible to easily search for and display the features imaged in the video.

(16) In the feature search system according to the present invention, the outer shape area data indicates the position in absolute coordinates, and the video data extraction means extracts an imaged position near the feature location from a plurality of video data. It is characterized by extracting the portion with information attached and transmitting a plurality of extracted video data.

Therefore, with one piece of external area data, it is possible to search for features in a plurality of moving image data.

(17) In the feature search system according to the present invention, the video data extraction means acquires three-dimensional point cloud data of the selected feature, and extracts a video based on at least the outline of the three-dimensional point cloud data. The present invention is characterized in that it includes means for specifying a feature in a video that identifies and marks a corresponding feature in the video.

Therefore, it becomes easy to find a feature in a video.

(18) In the feature search system according to the present invention, the server device is configured with a plurality of individual server devices, and the video data or the attribute data are recorded in different individual server devices. It is a feature.

Therefore, it is possible to search for features from video data scattered across different individual server devices.

(19) The feature search system according to the present invention includes video data attached with imaging position information obtained by capturing images of features when three-dimensional point cloud data of a plurality of features is measured while moving; Attribute data including outer shape area data surrounding the outer shape is recorded, the computer displays the attribute data of the plurality of features for the user to select the feature, and upon receiving the selection, the computer selects the feature. The computer obtains the feature position corresponding to the external shape area data included in the attribute data of the feature, and the computer extracts from the video data a portion with imaging position information in the vicinity of the feature location. It is characterized by displaying.

Therefore, it is possible to easily search for and display the features imaged in the video.

(20)(21) The outline area data generation device according to the present invention includes a planar outline determining means for determining a planar outline based on a plan view of a feature having absolute coordinates, and a three-dimensional point cloud data of the feature. and a three-dimensional outer shape determining means that uses a three-dimensional outer shape when the planar outer shape is continued from the lower end to the upper end as outer shape area data.

Therefore, it is possible to easily and accurately generate the external shape area data of the feature.

In the embodiment, the "three-dimensional point group data acquisition means" corresponds to step S13.

In the embodiment, the "three-dimensional model construction means" corresponds to step S15.

In the embodiment, the "temporal change acquisition means" corresponds to step S18.

In the embodiment, the "attribute data transmitting means" corresponds to step S51.

In the embodiment, the "terrestrial feature position acquisition means" corresponds to step S52.

In the embodiment, the "video data extraction means" corresponds to step S54.

In the embodiment, the "terrestrial feature attribute data display means" corresponds to step S12.

In the embodiment, the "selection information transmitting means" corresponds to step S19.

In the embodiment, the "video data reproducing means" corresponds to step S20. -
The term "program" is a concept that includes not only programs that can be directly executed by a CPU, but also programs in source format, compressed programs, encrypted programs, and the like.

FIG. 1 is a functional block diagram of a point cloud data management system according to a first embodiment of the present invention. This is the system configuration of a point cloud data management system. This is the hardware configuration of the terminal device. 3 is a flowchart of a terminal program. This is an example of three-dimensional point cloud data. It is a figure which shows the process for detecting a ground point. This is an example of design data for features. It is a diagram showing a state in which three-dimensional point cloud data is superimposed on a plan view of a feature. This is an example of three-dimensional point cloud data of a feature. This is an example of an item of attribute data. FIG. 3 is a diagram for explaining a process of estimating a feature name based on three-dimensional point cloud data of the feature. It is a flowchart of acquiring three-dimensional point cloud data of a feature and generating a three-dimensional model. It is a flowchart of acquiring three-dimensional point cloud data of a feature and generating a three-dimensional model. This is an example of displaying the external shape of a feature. FIG. 15A is the first three-dimensional point group data of the feature viewed from the first direction, and FIG. 15B is the second three-dimensional point group data of the feature viewed from the second direction. This is synthesized three-dimensional point cloud data. FIG. 17A shows the generated three-dimensional model. FIG. 17B is a display example of the measurement direction. FIG. 18A shows first three-dimensional point group data, FIG. 18B shows second three-dimensional point group data, and FIG. 18C shows composite three-dimensional point group data. FIG. 2 is a functional block diagram of a point cloud data management system according to a second embodiment. It is a flowchart for acquiring a change over time. It is a flowchart for acquiring a change over time. This is an example of displaying changes in features over time. This is an example of a screen that requests selection of three-dimensional point cloud data with different dates and times. It is a functional block diagram of a feature search system according to a third embodiment. This is the system configuration of the feature search system. It is a flowchart of feature search. FIG. 3 is a diagram showing the structure of video data. FIG. 3 is a diagram showing an imaging area. It is a flowchart of video extraction. 3 is a diagram showing the relationship between an imaging trajectory 200 and a terrestrial feature 106 at each time. FIG. FIG. 3 is a diagram showing an image in which a mark is attached to a search target feature.

1. First embodiment
1.1 Overall Configuration FIG. 1 shows the overall configuration of a point cloud data management system according to an embodiment of the present invention. The first point cloud server device 40a records a first three-dimensional point cloud data file from a first direction using absolute coordinates including terrestrial objects. The second point cloud server device 40b records a second three-dimensional point cloud data file from a second direction using absolute coordinates including terrestrial objects. The attribute data server device 60 records external area data based on absolute coordinates surrounding the external shape of a feature.

Here, features include, for example, road design standard information (distance markers, road center lines, measurement points, etc.), road surface features (roadway sections, sidewalk sections, trackbeds, etc.), and those that share an area with the road surface. Features (stop lines, lane markings, crosswalks, road markings, etc.), features other than the road surface (signposts, lighting poles, traffic lights, pedestrian bridges, etc.), features that indicate the road surface (retaining walls, bridges, road markings, etc.) surface, tunnel, shed, etc.). Note that this is not limited to road features.

