JP7526703B2 - Information processing device, information processing method, program, and measurement method - Google Patents

Description

The present invention relates to an information processing device, an information processing method, a program, and a measurement method, and in particular to an information processing device, an information processing method, and a program that perform estimation processing for measurement work related to three-dimensional image generation, and a measurement method for measurement work related to three-dimensional image generation.

Patent Document 1 describes a technology that uses a 3D scanner to measure the 3D data of the surface of the completed form at the construction site in three dimensions as point cloud data in order to grasp the shape of the completed form at the construction stage at the construction site, and then modifies the construction plan according to the actual shape of the completed form before the construction stage.

JP 2007-277813 A

The 3D images used at construction sites as described in the above document are sufficient if they allow the shape to be grasped, but when generating 3D images to preserve cultural assets or other buildings as historical legacy for future generations, it is desirable to represent not only the shape but also the texture, color, and scenery as closely as possible to the real thing.

In order to express the texture and color of a structure, the number of scans can be increased. However, increasing the number of scans too much will result in the overall scanning time for the job becoming too long, and the data volume of the point cloud data will also increase too much. In addition, more noise will be generated, which may impair the accuracy of the end product (3D image, 3D point cloud image, or video). If the scanning time is long, it will be more susceptible to the effects of ever-changing sunlight and the resulting changes in shadows, which may impair the accuracy of the end product (3D image, 3D point cloud image, or video) depending on the conditions.

The present invention was made in consideration of the above circumstances, and its purpose is to accurately estimate the number of scans when generating three-dimensional images that can express the texture and color of a structure.

In order to solve the above-mentioned problems, each aspect of the present invention adopts the following configuration.

The first aspect relates to an information processing device.
A first information processing device according to a first aspect includes:
An acquisition means for acquiring the area of the site to be measured, the attributes of the object to be measured, and the required reproducibility;
and a calculation means for calculating a recommended number of measurement points to be measured by a measurement device using the acquired site area, attributes of the object to be measured, and the required reproducibility.

A second information processing device according to a first aspect includes:
An acquisition means for acquiring the length of a path within a site to be measured and a required reproducibility;
and a calculation means for calculating a recommended number of measurement points to be measured by a measurement device using the acquired length of the path and the required reproducibility.

A second aspect relates to an information processing method implemented by at least one computer.
A first information processing method according to a second aspect includes:
An information processing device,
Obtain the area of the site to be measured, the attributes of the object to be measured, and the required reproducibility accuracy,
Calculating a recommended number of measurement points to be measured by a measurement device using the acquired site area, attributes of the object to be measured, and the required reproducibility accuracy.

A second information processing method according to a second aspect of the present invention includes:
An information processing device,
Obtain the length of the path within the site to be measured and the required repeatability accuracy,
Calculating a recommended number of measurement points to be measured by a measurement device using the acquired path length and the required repeatability.

The third aspect relates to a measurement method using a recommended value for the number of measurement points calculated by the information processing device of the first aspect.
The measurement method according to the third aspect comprises the steps of:
This includes measuring the object to be measured at a number of measurement points that is ±25% of a recommended value for the number of measurement points calculated by the information processing device of the first aspect, and generating point cloud data for generating a three-dimensional image.

As another aspect of the present invention, the present invention may be a program that causes at least one computer to execute the method of the second aspect. As a further aspect of the present invention, the present invention may be a program that causes at least one computer to execute a procedure for acquiring point cloud data for generating a three-dimensional image generated by the method of the third aspect, and a procedure for generating a three-dimensional image using the acquired point cloud data.

As yet another aspect of the present invention, a computer-readable recording medium having the above-mentioned program recorded thereon may be provided. This recording medium includes a non-transitory tangible medium.
The computer program includes computer program code which, when executed by a computer, causes the computer to carry out the information processing method on an information processing device.

In addition, any combination of the above components, and any transformation of the present invention into a method, device, system, recording medium, computer program, etc., are also valid aspects of the present invention.

Furthermore, the various components of the present invention do not necessarily have to be independent entities, but may be formed as a single member by multiple components, one component may be formed from multiple components, one component may be part of another component, or part of one component may overlap with part of another component, etc.

In addition, although the method and computer program of the present invention describe multiple steps in a sequential order, the order does not limit the order in which the multiple steps are executed. Therefore, when implementing the method and computer program of the present invention, the order of the multiple steps can be changed to the extent that does not interfere with the content.

Furthermore, the multiple steps of the method and computer program of the present invention are not limited to being executed at different times. Therefore, a step may occur while another step is being executed, or the execution timing of a step may overlap in part or in whole with the execution timing of another step, etc.

According to each of the above aspects, it is possible to accurately estimate the number of scans when generating a 3D image that can express the texture and color of a structure.

FIG. 1 is a diagram conceptually illustrating a system configuration of an image processing system that performs processing to generate a three-dimensional image. FIG. 4 is a diagram illustrating a laser irradiation direction of the measuring device. FIG. 13 is a diagram for explaining a method for reducing blind spots of a measurement target. FIG. 13 is a diagram for explaining a method for reducing blind spots of a measurement target. FIG. 13 is a diagram for explaining a method for reducing blind spots of a measurement target. FIG. 1 illustrates an example of a configuration of a computer that realizes an information processing device. 1 is a functional block diagram showing an example of a logical configuration of an information processing apparatus according to an embodiment of the present invention; FIG. 1 is a diagram showing an example of installation of a plurality of measuring devices on a measurement target. FIG. 2 is a diagram showing a route within a site to be measured. FIG. 13 is a diagram for explaining a method for calculating the number of measurement points of measurement devices within a site to be measured. FIG. 13 is a diagram showing an example of a screen for calculating a recommended number of scans. FIG. 13 is a diagram showing an example of a screen on which a calculation result of a recommended value of the number of scans is output. FIG. 13 is a diagram showing an example of a screen on which a calculation result of a recommended value of the number of scans is output. 13 is a diagram for explaining the area conversion of the first calculation process for estimating the average path length within a site to be measured. FIG. 13 is a diagram for explaining the effect on the average path length of the ratio of the short side A to the long side B of the site to be measured. FIG. 13 is a diagram for explaining the line conversion of the first calculation process for estimating the average path length within a site to be measured. FIG. FIG. 13 is a diagram for explaining a method for estimating an average path length in a maze. FIG. 13 is a diagram showing an example of a table in which calculation processes are associated with different attributes of measurement targets. FIG. 13 is a diagram showing an example of a screen for calculating a recommended number of scans. FIG. 13 is a diagram showing an example of a screen on which a calculation result of a recommended value of the number of scans is output. FIG. 13 is a diagram showing an example of a screen for calculating a recommended number of scans. FIG. 2 is a diagram showing an example of a measurement target according to the first embodiment. FIG. 11 is a diagram showing an example of a measurement target according to a second embodiment. FIG. 13 is a diagram showing an example of a measurement target according to a third embodiment. FIG. 13 is a diagram showing an example of a measurement target according to a fourth embodiment. FIG. 13 is a diagram showing an example of a result of comparing an estimated result of the number of scans calculated using parameters used in the calculation process with an actual value. FIG. 13 is a diagram for explaining the minimum number of scans of a measurement device.