The terminal device 20 can communicate with these server devices. The three-dimensional point cloud data acquisition means 22 of the terminal device 20 acquires external area data of a desired feature from the attribute data server device 60. The three-dimensional point cloud data acquisition means 22 accesses the first point cloud server device 40a based on the acquired external shape area data of the feature, and converts the three-dimensional point cloud data included in the area into a first three-dimensional point cloud. Obtain as data. Further, the three-dimensional point cloud data acquisition means 22 accesses the first point cloud server device 40b based on the acquired external shape area data of the feature, and transfers the three-dimensional point cloud data included in the area to the second three-dimensional point cloud data. Acquire as point cloud data.

The three-dimensional model construction means 24 constructs a three-dimensional shape of the feature based on the first three-dimensional point group data and second three-dimensional point group data acquired above. In this way, by taking advantage of the fact that the external area data has absolute coordinates, it is possible to collect three-dimensional point cloud data from different directions of the target feature, which is recorded dispersedly on the Internet. be able to. Therefore, a more complete three-dimensional model of the feature can be formed.

1.2 System Configuration FIG. 2 shows the system configuration of a point cloud data management system according to one embodiment. Point cloud server devices 40a, 40b, . . . 40n are provided on the Internet. An attribute data server device 60 is also provided. These server devices 40a, 40b...40n, 60 are provided with terminal devices 20a, 20b...20n so as to be able to communicate via the Internet.

The hardware configuration of the terminal device 20 is shown in FIG. A memory 82 , a display 84 , a hard disk 86 , a DVD-ROM drive 88 , a keyboard/mouse 90 , and a communication circuit 92 are connected to a CPU (or a GPU or the like; the same applies hereinafter) 80 . The communication circuit 92 is for connecting to the Internet.

An operating system 94 and a terminal program 96 are recorded on the hard disk 86. The terminal program 96 cooperates with the operating system 94 to perform its functions. These programs were recorded on the DVD-ROM 98 and installed on the hard disk 86 via the DVD-ROM drive 88.

The point cloud server device 40 and the attribute data server device 60 also have similar hardware configurations. However, in the point cloud server device 40, instead of the terminal program 96, a point cloud server program is recorded. Furthermore, in the attribute data server device 60, instead of the terminal program 96, an attribute data server program is recorded.

1.3 Public processing of point cloud data Surveying companies generate 3D point cloud data and videos of roads and features using MMS installed in cars and the like. The generated three-dimensional point group data is recorded, for example, on a recording medium such as a DVD-ROM, and is imported into the hard disk 86 of the terminal device 20.

The surveying company starts the terminal program 96 and generates attribute data based on the recorded three-dimensional point cloud data. FIG. 4 shows a flowchart of attribute data generation processing in the terminal program 96.

The CPU 80 of the terminal device 20a first reads out the recorded three-dimensional point group data (step S1). An example of three-dimensional point cloud data is shown in FIG. In this embodiment, the three-dimensional point group data is measured including the color of the object to be measured and the reflection intensity of the laser. Additionally, information on the measurement method and measuring equipment is recorded in the three-dimensional point cloud data.

The CPU 80 groups points within a predetermined distance from each other as a cluster in this three-dimensional point group data. This results in the formation of clusters of various sizes. Then, the CPU 80 removes clusters having a predetermined volume, a predetermined number of points or less, a predetermined point group density or less, as noise (step S2).

Subsequently, the CPU 80 extracts a ground point (ground surface) using a cloth simulation method or the like (step S2). In ground point extraction, the CPU 80 first inverts the elevation value of the three-dimensional point group data. For example, if there is three-dimensional point group data of a cross section as shown in FIG. 6A (the point group is represented as a line in the figure), inverted three-dimensional point group data as shown in FIG. 6B can be obtained.

Next, the CPU 80 performs a simulation on the inverted three-dimensional point group data as if a cloth was applied from above. In FIG. 6C, the simulated cloth is shown in dashed lines. Subsequently, the CPU 80 extracts the three-dimensional point group data in contact with the simulated cloth as ground points. Next, the CPU 80 inverts the elevation value again to obtain a ground point as shown in FIG. 6D.

The ground points extracted in this way are generally accurate, but as shown in FIG. 6D, they may include some features in the vicinity 100 where the features exist. Therefore, the normal direction of the line formed by each extracted ground point is calculated, and portions where the normal line is at a predetermined angle or more (for example, 30 degrees or more) with respect to the vertical direction are excluded from the ground points. Thereby, a ground point as shown in FIG. 6E can be obtained.

Note that in this embodiment, Cloth Simulation is used to extract the ground points, but the ground points may be extracted by other methods such as the lowest point extraction method.

After extracting the ground points as described above, the CPU 80 removes the ground points from the three-dimensional point group data. As a result, three-dimensional point group data of only the features existing on the ground is obtained (step S4).

Next, the CPU 80 reads the design data recorded on the hard disk 86 indicating the planar arrangement of the features. This design data is a drawing at the time of designing the feature obtained by measuring the three-dimensional point group data. FIG. 7 shows an example of a blueprint. The CPU 80 extracts the planar shape of each feature based on this design data (step S5). In this embodiment, the planar shape is extracted as a rectangle that includes the planar outer shape of the feature. Note that the planar external shape of each feature may be extracted as is as a planar shape.

Subsequently, the CPU 80 superimposes three-dimensional point cloud data of only the features on this planar shape. Overlay is performed by matching the absolute coordinates of the design data that is the basis of the planar shape with the absolute coordinates of the three-dimensional point group data. At this time, the position of the planar shape in the height direction is made to match the height (elevation) of the ground point (ground surface) of the three-dimensional point cloud data. FIG. 8 shows the matched state. In FIG. 8, only the pedestrian bridge portion of the features shown in FIGS. 6 and 7 is shown. The frame line 300 indicates a rectangular planar shape that surrounds the planar outline of the pedestrian bridge (feature), and it is confirmed that this planar shape 300 matches the position of the three-dimensional point cloud data of the pedestrian bridge (feature). Recognize. Note that the planar external shape is originally expressed as a plane, but in FIG. 8, it is shown with a height for ease of understanding.