The following describes an embodiment of the present invention with reference to the drawings. In all drawings, similar components are given similar reference numerals and descriptions are omitted where appropriate. In addition, in the following drawings, configurations of parts that are not related to the essence of the present invention are omitted and are not shown.

In the embodiment, "acquisition" includes at least one of the following: the device itself goes to retrieve data or information stored in another device or recording medium (active acquisition), and the device itself inputs data or information output from another device (passive acquisition). Examples of active acquisition include making a request or inquiry to another device and receiving a reply, and accessing and reading information from another device or recording medium. Examples of passive acquisition include receiving information that is distributed (or transmitted, push notification, etc.). Furthermore, "acquisition" may mean selecting and acquiring data or information from received data or information, or selecting and receiving distributed data or information.

First Embodiment
<System Overview>
1 is a diagram conceptually showing the system configuration of an image processing system 1 that performs processing to generate a three-dimensional image. The image processing system 1 includes a three-dimensional image generation device 200 that generates a three-dimensional image. The three-dimensional image generation device 200 generates a three-dimensional image using measurement data obtained by using a plurality of measuring devices 3 for a structure. The information processing device 100 of this embodiment estimates the measurement work required to obtain the measurement data used by the three-dimensional image generation device 200.

The measuring device 3 is, for example, a 3D (three-dimensional) laser scanner that performs 3D digital measurements and outputs point cloud data. Point cloud data is a collection of data for multiple points that indicate the three-dimensional shape of a structure, and includes measurement data (relative or absolute coordinate information) that indicates the position of each point, and color information for each point.

The point cloud data measured and generated by the measuring device 3 may be stored in the storage device 220 via the communication network 5, or may be stored in a recording medium of the measuring device 3. The three-dimensional image generating device 200 reads out the point cloud data from the storage device 220 or the recording medium and generates a three-dimensional image. Alternatively, the measuring device 3 may transmit the point cloud data directly to the three-dimensional image generating device 200 via the communication network 5. The three-dimensional image generating device 200 may acquire the point cloud data measured and generated by the measuring device 3 and generate a three-dimensional image. The configuration in which the three-dimensional image generating device 200 generates a three-dimensional image will be described in the embodiment described later.

In this embodiment, the structure preferably includes a cultural property, a temple, a shrine, a world heritage site (recognized by the World Heritage Committee), an industrial heritage site (recognized by the International Committee for the Conservation of Industrial Heritage), a modern industrial heritage site (recognized by the Ministry of Economy, Trade and Industry), a civil engineering heritage site (recommended by the Japan Society of Civil Engineers), a scenic spot, a topography, or a historical building and ruins including at least one of a building and a scenic spot that need to be preserved for future generations. In this case, the three-dimensional image generated by the three-dimensional image generating device 200 of this embodiment is intended to convey the texture and color of the structure to future generations. At a typical construction site, the purpose is to survey the structure, so it is sufficient if the shape can be grasped from the three-dimensional image. In contrast, in this embodiment, the purpose is to convey the shape and texture of the structure to future generations, so it is preferable that not only the shape but also the texture, color, and scene are expressed in the three-dimensional image.

In addition, in historical wooden buildings such as cultural properties, multiple beams and small beams are arranged in a lattice pattern under the roof, eaves, ceiling, etc., so the laser does not directly hit the backs of these beams and small beams. Also, Japanese-style rooms often have complicated shapes such as a tokonoma alcove, a top drawer, a transom, a drop-down hanging, and a bookshelf, so there are many blind spots for the laser.

Figure 2 is a diagram explaining the laser irradiation direction of the measuring device 3. The measuring device 3 changes the laser irradiation angle in the horizontal direction within a range of 360 degrees (Figure 2(a)), and in the vertical direction, it scans the measurement object while changing the laser irradiation angle within a range of 90 degrees toward the zenith and -60 degrees downward, with the horizontal position being 0 degrees (Figure 2(b)). Figure 2(c) shows the entire measurement range of the measuring device 3.

As mentioned above, in historical wooden buildings such as cultural properties, multiple beams and small beams are arranged under the roof and eaves, resulting in blind spots where the laser does not directly hit the backs of these beams and small beams. Below, we will explain a method for reducing these blind spots by devising the installation location of the measurement device 3 using Figures 3 to 5.

In the example shown in FIG. 3, where there is a small beam P2 under a roof P1, when the measuring device 3a is installed in a position under the roof, the area indicated by the dashed line r1 indicates the area where the laser can be irradiated, i.e., the area that can be measured by the measuring device 3a. The rest of the area is a blind spot d.

Therefore, as shown in Figure 4, if the measurement point of measuring device 3a is moved to the position of measuring device 3b and the laser is irradiated from a different angle, the range of dashed line r2 becomes a range that can be measured by measuring device 3a, and the range that was blind spot d in Figure 3 is covered.

On the other hand, even if there is no blind spot d, if the number of measurement installation locations of the measuring devices 3 is small, the number of points in the acquired point cloud data will be small, resulting in a see-through point cloud. To fill in the gaps between the points and make them opaque, it is necessary to increase the number of installation locations of the measuring devices 3 and the number of points acquired. In other words, as shown in Figure 5, by increasing the number of measuring devices 3 (measuring devices 3a to 3e), the density of the point cloud data can be increased (thick lines R1 and R2), and the resolution of the three-dimensional image can be improved.

<Hardware configuration example>
Each function of the information processing device 100 of this embodiment, which will be described later, is realized by a computer. Fig. 6 is a diagram showing an example of the configuration of a computer 60 that realizes the information processing device 100. The 3D image generating device 200 is also realized by a similar computer 60.

The computer 60 includes a CPU (Central Processing Unit) 62, a memory 64, a program 80 that implements the components of the information processing device 100 loaded into the memory 64, a storage 66 that stores the program 80, an I/O (Input/Output) 68, and an interface (communication I/F) 70 for connecting to a communication network.

The CPU 62, memory 64, storage 66, I/O 68, and communication I/F 70 are connected to each other via a bus 69, and the entire information processing device 100 is controlled by the CPU 62. However, the method of connecting the CPU 62 and other components to each other is not limited to bus connection.

Memory 64 is memory such as RAM (Random Access Memory) or ROM (Read Only Memory). Storage 66 is a storage device such as a hard disk, SSD (Solid State Drive), or memory card.

Storage 66 may be a memory such as a RAM or a ROM. Storage 66 may be provided inside computer 60, or may be provided outside computer 60 and connected to computer 60 by wire or wirelessly as long as computer 60 can access it. Alternatively, storage 66 may be provided detachably on computer 60.

The CPU 62 reads the program 80 stored in the storage 66 into the memory 64 and executes it, thereby realizing the functions of each unit (the acquisition unit 102 and the calculation unit 104) of the information processing device 100 in FIG. 7 described below.

The I/O 68 controls the input and output of data and control signals between the computer 60 and other input and output devices. The other input and output devices include, for example, input devices 72 such as a keyboard, touch panel, mouse, and microphone connected to the computer 60, output devices 74 such as a display, printer, and speaker, and interfaces between these input and output devices and the computer 60. Furthermore, the I/O 68 may control the input and output of data with other recording medium reading or writing devices (not shown).

The communication I/F 70 is a network connection interface for communication between the computer 60 and an external device. The communication I/F 70 may be a network interface for connecting to a wired line or a network interface for connecting to a wireless line. For example, the computer 60 that realizes the information processing device 100 may be connected to each measuring device 3 via the communication I/F 70 through the communication network 5.

Each component of the information processing device 100 of this embodiment shown in FIG. 7, which will be described later, is realized by any combination of hardware and software of the computer 60 shown in FIG. 6. Those skilled in the art will understand that there are various variations in the implementation method and device. The functional block diagram showing the information processing device 100 of each embodiment described below shows logical functional blocks, rather than a hardware-based configuration.

The information processing device 100 is, for example, a personal computer, a server computer, a tablet terminal, a smartphone, etc. The information processing device 100 may be configured with multiple computers 60, or may be realized by a virtual server. Alternatively, the information processing device 100 may be in a form in which the use of an application running on a server computer is provided to a user's terminal by SaaS (Software as a Service). Alternatively, the functions of the information processing device 100 running on a web server may be available on a user terminal (for example, a personal computer or a smartphone) by logging in to a dedicated web page from a browser or the like.

In the example of FIG. 1, the information processing device 100 and the 3D image generating device 200 are configured with different hardware, but in other examples, the information processing device 100 may be included in the 3D image generating device 200. In other words, the 3D image generating device 200 may have the functions of the information processing device 100, and the functions of the information processing device 100 may be realized by a computer that realizes the 3D image generating device 200.

<Functional configuration example>
7 is a functional block diagram showing an example of a logical configuration of the information processing device 100 according to this embodiment. The information processing device 100 includes an acquisition unit 102 and a calculation unit 104.
The acquisition unit 102 acquires the length of a path within a site to be measured and the required reproducibility accuracy.
The calculation unit 104 calculates a recommended number of measurement points to be measured by the measurement device 3 using the acquired path length and the required reproducibility accuracy.

The required reproduction accuracy indicates the accuracy required for the three-dimensional image generated as a result of measurement (scanning), i.e., the required point cloud density. In this embodiment, the required reproduction accuracy can be set in four classes (highest accuracy, high accuracy, medium accuracy, and standard). For example, the required reproduction accuracy for the highest accuracy three-dimensional image is called "S class," the required reproduction accuracy for a high accuracy three-dimensional image is called "A class," the required reproduction accuracy for a medium accuracy three-dimensional image is called "B class," and the required reproduction accuracy for a standard three-dimensional image is called "C class." It is to be noted that C class is the level of accuracy for general surveying.

Specifically, for example, point cloud data with a lot of noise or low point cloud density may be classified as Class C, while class S may be classified as Class S with very little noise and the highest point cloud density. As an example, Class C does not require noise reduction or improved color, and only requires a point cloud with a certain degree of density and the shape to be known. For example, if there is a pillar, when drawing a line along the edge of one side of the pillar, the pillar is assumed to be straight, so two points are sufficient. These two points are connected by a line to form the edge of one side of the pillar. In other words, for example, in Class C, even if the pillar is not straight, it can be considered to be straight, and a low point cloud density is sufficient. On the other hand, Class S obtains detailed information between two points, so it is possible to grasp in detail the unevenness of the edge of a pillar that appears to be straight.

<Basics of estimating the number of scans>
The basis for estimating the number of measurement points of the measuring device 3 will be described.
Within the site to be measured, which is surrounded by external structures, a number of measuring devices 3 are installed, for example, at positions indicated by circles marked with the symbol S in Fig. 8. As shown in Fig. 9, the measuring devices 3 (S) are often installed on a route R within the site.

The number of measuring devices 3 to be installed varies depending on the attributes of the object to be measured, as described below. However, the simplest method is to assume that the number of measuring devices 3 to be installed is the number that would be obtained if the measuring devices 3 were arranged at equal intervals along the path length that the measuring devices 3 can be arranged on, and the number can be calculated using the following formula (1).
n _S = L/L _S ... Formula (1)
Here, _nS is a reference value for the number of measurement points (also called the number of scans) of the measurement device 3, L is the path length, and _Ls is the installation interval of the measurement device 3. This reference value _nS of the number of scans serves as a reference for calculating recommended values (e.g., maximum, standard, and minimum values) for each class, which will be described later. As an example, as will be described later, the reference value _nS of the number of scans of the measurement points corresponds to the standard value of the recommended value for class C.

In this way, the calculation unit 104 calculates the reference value _nS for calculating the recommended value by dividing the length (L) of the route within the site by the installation interval (L _S ) of the measurement points within the route.

Figure 10(b) shows an image of measuring devices 3 (symbol S) arranged along a route within the site as shown in Figure 10(a) and spaced at equal intervals within the site.