For each feature, the CPU 80 selects the one with the highest elevation among the three-dimensional point cloud data corresponding to the planar shape of the feature. The area of the feature is determined by giving the height to the planar shape. Thereby, as shown in FIG. 9, a three-dimensional shape (cuboid) surrounding the feature can be specified. The CPU 80 records the three-dimensional shape thus generated as external shape area data. In this embodiment, external area data is recorded based on the planar shape and the position and height based on absolute coordinates of horizontal azimuth height.

Next, the CPU 80 superimposes each feature area on the three-dimensional point cloud data and displays it on the display 84. The operator looks at this screen and selects the region of each feature using the mouse 90. As a result, an input screen for attribute data such as feature names is displayed, and the operator performs this input. Further, based on the coordinates of the vertices of the three-dimensional shape of the external area data, the coordinates of the center position (other positions representative of the feature may be used) are calculated and used as the coordinate position of the feature. In this way, attribute data can be generated and recorded. FIG. 10 shows attributes data items in this embodiment. The creation date and time is the creation date and time (measurement date and time) of the three-dimensional point cloud data. The feature name is the name of the feature. The feature area is external shape data indicating the external shape of the feature. The feature position is the center position calculated above. In this embodiment, three-dimensional point group data is recorded in XML format.

Note that, in the above description, the operator inputs the name of the feature, but the name of the feature may be estimated based on three-dimensional point cloud data. For example, as shown in FIG. 11, three-dimensional point cloud data 6 of a feature is projected onto a plane at different angles, and the projected image and feature name are used as learning data to run a deep learning program (or other machine learning program). may also be used). Learn about various features and obtain a learned feature estimation program. This feature estimation program may be provided with three-dimensional point cloud data of a target for feature estimation, and the feature name may be automatically estimated and assigned. Alternatively, the estimated feature name may be displayed and the operator may be asked to confirm it.

Furthermore, since there is a possibility that the three-dimensional point cloud data 6 and the external area are misaligned, it may be displayed on the display 84, and the operator may use a mouse or the like to align the two so that their positions match. .

Attribute data can be obtained as described above.

Note that in the above, the external shape area of the feature is determined by giving the height to the planar data. However, if there is no plane data such as design data, the outer shape area may be determined from the three-dimensional point group data itself.

For example, the outline area is determined as follows. The three-dimensional space is divided into small cubic grids, and if there are points in adjacent grids vertically, horizontally, horizontally, and diagonally, they are combined into one. For example, spatial labeling techniques using connected components can be used.

For each region organized as a grid where points exist, regions having a predetermined volume, a predetermined number of points or less, a predetermined point group density or less are removed as noise. Each remaining area becomes an area corresponding to a feature. For each region, a rectangular parallelepiped is set to enclose its outer shape. This rectangular parallelepiped is the external area of the feature.

As described above, attribute data corresponding to three-dimensional point cloud data can be generated in the terminal device 20. The surveying company delivers this three-dimensional point cloud data and attribute data to the delivery destination.

Furthermore, only attribute data can be uploaded to the attribute data server device 60 at the discretion of the surveying company. This allows companies that require three-dimensional point cloud data to access the attribute data server device 60 and refer to the attribute data. If the information of the company providing the corresponding 3D point cloud data is recorded in the attribute data, the company that requires the 3D point cloud data can request the surveying company to obtain the 3D point cloud data based on this information. You can make requests.

Further, the three-dimensional point cloud data may be uploaded to the point cloud server device 40. If the requester for the three-dimensional point cloud data is a local government or the like, it may be requested to upload it to a predetermined point cloud server device 40. As a result, the three-dimensional point cloud data and attribute data will be made public on the Internet.

Furthermore, the three-dimensional point group data and the external area data of the attribute data are indicated in absolute coordinates. Therefore, if there are multiple pieces of 3D point cloud data about the same feature uploaded by different businesses to different point cloud server devices, if at least one attribute data has been uploaded, these multiple 3D point cloud data Group data can be obtained. For example, if three-dimensional point cloud data regarding the same feature is available from different directions depending on the operator, more complete three-dimensional point cloud data can be obtained by acquiring both.

1.4 Three-dimensional model construction process Next, the process of constructing a three-dimensional model based on the plurality of three-dimensional point cloud data released as described above will be described.

Below, a case will be described in which a three-dimensional model of a specific feature is constructed.

12 and 13 show flowcharts of three-dimensional model construction. In the figure, the terminal device is shown as a flowchart of the terminal program, the attribute data server device is shown as the flowchart of the attribute data server program, and the point cloud server device is shown as the flowchart of the point cloud server program. be.

First, the CPU 80 of the terminal device 20b (hereinafter sometimes abbreviated as the terminal device 20b) accesses the attribute data server 60 and specifies the location where a desired feature exists (step S11). The location is specified by specifying a range using latitude and longitude, for example. It is also possible to input (or search) an address or place name, and convert this into a range of latitude and longitude. Furthermore, the range clicked or selected on the map may be converted into a range of latitude and longitude.

In response to this, the CPU of the attribute server device 60 (hereinafter sometimes abbreviated as the attribute server device) searches the attribute data and searches for the external shape within the specified location (which may also include the vicinity outside the location). Region data is extracted (step S21). The attribute server device 60 transmits the extracted external shape area data (planar shape data and its position and height in absolute coordinates), feature name, etc. to the terminal device 20b.