The installation interval of the measuring devices 3 can be set to the maximum interval (L S in the above formula (1)) at which point clouds acquired by installing measuring devices 3 at multiple locations can be combined in point cloud processing by the three-dimensional image _{generating device 200. This interval differs depending on the equipment and software used. In this embodiment, the set value L S of} this installation interval is described as 15 m, but is not limited to this and can be determined based on the results of trial measurements made in advance for each equipment and software used. Furthermore, the information processing device 100 may be provided with a setting screen (not shown) for changing settings, which can be changed by user operation.

The calculation unit 104 first calculates the reference value n _s of the number of scans at the measurement point using the above formula (1). Then, the calculation unit 104 calculates the standard value of the recommended value for each class using, for example, this reference value n _s . Then, the calculation unit 104 calculates the recommended value for each class with a range (for example, maximum value, standard value, minimum value). In other words, the calculation unit 104 calculates the range of the recommended value. The maximum recommended value is, for example, a value obtained by multiplying the standard value n _x by a coefficient greater than 1 or a value obtained by adding a predetermined value to the standard value n _x . The minimum recommended value is, for example, a value obtained by multiplying the standard value n _x by a coefficient less than 1 or a value obtained by subtracting a predetermined value from the standard value n _x .

The calculation unit 104 calculates, for example, the standard value _{n_x} of the recommended value by multiplying the reference value _{n_s} of the number of scans by a predetermined coefficient ^p_n , as shown in the following formula (2).
n _x = n _s ×p ⁿ ...Formula (2)

Here, an example of the coefficient p ⁿ shown in formula (2) will be described. The coefficient p ⁿ increases as the required measurement accuracy increases. As an example, the required measurement accuracy is defined as a point cloud density level n. The possible values of n are, for example, 0, 1, 2, and 3. When n = 0, the coefficient p ⁿ is 1, when n = 1, the coefficient p ⁿ is 1.5, when n = 2, the coefficient p ⁿ is 2.25, and when n = 3, the coefficient p ⁿ is 3.375. Note that n = 3 corresponds to the above-mentioned S class, and n = 0 corresponds to the above-mentioned C class. When n = 2 corresponds to the above-mentioned A class, and n = 1 corresponds to the above-mentioned B class.

However, the number of values that n can take and the value of the coefficient ^pn for each n are not limited to the above example, and may be changed as appropriate depending on the purpose of the measurement, the object to be measured, etc.

<Example of operation screen>
FIG. 11 is a diagram showing an example of a screen 300 for calculating a recommended number of scans.
The screen 300 includes a path length input section 302 , a class selection section 304 , and a calculate button 310 .
The path length input unit 302 is made up of a GUI (Graphical User Interface) such as a text box, and accepts input of the path length (numerical value) of the measurement target. The class selection unit 304 is made up of a GUI such as a pull-down menu or a drum roll, and accepts selection of the required point cloud density level. In the example shown in this figure, it accepts selection of one class from the above-mentioned four classes (S, A, B, C). The calculation button 310 is made up of a GUI such as an operation button, and accepts an instruction to execute calculation of the number of scans using the class selected in the class selection unit 304 and the path length input to the path length input unit 302.

When the calculation button 310 is pressed, the calculation unit 104 uses the path length input to the path length input unit 302 to calculate the number of scans using formula (1).

FIG. 12 is a diagram showing an example of a screen 300 on which the calculation result of the recommended value of the number of scans is output. When the calculation button 310 of the screen 300 of FIG. 11 is pressed, the display is updated to the screen 300 of FIG. 12. The screen 300 further includes a scan number display section 320 in addition to the screen 300 of FIG. 11. In this example, the scan number display section 320 displays, for example, the recommended value of the number of scans. The recommended values displayed here are the maximum value, the standard value, and the minimum value. The standard value of the recommended value of the number of scans is the value (n _x ) calculated by the calculation section 104 using the above formula (2) for each class. An example of the maximum and minimum values of the recommended value of the number of scans is as described above. In the example shown in this figure, the maximum recommended value is a value obtained by multiplying the standard value by 1.25, and the minimum recommended value is a value obtained by multiplying the standard value by 0.75. However, the coefficients are not limited to these values.

The maximum recommended value is used, for example, when high accuracy is desired, and the minimum recommended value is used, for example, when measurement needs to be completed in a short time. The user then selects the number of scans from the range between the minimum and maximum values. This reduces the burden on the user when deciding the number of scans.

In addition, when the operator changes the class in the class selection section 304 on the screen 300 and presses the calculate button 310, the calculation section 104 recalculates the number of scans corresponding to the selected class and updates the value in the scan number display section 320. Note that the calculate button 310 does not have to be provided, and the number of scans calculated in a predetermined default class (e.g., class C) may be automatically displayed at least when the path length input section 302 receives input of the path length (e.g., each time a numerical value is input, or when a numerical value is input and the input is confirmed by pressing the enter key, etc.).

In another example, as shown in FIG. 13, the screen 300 may not include a class selection section 304, but may include a list display section 322 that displays a list of the number of scans for multiple classes.

Furthermore, screen 300 may be configured to calculate the number of scans by, for example, specifying the above-mentioned formula in a cell of a spreadsheet using general-purpose spreadsheet software.

As described above, in this embodiment, the acquisition unit 102 acquires the length of the path on the site to be measured and the required reproducibility, and the calculation unit 104 estimates the recommended number of scans using the acquired path length and required reproducibility. With this configuration, the recommended number of scans to generate a 3D image of the measurement target can be estimated simply by inputting the path length and the required reproducibility. Therefore, an estimate of the measurement work based on the number of scans can be created efficiently and with simple operations.

Second Embodiment
The above embodiment is configured to calculate the recommended number of scans by inputting the path length of the measurement target, but the present embodiment is configured to calculate the recommended number of scans by inputting the site area of the measurement target. This embodiment is configured to be applied when it is not possible to directly measure the actual path on which the measurement device 3 can be placed. The information processing device 100 of this embodiment has the same configuration as the information processing device 100 of FIG. 7, and will be described using FIG. 7. Note that the configuration of this embodiment can be combined with the configuration of at least one of the other embodiments to the extent that no contradiction occurs.

It is desirable to actually measure the path length of the measurement object, but in some cases, such as at the stage of creating a measurement estimate, it may be difficult to actually measure by on-site reconnaissance, so an average path length, which is an approximate path length, is estimated based on a plan view of the object, etc.

The acquisition unit 102 acquires the site area of the measurement target, the attributes of the measurement target, and the required reproduction accuracy.
The calculation unit 104 calculates a recommended number of measurement points to be measured by the measurement device 3 using the acquired site area, attributes of the measurement object, and required reproducibility.