The terminal device 20b displays the outer shape area and the feature name on the display 84 based on the received outer shape area data (step S12). A display example is shown in FIG. The outline areas 102 to 112 of each feature are shown, and the name of the feature is shown in the vicinity.

The user operates the mouse 90 of the terminal device 20b to select the outer shape area of the desired feature. Here, the explanation will proceed assuming that the external area 110 of the building has been selected. In response to this selection, the terminal device 20b specifies the external area data of the external area 110 of the building and transmits a request for three-dimensional point cloud data to each of the point cloud server devices 40a to 40n (step S13).

Each of the point cloud server devices 40a to 40n that receives this request determines whether or not it holds the three-dimensional point cloud data within the relevant external area 110, and if so, returns the data. Here, the explanation will proceed assuming that two of the point cloud server devices 40a to 40n, point cloud server devices 40a and 40b, hold three-dimensional point cloud data of the external area 110 of the building.

The CPU of the point cloud server device 40a (hereinafter sometimes abbreviated as point cloud server device 40a) returns the three-dimensional point cloud data of the external area 110 of the building to the terminal device 20b (step S31).

Similarly, the CPU of the point cloud server device 40b (hereinafter sometimes abbreviated as point cloud server device 40b) returns the three-dimensional point cloud data of the external area 110 of the building to the terminal device 20b (step S41 ).

Note that the point cloud server devices 40a and 40b transmit three-dimensional point cloud data of an area widened in consideration of measurement errors to the designated external area 110. Measurement errors vary depending on the measurement method and equipment, and therefore vary depending on the measurement method and equipment used to measure the three-dimensional point cloud data. Therefore, the widened area described above differs depending on the measurement method and equipment.

The terminal device 20b receives three-dimensional point cloud data (first three-dimensional point cloud data) from the point cloud server device 40a and three-dimensional point cloud data (second three-dimensional point cloud data) from the point cloud server device 40b. receive. FIG. 15A shows an example of the first three-dimensional point group data, and FIG. 15B shows an example of the second three-dimensional point group data. As shown in FIGS. 15A and 15B, the first three-dimensional point group data is measured from the front side of the rectangular parallelepiped as viewed from the paper surface. The second three-dimensional point group data is measured from the back side of the rectangular parallelepiped toward the plane of the paper.

The terminal device 20b aligns and synthesizes the first three-dimensional point group data and the second three-dimensional point group data, and generates synthesized three-dimensional point group data as shown in FIG. 16 (step S14). As is clear from this figure, the 3D point cloud data (which has measurement data from both the front side and the back side) has a larger amount of information than the 1st 3D point cloud data and the 2nd 3D point cloud data alone. Original point cloud data can be obtained.

The composite three-dimensional point group data obtained in this way can be used for various purposes. In this embodiment, a three-dimensional model of a feature is generated based on synthetic three-dimensional point cloud data.

The terminal device 20b generates an external surface of the feature based on the composite three-dimensional point group data (step S15). This processing can be performed, for example, by a three-dimensional convex hull algorithm (http:/www.qhull.org/download/). FIG. 17A shows the generated three-dimensional model of the feature. It is possible to generate a three-dimensional model with a higher degree of completeness than a three-dimensional model generated using only the first three-dimensional point group data and a three-dimensional model generated only using the second three-dimensional point group data. The terminal device 20b displays this on the display 84 and records it on the hard disk 86 (step S15).

According to this embodiment, it is possible to easily search for a plurality of three-dimensional point cloud data scattered on the Internet regarding the same feature, and to synthesize these three-dimensional point cloud data to obtain composite three-dimensional point cloud data. Further, if one attribute data is generated based on any one of a plurality of three-dimensional point cloud data regarding the same feature, the above-described search and synthesis can be performed. This is because the attribute data is independent of the three-dimensional point group data, and the external area data of the attribute data is defined using absolute coordinates.

1.5Other
(1) In the above embodiment, two three-dimensional point group data are acquired to obtain composite three-dimensional point group data. However, three or more three-dimensional point group data may be acquired to generate composite three-dimensional point group data.

(2) In the above embodiment, composite three-dimensional point group data about one feature is obtained. However, composite three-dimensional point cloud data regarding a plurality of terrestrial features may be obtained. Alternatively, composite three-dimensional data for all terrestrial features within a predetermined range may be obtained. Such examples are shown in FIGS. 18A, 18B, and 18C. 18A is the first three-dimensional point group data, FIG. 18B is the second three-dimensional point group data, and FIG. 18C is the composite three-dimensional point group data.

(3) In the above embodiment, three-dimensional point group data with different measurement directions are combined. However, three-dimensional point cloud data with different measured distances to terrestrial features or three-dimensional point cloud data with different measuring instruments (for example, measurements by MMS and UAV) may be combined.

(4) In the above embodiment, a case has been described in which only one piece of attribute data is found for a location in step S21. However, in some cases, different measurement companies create 3D point cloud data for the same location and each uploads attribute data, or even if the same measurement company creates 3D point cloud data for the same location at different times. In some cases, point cloud data is created and attribute data is uploaded for each. In such cases, multiple attribute data will be found for the specified location. In this case, the attribute data server device 60 transmits to the terminal device 20b that there is a plurality of attribute data. The terminal device 20b displays the creation date and time, creation device, etc. for a plurality of attribute data on the display 84, and allows the operator to make a selection. Based on the selected attribute data, the external shape area is transmitted (step S21).

(5) In the above embodiment, the terminal device 20b requests three-dimensional point group data (step S13). However, the attribute data server device 60 may perform this and transmit the result to the terminal device 20b.

(6) In the above embodiment, the terminal device 20b synthesizes three-dimensional point group data and generates a three-dimensional model. However, either one or both may be performed by the attribute data server device 60, and the results may be transmitted to the terminal device 20b.

(7) In the above embodiment, when the terminal device 20b acquires the three-dimensional point group data in step S14, it does not display which direction the data was measured from. However, in order to make this clear, the measurement direction and distance to the feature are displayed using arrows, as shown in Figure 17B, based on the data attached to the acquired three-dimensional point cloud data. You can. The base of the arrow is the measurement position, and the direction of the arrow is the measurement direction. In the case of FIG. 17B, it is clear that three-dimensional point cloud data measured from two locations exists.

Therefore, it can be seen that when performing the next measurement based on this screen display, it is preferable to measure from a direction different from the arrow if the quality of the composite three-dimensional point cloud data is to be improved. Furthermore, if you want to know the temporal changes of a feature, it is preferable to measure from the same position and direction as the arrow.

(8) In the above embodiment, the first and second three-dimensional point group data are simply combined to generate composite three-dimensional point group data. However, if the first 3D point cloud data and the second 3D point cloud data exist for the same location in the 3D point cloud data, only the 3D point cloud data with high measurement accuracy will be used for that location. Alternatively, synthetic three-dimensional point group data may be generated.

(9) In the above embodiment, the first point cloud server device 40a and the second point cloud server device 40b are constructed as separate server devices, but they may be constructed as a single server device.

(10) In the above embodiment, the first point cloud server device 40a, the second point cloud server device 40b, and the attribute data server device 60 are constructed as separate server devices, but they are constructed as a single server device. You can leave it there.

(11) The above embodiments and modified examples can be implemented in combination with other embodiments unless it contradicts the essence thereof.

2. Second embodiment
2.1 Overall Configuration FIG. 19 shows the overall configuration of a point cloud data management system according to the second embodiment of the present invention. The first point cloud server device 40a records a first three-dimensional point cloud data file at a first date and time based on absolute coordinates including terrestrial objects. The second point cloud server device 40b records a second three-dimensional point cloud data file at a second date and time based on absolute coordinates including terrestrial objects. The attribute data server device 60 records external area data based on absolute coordinates surrounding the external shape of a feature.

The terminal device 20 can communicate with these server devices. The three-dimensional point cloud data acquisition means 22 of the terminal device 20 acquires external area data of a desired feature from the attribute data server device 60. The three-dimensional point cloud data acquisition means 22 accesses the first point cloud server device 40a based on the acquired external shape area data of the feature, and converts the three-dimensional point cloud data included in the area into a first three-dimensional point cloud. Obtain as data. Further, the three-dimensional point cloud data acquisition means 22 accesses the first point cloud server device 40b based on the acquired external shape area data of the feature, and transfers the three-dimensional point cloud data included in the area to the second three-dimensional point cloud data. Acquire as point cloud data.

The temporal change acquisition means 25 detects changes in the shape of the feature between the first date and time and the second date and time based on the acquired first three-dimensional point cloud data at the first date and time and second three-dimensional point cloud data at the second date and time. Calculate.

2.2 System Configuration The system configuration is the same as the first embodiment.

2.3 Process for calculating changes over time Next, a process for obtaining changes over time of features based on a plurality of published three-dimensional point cloud data will be described.

FIGS. 20 and 21 show flowcharts for acquiring changes in features over time. In the figure, the terminal device is shown as a flowchart of the terminal program, the attribute data server device is shown as the flowchart of the attribute data server program, and the point cloud server device is shown as the flowchart of the point cloud server program. be.

First, the CPU 80 of the terminal device 20b (hereinafter sometimes abbreviated as the terminal device 20b) accesses the attribute data server 60 and specifies the location where a desired feature exists (step S11). The location is specified by specifying a range using latitude and longitude, for example. It is also possible to input (or search) an address or place name, and convert this into a range of latitude and longitude. Furthermore, the range clicked or selected on the map may be converted into a range of latitude and longitude.

In response to this, the CPU of the attribute server device 60 (hereinafter sometimes abbreviated as the attribute server device) searches the attribute data and extracts external shape area data within the specified location (step S21). The attribute server device 60 transmits the extracted external shape area data (planar shape data and its position and height in absolute coordinates), feature name, etc. to the terminal device 20b.

The terminal device 20b displays the outer shape area and the feature name on the display 84 based on the received outer shape area data (step S12). A display example is shown in FIG. The outline areas 102 to 112 of each feature are shown, and the name of the feature is shown in the vicinity.

The user operates the mouse 90 of the terminal device 20b to select the outer shape area of the desired feature. Here, the explanation will proceed assuming that the external area 110 of the building has been selected. In response to this selection, the terminal device 20b specifies the external area data of the external area 110 of the building and transmits a request for three-dimensional point cloud data to each of the point cloud server devices 40a to 40n (step S13).

Each of the point cloud server devices 40a to 40n that receives this request determines whether or not it holds the three-dimensional point cloud data within the relevant external area 110, and if so, returns the data. Here, the explanation will proceed assuming that two of the point cloud server devices 40a to 40n, point cloud server devices 40a and 40b, hold three-dimensional point cloud data of the external area 110 of the building.

The CPU of the point cloud server device 40a (hereinafter sometimes abbreviated as point cloud server device 40a) returns the three-dimensional point cloud data of the external area 110 of the building to the terminal device 20b (step S31).

Similarly, the CPU of the point cloud server device 40b (hereinafter sometimes abbreviated as point cloud server device 40b) returns the three-dimensional point cloud data of the external area 110 of the building to the terminal device 20b (step S41 ).

The terminal device 20b receives three-dimensional point cloud data (first three-dimensional point cloud data) from the point cloud server device 40a and three-dimensional point cloud data (second three-dimensional point cloud data) from the point cloud server device 40b. receive. Here, the first three-dimensional point group data and the second three-dimensional point group data are obtained by measuring the terrestrial feature from substantially the same direction. However, the measurement date and time are different.