The site area is the area inside the exterior surrounding the site to be measured, which can be obtained from a floor plan or the like, or, if indoors, the floor area of each floor of the building. If the shape of the site is rectangular and the short and long sides are clear, the area acquired by the acquisition unit 102 may be the length of the short and long sides of the site rectangle. Since sites can have various shapes, it is designed to be able to handle shapes other than rectangular. Details will be described later.

The attributes of the object to be measured include, for example, (a) a site surrounded by an exterior structure, (b) a building, (c) an earthen wall, (d) a structure that requires measurement of the bottom and ceiling, such as a steam locomotive, and (e) a structure contained within a space such as the inside of a tunnel, a maze, or indoors.

The calculation unit 104 calculates the recommended value by performing a first calculation process based on the attributes of the object and a second calculation process according to the required reproduction accuracy on the site area.

The first calculation process is a process of calculating an estimate of the length of a route within the site. The second calculation process is a process of dividing the route by the installation interval of measurement points within the route. A "route" is a route that people can walk on and/or a route on which a measurement device 3 can be installed. The estimated value of the route length is also called the average route length.

The average path length is estimated based on the area of the site to be measured or the length of the short and long sides of the exterior structure. The calculation unit 104 first determines the average path length (first calculation process), and then calculates the number of scans from the average path length (second calculation process). First, the method of estimating the average path length using the first calculation process will be described, and then the method of calculating the number of scans from the average path length using the second calculation process will be described.

<Method 1 for estimating average path length>
A method for estimating the average path length will be described with reference to Fig. 14. Hereinafter, this estimation method will also be referred to as "area conversion".
Fig. 14(a) is a diagram showing an actual route _Rr in a plan view of a site to be measured, in a solid line, on which the measuring device 3 can be placed. Considering a model in which the total length of the actual route _Rr in the vertical direction and the total length of the horizontal direction are assumed to be i times the short side A and j times the long side B of the exterior structure of the site in a plan view, the model can be replaced with the schematic diagram shown in Fig. 14(b). In Fig. 14(b), the replaced route _Rc is shown in a solid line. Fig. 14(b) shows an example in which i and j are each set to 2.

When this model is used, the average path length L _ave can be expressed by equation (3).
L _ave =iA+jB...Formula (3)

When the short side and the long side are equal, the average path length is given by equation (4). That is, it is the square root of the area of the measurement target (the length of one side when the measurement target is a square) multiplied by i.
L _ave = i√(AB) ...Formula (4)

Even if the site is not rectangular, such as triangular or trapezoidal, the average path length can be calculated using the area of the site with formula (4).

Figure 15 shows the ratio (A/B) of the short side A to the long side B of the site, and the calculation results of the average path length calculated using formula (3) and formula (4). The calculation results of formula (3) are plotted as circles (○), and the calculation results of formula (4) are plotted as triangles (△).

As is clear from this figure, the difference between the average path length calculated using the area in formula (4) and the square area (the value when the ratio = 1.0) increases as the ratio (A/B) of the short side A to the long side B of the site decreases or as the ratio (A/B) increases. In other words, when the ratio (A/B) of the short side A to the long side B is far from 1, it is preferable to calculate the average path length using formula (3) using the short side A and long side B rather than using the area.

<Method 2 for estimating average path length>
Further, another method of estimating the average path length is described below, which will hereinafter also be referred to as "linear conversion".
This example is a method for estimating the average path length that is suitable for cases where the measurement object is indoors (especially when it is divided into a corridor and multiple rooms), a passageway, a long and narrow structure, and the inside of a tunnel with a small internal cross section, etc. As shown in Fig. 16, the area of the measurement object is calculated, and then it is assumed to be a passageway with an average width k (m) so that it is equal to the area, and it can be converted to the average path length L _ave using formula (5).
L _ave =AB÷k...Formula (5)

In equation (5), it is preferable to set the average aisle width k = 3.

Furthermore, even in the case of a complex and intricate passageway such as a maze as shown in Figure 17(a), the average path length can be estimated using equation (5). For example, in this example, the dashed line in Figure 17(b) is a path on which measuring device 3 can be placed. As shown in Figure 17(c), the entire area can be modeled as a passageway of width k, and the sum of the lengths of the passageways can be regarded as the average path length. In other words, the average path length can be calculated from the area using equation (5).

<How to convert the average path length to the number of scans>
The calculation unit 104 calculates the number of scans by substituting the average path length calculated using formula (3), formula (4), or formula (5) into formula (1) as the path length.

Here, when the area of the measurement target is small, the reference value _nS of the number of scans becomes too small, resulting in a deviation from reality. In other words, when the area is small, the calculation result of formula (1) may not reach 1. Therefore, in this embodiment, the minimum number of scans α required for measurement (hereinafter also referred to as the minimum number of scans α) is added to formula (1) as shown in the following formula (6).
n _S = L _ave / L _S + α... Formula (6)

The minimum number of scans α can be determined for each estimation method described above as follows:

<<When converted into an area>>
In this case, a provisional value for the number of scans is calculated by substituting the average path length L _ave calculated by equation (3) or equation (4) into equation (1), and the number of scan processes of the measuring devices 3 additionally installed at the starting end and the ending end in order to combine the point clouds is added to this provisional value.
In the method of estimating the average path length using formulas (3) and (4), when i and j are 2, the average path length is estimated as four paths Rc as shown in Fig. 14(a) . At the four intersections of these four paths Rc, it is necessary to place measuring devices 3 that serve as keys for connecting point clouds to each path Rc.

FIG. 27 is a diagram for explaining an example of calculating the minimum number of scans of the measuring device 3. In FIG.
27(a), route R10 has two intersections with other routes, and two measuring devices 3, S1 and S2, are placed on route R10. Similarly, measuring devices 3, S3 and S4, S5 and S6, and S7 and S8, are placed on route R20, R30, R40, respectively. A total of eight measuring devices 3 are placed. Therefore, the minimum number of scans, α, is 8.

<<When converted into a line>>
In the method of estimating the average path length using equation (5), two measuring devices 3 are placed at the beginning and end of each path so that the path converted from the area can be connected to other adjacent paths.

As shown in FIG. 27(b), a measuring device 3 S11 is placed at the start of route R11, and a measuring device 3 S12 is placed at the end. Furthermore, a measuring device 3 S13 is placed at the end of route R12 to connect to route R12 adjacent to the start of route R11. Similarly, a measuring device 3 S14 is placed at the start of route R13 to connect to the start of route R13 adjacent to the end of route R11. Therefore, the minimum number of scans α = 4.