The terminal device 20b generates a three-dimensional model of the feature at the first date and time based on the first three-dimensional point group data (step S17). Furthermore, a three-dimensional model of the feature at the second date and time is generated based on the second three-dimensional point group data (step S17).

The terminal device 20b displays the generated three-dimensional model of the first date and time and the three-dimensional model of the second date and time on the display 84 so that they can be compared (step S18). For example, as shown in FIG. 22, a three-dimensional model 120 for a first date and time and a three-dimensional model 122 for a second date and time are displayed. Below that, the feature name and measurement date are shown. Therefore, in the example of FIG. 22, it can be seen by comparison that the outer shape of the building on February 24, 2010 and the outer shape of the building on March 5, 2019 have changed.

Note that, in order to facilitate comparison, both may be displayed in different colors in an overlapping manner.

Also, for example, if there is a situation in which a feature does not exist at the first date and time and a feature exists at the second date and time (for example, a new road sign has been installed), the location at the second date and time If you have the measurement data of , you can obtain 3D point cloud data of the feature. However, even if there is measurement data for the location at the first date and time, three-dimensional point cloud data of the feature cannot be obtained. Therefore, by displaying a three-dimensional model based on this, it becomes clear that there is no feature on the first date and that there is a feature on the second date.

Note that for the purpose of the above-mentioned comparison over time, it is preferable to use three-dimensional point group data in which the measurement direction with respect to the feature is the same (or similar).

2.4 Others
(1) In the above embodiment, three-dimensional models are generated using three-dimensional point cloud data at two dates and times, and the models are compared. However, it is also possible to acquire three-dimensional point cloud data at three or more dates and times, generate three-dimensional models for each, and compare them.

(2) In the above embodiment, a comparison is made over time for one feature. However, comparison over time may be performed for a plurality of terrestrial features. Alternatively, comparison over time may be performed for all terrestrial features within a predetermined range.

(3) In the above embodiment, the terminal device 20b requests three-dimensional point group data (step S13). However, the attribute data server device 60 may perform this and transmit the result to the terminal device 20b.

(6) In the above embodiment, the three-dimensional model is generated in the terminal device 20b. However, the attribute data server device 60 may perform the process and send the result to the terminal device 20b.

(7) In the above embodiment, when the terminal device 20b acquires the three-dimensional point group data in step S17, it does not display when each three-dimensional point group data was measured. However, in order to clarify this, the measurement date and time of each 3D point cloud data may be displayed based on the data attached to the acquired 3D point cloud data, as shown in FIG. good. When three-dimensional point group data for many dates and times exists, it is preferable to perform such display. The user can view this, select a predetermined number of dates to compare, and display the three-dimensional model.

(8) The above embodiments and modified examples can be implemented in combination with other embodiments unless it contradicts the essence thereof. For example, if there is multiple 3D point cloud data from different directions at the same date and time (even the same month or year), create a 3D model by generating composite 3D point cloud data, and then It may also be compared with three-dimensional models of other dates and times.

(9) In the above embodiment, the first point cloud server device 40a and the second point cloud server device 40b are constructed as separate server devices, but they may be constructed as a single server device.

(10) In the above embodiment, the first point cloud server device 40a, the second point cloud server device 40b, and the attribute data server device 60 are constructed as separate server devices, but they are constructed as a single server device. You can leave it there.

(11) In the above embodiment, the searched video data is played back on the terminal device. At this time, a map or three-dimensional point cloud data of the vicinity where the video was captured may also be displayed. Furthermore, three-dimensional point group data viewed from an imaging position that changes over time may be displayed to match the playback of a moving image.

(12) In the above embodiment, three-dimensional models of features at two points in time are displayed for comparison. However, for both, the normal vector of the outline of the three-dimensional model may be displayed to make it easier to understand the change in surface angle.

Alternatively, it may be divided into minute areas (25 cm cubic area) and the number of points within the areas may be compared and displayed. This allows the degree of roughness of the surface of the feature to be compared.

Furthermore, the reflection intensity of each point or area may be compared. This allows the smoothness of the surfaces to be compared.

Alternatively, the volumes of three-dimensional models may be compared. This allows you to compare the growth of plants, the condition of leaves, etc.

(13) The above embodiments and modified examples can be implemented in combination with other embodiments unless it contradicts the essence thereof.

3. Third embodiment
3.1 Overall Configuration FIG. 24 shows a functional block diagram of a feature search system according to the third embodiment of the present invention. The recording unit 158 of the feature search server device 150 records attribute data such as feature names, external areas, and positions of three-dimensional point cloud data, and video data.

The attribute data transmitting means 150 of the feature search server device 150 reads the feature name or external shape area corresponding to the three-dimensional point cloud data recorded in the recording unit 158 and transmits it to the terminal device 40 . The feature attribute data display means 182 of the terminal device 40 displays the feature name or outer shape area of the received feature on the display unit 188.

An operator operating the terminal device 40 uses the mouse 90 to look at the feature name or outline area of the displayed feature and selects the feature for which the moving image is to be searched. Selection information transmitting means 184 transmits which feature has been selected to feature search server device 150.

The feature position acquisition means 154 of the feature search server device 150 obtains the position of the selected feature. The video data transmitting means 156 searches the video data in chronological order and extracts a portion attached with imaging position information in the vicinity of the feature position. The extracted video data is transmitted to the terminal device 40. The video playback means 186 of the terminal device 40 plays back the received video data on the display unit 188.

This makes it possible to easily search for and display locations where desired terrestrial features are imaged from a huge amount of video data.

3.2 System Configuration FIG. 25 shows the system configuration of the point cloud data management system according to the third embodiment. Point cloud server devices 40a, 40b, . . . 40n are provided on the Internet. A feature search server device 62 is also provided. The point cloud server devices 40a, 40b, . . . , 40n record three-dimensional point cloud data of measured features and video data (with imaging position information) captured at the time of measurement. Attribute data as shown in FIG. 10 is recorded in the feature search server device 62. These server devices 40a, 40b...40n, 60 are provided with terminal devices 20a, 20b...20n so as to be able to communicate via the Internet.

The hardware configuration of the terminal device 20 is the same as that shown in FIG. 3. The point cloud server device 40 and the feature search server device 62 also have similar hardware configurations. However, in the point cloud server device 40, instead of the terminal program 96, a point cloud server program is recorded. Furthermore, in the feature search server device 62, a feature search server program is recorded in place of the terminal program 96.

3.3 Feature Search Process Figure 26 shows a flowchart of feature search. In the figure, what is shown as a terminal device is a flowchart of a terminal program, and what is shown as a feature search server device is a flowchart of a feature search server program.

First, the CPU 80 of the terminal device 20b (hereinafter sometimes abbreviated as the terminal device 20b) accesses the feature search server device 62 and specifies the location where a desired feature exists (step S11). The location is specified by specifying a range using latitude and longitude, for example. It is also possible to input (or search) an address or place name, and convert this into a range of latitude and longitude. Furthermore, the range clicked or selected on the map may be converted into a range of latitude and longitude.

In response to this, the CPU of the feature search server device 62 (hereinafter sometimes abbreviated as feature search server device 62) searches the attribute data and extracts the external shape area data within the specified location ( Step S51). The feature search server device 62 transmits the extracted external shape area data (planar shape data and its position and height in absolute coordinates), feature name, etc. to the terminal device 20b.

The terminal device 20b displays the outer shape area and the feature name on the display 84 based on the received outer shape area data (step S12). A display example is shown in FIG. The outline areas 102 to 112 of each feature are shown, and the name of the feature is shown in the vicinity.

The user operates the mouse 90 of the terminal device 20b to select the outer shape area of the desired feature. Here, the description will proceed assuming that the outer shape area 106 of the marker has been selected. In response to this selection, the terminal device 20b specifies the external area data of the external area 106 of the sign and sends an instruction to the feature search server device 62 to search for the part of the video in which this sign is imaged. (Step S19).

The feature search server device 62 reads the attribute data of the specified feature and acquires the location of the feature (position based on absolute coordinates) (step S52). Next, the feature search server device 62 extracts the portion where the feature is imaged in the captured video data 162 based on the location of the feature (step S53).

FIG. 27 shows the data structure of the captured moving image data 162. Time stamps TS1, TS2, . . . indicating imaging times are recorded in association with the moving image body data 170. Additionally, the position of the camera that captured the image (or the vehicle equipped with the camera, etc.) is recorded in absolute coordinates. Further, although not shown, the captured moving image data 162 is recorded in association with imaging area information for the entire moving image.

FIG. 28 shows the concept of imaging area information. The imaging path R is indicated by a solid line. It is shown that imaging started from the starting point S and was performed until the ending point E. An imaging area AR is shown by a broken line surrounding this imaging path R. The imaging area AR is specified, for example, by diagonal absolute coordinates (imaging area information).

FIG. 29 shows details of the moving image portion extraction process in step S53. The feature search server device 62 selects the video data 162 in which the measurement position is included in the imaging area based on the position of the feature to be searched (step S531). The video data 162 selected in this way may include a portion where a terrestrial feature is imaged. The feature search server device 62 searches for each of the selected video data 162 to see if the imaged portion exists.

In step S533, the feature search server device 62 reads out the imaging positions recorded in the video data 162 in chronological order (step S533). In the example of FIG. 27, the imaging positions PS1, PS2, . . . PSn are read out.

The feature search server device 62 identifies the imaging position PSf closest to the location of the feature among the read imaging positions PS1, PS2, . . . PSn (step S534). The feature search server device 62 determines whether the distance between the closest imaging position PSf and the feature position is less than or equal to a predetermined value (step S535). If it is less than or equal to the predetermined value, it means that a feature is being imaged at the location. The feature search server device 62 extracts video data for a predetermined period of time before and after the identified imaging position PSf (step S536). If the distance between the imaging position PSf and the feature position exceeds a predetermined value, the moving image portion is not extracted.

The above process is performed for all the videos selected in step S531 (steps S532, S537).

After extracting the video data portion as described above, the feature search server device 62 transmits it to the terminal device 20b (step S54).

In response to this, the terminal device 20b displays the extracted video data portion on the display 84 (step S20). Note that when there is a plurality of moving image data, it is preferable to display a list of the moving image data together with the imaging date and time before playback, and allow the user to select and play back.

By watching this video, the user can understand the situation of the search target feature as an image. For example, when you want to know the condition of features that can be identified on the screen display in Figure 14 (checking looseness detection marks (for example, matching marks) attached to bolts and nuts, checking the state of appearance deterioration, etc.) It can be used for.

Note that the above-mentioned confirmation of the detection mark and confirmation of the state of appearance deterioration may be performed by the terminal device using image processing or AI.

3.4 Others
(1) In the above embodiment, the feature position of attribute data and the imaging position of video data are indicated in absolute coordinates. Therefore, without having to prepare attribute data corresponding to each piece of video data, it is possible to search for a feature from a plurality of video data as long as there is one piece of attribute data.

In addition, if attribute data is provided corresponding to each video data, the terrestrial feature position and the imaging position do not need to be absolute coordinates as long as they are consistent. Additionally, if there is attribute data corresponding to the video data, it is also possible to search based on the date and time when the 3D point cloud data of the feature was created and the date and time of shooting, instead of searching based on the feature location and imaging location. .

(2) In the above embodiment, as shown in FIG. 14, the feature to be searched for is specified by the outer shape area and the feature name. However, it is also possible to display only the name of the feature without displaying the external area and to allow the user to select it.