Which of the above-mentioned surface conversion or line conversion calculation processes is used to calculate the path length estimate depends on the attributes of the measurement object. Figure 18 shows an example of a table 120 that associates calculation processes (spatial conditions) with attributes of the measurement object. Table 120 is stored, for example, in storage 66 or memory 64 of computer 60.

<Example of operation screen>
FIG. 19 is a diagram showing an example of a screen 300 for calculating a recommended number of scans.
The screen 300 includes an attribute selection section 332, an area input section 334, a class selection section 304, and a calculation button 310. The class selection section 304 and the calculation button 310 are the same as those in FIG.

The attribute selection unit 332 is made up of a GUI such as a pull-down menu or drum roll, and accepts the selection of the attributes of the measurement object. The attributes of the measurement object are selected from, for example, the exterior, building (indoors), passageway, and structure shown in FIG. 18.

This attribute is used to select the calculation process (area conversion/line conversion), but instead of selecting an attribute, it may be possible to select whether to perform calculation processing, area conversion or line conversion. Attribute selection has the advantage that even beginners with no experience can handle it. On the other hand, calculation processing selection has the advantage that an experienced person can select an appropriate method based on the situation on-site.

The area input unit 334 is made up of a GUI such as a text box, and accepts input of the site area (numerical value) of the measurement target. In this example, the area input unit 334 accepts input of the area, but in other examples, it may accept input of the short side and long side of the site.

Figure 20 is a diagram showing an example of a screen 300 on which the calculation result of the recommended number of scans is output. When the calculate button 310 on the screen 300 of Figure 19 is pressed, the display is updated to the screen 300 of Figure 20. In addition to the screen 300 of Figure 19, the screen 300 further includes a scan number display section 336. In this example, for example, the maximum, standard, and minimum recommended number of scans are output to the scan number display section 336. The standard number of scans is one of the recommended values calculated by the calculation section 104. The maximum and minimum recommended number of scans are values within a predetermined percentage of the standard value, for example, within a range of ±25% of the standard value.

By outputting the maximum and minimum recommended number of scans along with the standard recommended value, it is possible to accommodate changes according to on-site conditions.

In addition, when the operator changes the class in the class selection section 304 on the screen 300 and presses the calculate button 310, the calculation section 104 recalculates the number of scans corresponding to the selected class and updates the value in the scan number display section 336. Note that the calculate button 310 does not have to be provided, and the number of scans calculated in a predetermined default class (e.g., class C) may be automatically displayed at least when the area input is received in the area input section 334 (e.g., each time a numerical value is input, or when a numerical value is input and the input is confirmed by pressing the enter key, etc.).

In another example, similar to FIG. 13 above, the screen 300 may not include the class selection section 304, but may include a list display section (not shown) that displays a list of the number of scans for multiple classes.

Furthermore, the screen 300 may be configured to calculate the number of scans by, for example, specifying the above-mentioned formula in a cell of a spreadsheet using general-purpose spreadsheet software. For example, a spreadsheet may be provided for each type of measurement object, and the number of scans may be calculated by specifying a formula corresponding to each cell.

In this embodiment, the acquisition unit 102 acquires the area of the facility to be measured, the attributes of the measurement object, and the required reproducibility, and the calculation unit 104 estimates the recommended number of scans using the acquired area, attributes, and required reproducibility of the facility. With this configuration, even if the path length of the measurement object cannot be actually measured, the recommended number of scans can be estimated with high accuracy if the area of the measurement object is known.

Third Embodiment
This embodiment is the same as the first and second embodiments, except that it has a configuration in which multiple spaces are set for the object to be measured according to the attributes (shape, etc.) of the object to be measured, and the number of scans is calculated by combining multiple estimation methods for each space.
For example, if the measurement target is a building and its surrounding grounds, three spaces are set: the space of the grounds, and the outside and inside of the building, and the estimation method to be applied to each space is selected to calculate the number of scans.

The calculation unit 104 sets multiple spaces for the object and performs calculation processing for each space.

The calculation unit 104 can select the calculation process for each space using table 120 ( FIG. 18 ) described in the second embodiment above. In table 120, the calculation process is linked to the attributes of the building. For the outside of the building, the calculation unit 104 estimates the average path length by area conversion using formula (4) from the site area, and then calculates the number of scans. For the inside of the building, the calculation unit 104 estimates the average path length by line conversion using formula (5) from the site area for each floor, and then calculates the number of scans.

The calculation unit 104 sums up the calculated number of scans and estimates the recommended number of scans for the entire measurement target.

<Example of operation screen>
FIG. 21 is a diagram showing an example of a screen 300 for calculating a recommended number of scans.
The screen 300 includes an attribute selection section 342, an area input section 344, a class selection section 346, a scan number display section 348, and a calculation button 310. The scan number display section 348 includes a row 340 that displays at least one calculation result, and a total display section 350 that displays the total value of the scan numbers of the multiple rows 340.

Rows 340 on screen 300 may be added or deleted by user operation. In this example, attribute selection section 342 selects the conversion method used when calculating the number of scans from the area. Area input section 344 and class selection section 346 are the same as area input section 334 and class selection section 304 in FIG. 19.

For example, when measuring the exterior and interior of a building, the calculation unit 104 calculates the recommended number of scans by converting the area of the lot on which the building is built into an area using formula (3) or formula (4). The operator selects "area" in the attribute selection unit 342 of the screen 300, inputs the lot area into the area input unit 344, and selects, for example, "C class" in the class selection unit 346. Furthermore, for the interior of the building, the calculation unit 104 calculates the recommended number of scans by converting the area of each floor into a line using formula (5), that is, by substituting the average path length calculated using formula (5) into formula (1) as the path length. The operator adds one row 340 for each floor, selects "line" in the attribute selection unit 342 of the screen 300, inputs the area of each floor into the area input unit 344, and selects, for example, "S class" in the class selection unit 346.

When the operator presses the calculation button 310, the number of scans calculated by the calculation unit 104 is displayed in the scan number display unit 348 and the total display unit 350, respectively.

In this embodiment, the calculation unit 104 sets multiple spaces for the object and performs calculation processing for each space. The calculation unit 104 can select the calculation processing corresponding to the attributes of the measurement object using table 120 in FIG. 18. The calculation unit 104 can calculate the number of scans by adding up the recommended values calculated for each space. With this configuration, the measurement object can be divided into multiple spaces and the recommended number of scans can be estimated using appropriate calculation processing for each space, allowing for more accurate estimation.