(3) In the above embodiment, the portion of the video in which the feature is captured is extracted and played back. However, in addition to this, it is also possible to mark the features in the video and play it back.

The feature search server device 62 can realize this by performing the following processing. For example, suppose that when there is an external shape area of a feature as shown in FIG. 30, the sign 106 is set as a search target. A trajectory 200 when a moving image is captured is drawn in the space in which this outer shape area is arranged. Since time is attached to this trajectory 200, the imaging time at each point can be known. At each imaging time t1, t2, . . . , it is determined in which direction and at what distance the target feature 106 exists.

Based on this direction and distance, it is calculated how the terrestrial feature 106 will be imaged at the same time corresponding to the captured video. The appearance of the external area of the feature 106 generated in this way at each time is superimposed on the extracted video and transmitted to the terminal device 20b.

When this is played back on the terminal device 20b, as shown in FIG. 31, a frame of the external area will be displayed to surround the target feature in the image. Therefore, the user can easily find the target feature in the video.

(4) In the above embodiment, the point cloud server device 40 and the search server device 62 are constructed as separate server devices, but they may be constructed as a single server device.

(5) In the above embodiment, the case where a video is searched has been described. However, the same process can also be used to search for still images with an image capture location and image capture time attached.

(6) The above embodiments and modified examples can be implemented in combination with other embodiments unless it contradicts the essence thereof.

Claims (10)

A point cloud data management system comprising first and second point cloud data server devices, an attribute data server device, and a plurality of terminal devices capable of communicating with these server devices,
The first point cloud server device records a first three-dimensional point cloud data file from a first direction using absolute coordinates including features,
The second point cloud server device records a second three-dimensional point cloud data file from a second direction using absolute coordinates that includes the same feature as the feature,
The attribute data server device records outer shape area data surrounding the outer shape of the feature and indicated by absolute coordinates,
The terminal device is
The external shape area data of the feature is acquired from the attribute data server device, and based on the external shape area data, the first point cloud data file of the feature corresponding to the first three-dimensional point cloud data file is obtained from the first point cloud server device. 3D point cloud data acquisition means for acquiring 3D point cloud data and acquiring second 3D point cloud data of the feature corresponding to the second 3D point cloud data file from the second point cloud server device; ,
a three-dimensional model construction means for constructing a three-dimensional shape of the feature based on the acquired first and second three-dimensional point cloud data from the first and second directions;
A point cloud data management system with The system of claim 1,
A system characterized in that the first point cloud server device and the second point cloud server device are constructed as one point cloud server device. The system of claim 2,
A system characterized in that the attribute data server device is a server device constructed integrally with the point cloud server device. A first point cloud server device that records a first three-dimensional point cloud data file from a first direction using absolute coordinates including a feature, and a first point cloud data file from a second direction using absolute coordinates including the same feature as the feature. 2. A terminal device capable of communicating with a second point cloud server device that records a three-dimensional point cloud data file and an attribute data server device that records external shape area data surrounding the contour of a feature and indicated by absolute coordinates,
The external shape area data of the feature is acquired from the attribute data server device, and based on the external shape area data, the first point cloud data file of the feature corresponding to the first three-dimensional point cloud data file is obtained from the first point cloud server device. 3D point cloud data acquisition means for acquiring 3D point cloud data and acquiring second 3D point cloud data of the feature corresponding to the second 3D point cloud data file from the second point cloud server device; ,
a three-dimensional model construction means for constructing a three-dimensional shape of the feature based on the acquired first and second three-dimensional point cloud data from the first and second directions;
A terminal device equipped with The terminal device according to claim 4,
A terminal device characterized in that the first point cloud server device and the second point cloud server device are constructed as one point cloud server device. The terminal device according to claim 5,
A terminal device characterized in that the attribute data server device is a server device built integrally with the point cloud server device. A first point cloud server device that records a first three-dimensional point cloud data file from a first direction using absolute coordinates including a feature, and a first point cloud data file from a second direction using absolute coordinates including the same feature as the feature. 2. A computer realizes a terminal device capable of communicating with a second point cloud server device that records a three-dimensional point cloud data file and an attribute data server device that records external area data surrounding the external shape of a feature and indicated by absolute coordinates. A terminal program for running a computer,
The external shape area data of the feature is acquired from the attribute data server device, and based on the external shape area data, the first point cloud data file of the feature corresponding to the first three-dimensional point cloud data file is obtained from the first point cloud server device. 3D point cloud data acquisition means for acquiring 3D point cloud data and acquiring second 3D point cloud data of the feature corresponding to the second 3D point cloud data file from the second point cloud server device; ,
A terminal program for functioning as a three-dimensional model construction means for constructing a three-dimensional shape of the feature based on first and second three-dimensional point cloud data acquired from first and second directions. The terminal program according to claim 7,
A terminal program characterized in that the first point cloud server device and the second point cloud server device are constructed as one point cloud server device. The terminal program according to claim 8,
A terminal program characterized in that the attribute data server device is a server device built integrally with the point cloud server device. Record a first three-dimensional point cloud data file from a first direction using absolute coordinates that includes a feature, and record a second three-dimensional point cloud data file from a second direction using absolute coordinates that includes the same feature as the feature. Record the
Recording outer shape area data indicated by absolute coordinates surrounding the outer shape of the feature,
The computer acquires external shape area data of the feature, based on the external shape area data, obtains first three-dimensional point cloud data of the feature corresponding to the first three-dimensional point cloud data file, and obtains second three-dimensional point cloud data of the feature. Obtaining second three-dimensional point cloud data of the feature corresponding to the three-dimensional point cloud data file,
A point cloud data management method in which synthesized three-dimensional point cloud data is obtained by a computer based on first and second three-dimensional point cloud data acquired from first and second directions.

Images