Fourth Embodiment
This embodiment differs from the above-mentioned embodiments in that a measuring device 3 is installed to perform measurements so that the three-dimensional image generating device 200 can obtain point cloud data to be used to generate a three-dimensional image using recommended values calculated by the information processing device 100 of the first to third embodiments.

In this embodiment, a predetermined number of measurement points are set in the measuring device 3. This predetermined number is a value within the range of recommended values calculated by the information processing device 100, as described in the first embodiment. The upper limit of this range is, for example, a value 1.25 times the standard value, and the lower limit is, for example, a value 0.75 times the standard value. The measuring device 3 then measures the measurement target at the set measurement points and generates point cloud data for generating a three-dimensional image. Here, the measurements may be performed using one measuring device 3 or multiple measuring devices 3.

With this configuration, measurement work can be performed on-site with the number of scans calculated with high accuracy, so there is little discrepancy between the number of scans at the estimation stage and the number of scans at the actual measurement work stage, reducing confusion on-site and improving work efficiency.

Then, the three-dimensional image generating device 200 generates a three-dimensional image using the point cloud data generated by the above-mentioned measurement method. The program executed by the computer 60 that realizes the three-dimensional image generating device 200 includes a procedure for acquiring point cloud data for generating a three-dimensional image generated by the measurement method, and a procedure for generating a three-dimensional image using the acquired point cloud data.

With this configuration, it is possible to generate a three-dimensional image that satisfies the required reproduction accuracy conditions using point cloud data generated through efficient measurement work using the measurement method of the present invention.

Although the embodiments of the present invention have been described above with reference to the drawings, these are merely examples of the present invention, and various configurations other than those described above can also be adopted.
For example, in the above embodiment, the calculated recommended number of scans is configured to be displayed on screen 300, but in other examples, it may be printed out on a printer, output to a file and recorded, or the data file may be sent or uploaded to another computer (a specified destination) via email or message.

Hereinafter, an embodiment of estimating the number of scans for each measurement object will be described. Here, the following four objects will be described as examples.
(Example 1) When the site includes a building and the purpose is to measure the shape of the site (however, measurement of the building roof is not included)
(Example 2) Combined measurement of the exterior and interior of a building (Example 3) Earthen wall (Example 4) Steam locomotive

Example 1
A case will be described where a site includes a building and the purpose is to measure the shape of the site (excluding measurement of the building roof). FIG. 22(a) shows the shape of the site of an example of the measurement target. FIG. 22(b) is a plan view of the site of the example of the measurement target in FIG. 22(a). As shown in FIG. 22(b), the site is a rectangle with a clear short side A and long side B. Therefore, the measurement target can be captured as a single space in the form of a surface. In this example, the calculation unit 104 can calculate the average path length using formula (3) or formula (4).

The calculation results are shown in Table 1 below. The number of scans can be calculated using formula (2). For comparison, the calculation results of both formulas (3) and (4) are shown.

Example 2
A case where the exterior and interior of a building are measured in combination as shown in Fig. 23(a) will be described. Fig. 23(b) is a plan view showing the perimeter of the site surrounding the building to be measured in Fig. 23(a) and building P3. Unlike the above, in this example, the number of scans is estimated for each of the exterior and interior of the building, and the total number of scans is calculated.

Specifically, the measurement target is divided into three spaces: the exterior, the first floor of the building, and the second floor of the building. Furthermore, if the building roof is included in the measurement range, the building roof can be added to divide it into four spaces. The building roof is classified as a different space because it cannot be seen from the exterior or the interior of the building, and is not at the same line of sight (height of the measuring device 3) as the exterior and interior of the building. Furthermore, the calculation processing for each space is performed using area conversion for the exterior of the building, i.e., the exterior part, and linear conversion for the interior of the building (first and second floors).

The calculation results for this example are shown in Table 2. Table 2 also shows the actual number of scans measured.

For the exterior, it can be seen that the actual values are within the range of the maximum (standard value + 25%) and minimum (standard value - 25%) recommended values for each number of scans for B class. For the first and second floors of the building, which have a complex structure, it can be seen that the actual values are within the range of the maximum (standard value + 25%) and minimum (standard value - 25%) recommended values for each number of scans for S class.

Example 3
An example will be described in which a 100 m long and 3 m high earthen wall P4 is the measurement target as shown in FIG. 24(a). FIG. 24(b) is a plan view of the site of the earthen wall P4 in FIG. 24(a). The site area is 2400 ^m2 . The space to be measured can be divided into three spaces: a path extending along both sides of the earthen wall in the longitudinal direction of the earthen wall, and the roof of the earthen wall. On the other hand, each space is arranged so that the entire space can be seen across the roof of the earthen wall, so it can be treated as a surface. As Example 5, the number of scans was also calculated when the measurement of the earthen wall was treated as a line.

The calculation results for this example are shown in Table 3. Table 3 also shows the actual number of scans measured.

Whereas the actual number of scans was 85, the standard recommended value for S class in surface conversion in Example 3 was 75, and the standard recommended value for B class in line conversion in Example 5 was 87. In other words, in Example 3, an S class 3D image was obtained with approximately the same number of scans as the actual number of scans that obtained an S class 3D image. On the other hand, in Example 5, only a B class 3D image was obtained with approximately the same number of scans as the actual number of scans that obtained an S class 3D image. In order to obtain an S class 3D image in Example 5, a number of scans that was twice the actual number of scans was calculated.

Based on these results, it can be said that the linear conversion estimation method is preferable for application in places with no line of sight and indoors.

Example 4
An example will be described in which a steam locomotive P5 as shown in FIG. 25(a) is the measurement target. FIG. 25(b) is a plan view of the site on which the steam locomotive P5 of FIG. 25(a) is installed. Even if the area of the measurement target is small, such as the steam locomotive P5, it is a structure with a complicated shape, such as machine parts, and detailed measurement is required. The exterior of the side of the steam locomotive P5 can be treated as the site (exterior) part. However, the bottom and ceiling of the steam locomotive P5 are at a different line of sight from the exterior part and the measuring device 3, and the bottom part is at a lower position and the ceiling part is at a higher position than the exterior part, and can be divided into three spaces.

Specifically, the tripod with the measuring device 3 attached was placed on the ground at the exterior position, the tripod was placed on top of the steam locomotive at the ceiling position, and the measuring device 3 was slid under the bottom surface at the bottom surface position, and positioned so as to surround the sides of the object to be measured. Therefore, the exterior and ceiling parts were converted into surfaces, but the bottom surface part was converted into lines.

The calculation results for this example are shown in Table 4. Table 4 also shows the actual number of scans measured.

As can be seen from Table 4, by appropriately dividing the space and selecting the appropriate calculation process, highly accurate estimation results can be obtained.

Example 5
In Example 5 described in Example 3, the following parameters were used in the estimation method described in the above embodiment.
L _S =15: installation interval of the measuring device 3 i=j=2: constant when the path length within the area of the short side A and the long side B is modeled as a constant multiple of the short side and the long side k=3: width (m) when the average path length is a line
p = 1.5: A constant for calculating the number of scans for each level when increasing the point cloud density level.

These parameters were determined so that the squared error of the difference (proportion) between the estimated number of scans and the actual value was minimized using past performance data as input data. However, the values of each parameter were rounded down.

Figure 26 shows an example of the results of comparing the estimated number of scans calculated using the above parameters with the actual value. In the figure, the ±15% error range is indicated by a dashed line. The ±25% range for the standard value of the recommended number of scans is plotted as "-". Figure 26 also shows that the estimated results are within a deviation of ±15%, indicating that the estimated results are obtained with high accuracy.

Although the present invention has been described above with reference to the embodiments and examples, the present invention is not limited to the above-mentioned embodiments and examples. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.
In the present invention, when information about a user is acquired and used, this shall be done lawfully.

Below, examples of reference forms are given.
1. An acquisition means for acquiring the area of a site to be measured, attributes of the object to be measured, and a required reproducibility;
a calculation means for calculating a recommended number of measurement points to be measured by a measurement device using the acquired site area, attributes of the object to be measured, and the required reproducibility.
2. An acquisition means for acquiring the length of a route within a site to be measured and a required reproducibility;
a calculation means for calculating a recommended value for the number of measurement points to be measured by a measurement device, using the acquired length of the path and the required reproducibility.
3. In the information processing device according to 1 or 2,
The calculation means is an information processing device that sets a plurality of spaces for the measurement object and performs calculation processing for each of the spaces.
4. In the information processing device according to 1. or 3. which cites 1.,
The calculation means calculates the recommended value by performing a first calculation process based on an attribute of the object to be measured and a second calculation process according to the required reproduction accuracy on the site area.
5. In the information processing device according to 4.,
An information processing device, wherein the first calculation process is a process of calculating an estimate of the length of a route within the premises, and the second calculation process is a process of dividing the route by an installation interval of measurement points within the route.
6. In the information processing device according to 5.,
The information processing device, wherein the first calculation process calculates an estimated value L of the path length using any one of the following equations:
L=iA+jB...Formula (a)
L= 4 √(AB) ...Formula (b)
L=AB÷k...Formula (c)
Here, i and j are each 2, A is the length of the short side of the exterior of the site, B is the length of the long side of the exterior of the site, and k is the conversion passage width.
7. In the information processing device according to 2. or 3. which cites 2.,
The calculation means calculates the recommended value by dividing a length of a route within the premises by an installation interval of measurement points within the route.

8. On the computer:
Procedures for obtaining the area of the site to be measured, the attributes of the object to be measured, and the required reproducibility;
A program for executing a procedure for calculating a recommended number of measurement points to be measured by a measuring device using the acquired site area, attributes of the object to be measured, and the required reproducibility accuracy.
9. On the computer:
The length of the path through the premises to be measured and the procedure for obtaining the required repeatability;
A program for executing a procedure for calculating a recommended number of measurement points to be measured by a measuring device, using the acquired path length and the required reproducibility.
10. In the program according to 8. or 9.,
The calculating step is a program that causes a computer to set a plurality of spaces for the measurement object and perform calculation processing for each of the spaces.
11. In the program according to 8. or 10. which cites 8.,
The calculation step calculates the recommended value by having a computer perform a first calculation process based on the attributes of the object to be measured and a second calculation process according to the required reproduction accuracy on the site area, the program.
12. In the program according to 11.,
A program, wherein the first calculation process is a process of calculating an estimate of the length of a route within the premises, and the second calculation process is a process of dividing the route by the installation interval of measurement points within the route.
13. In the program according to 12.,
The first calculation process calculates an estimated value L of the path length using any one of the following formulas.
L=iA+jB...Formula (a)
L= 4 √(AB) ...Formula (b)
L=AB÷k...Formula (c)
Here, i and j are each 2, A is the length of the short side of the exterior of the site, B is the length of the long side of the exterior of the site, and k is the conversion passage width.
14. In the program according to 9. or 10. which cites 9.,
The calculation step includes a program for calculating the recommended value by a computer dividing the length of the route within the site by the installation interval of measurement points within the route.

15. An information processing device,
Obtain the area of the site to be measured, the attributes of the object to be measured, and the required reproducibility accuracy,
An information processing method, comprising: calculating a recommended number of measurement points to be measured by a measuring device using the acquired site area, attributes of the object to be measured, and the required reproducibility accuracy.
16. An information processing device,
Obtain the length of the path within the site to be measured and the required repeatability accuracy,
An information processing method, comprising: calculating a recommended number of measurement points to be measured by a measurement device using the acquired path length and the required reproducibility.
17. In the information processing method according to 15. or 16.,
The information processing method, wherein the information processing device sets a plurality of spaces for the measurement object and performs arithmetic processing for each of the spaces.
18. In the information processing method according to 15. or 17. which cites 15.,
The information processing method, wherein the information processing device calculates the recommended value by performing a first calculation process based on attributes of the object to be measured and a second calculation process according to the required reproduction accuracy on the site area.
19. In the information processing method according to 18.,
An information processing method, wherein the first calculation process is a process of calculating an estimate of the length of a route within the premises, and the second calculation process is a process of dividing the route by the installation interval of measurement points within the route.
20. In the information processing method according to 19.,
The information processing method, wherein the first calculation process calculates an estimated value L of the path length using any one of the following equations:
L=iA+jB...Formula (a)
L= 4 √(AB) ...Formula (b)
L=AB÷k...Formula (c)
Here, i and j are each 2, A is the length of the short side of the exterior of the site, B is the length of the long side of the exterior of the site, and k is the conversion passage width.
21. In the information processing method according to 16. or 17. which cites 16.,
The information processing method, wherein the information processing device calculates the recommended value by dividing a length of a route within the premises by an installation interval of measurement points within the route.

22. A measurement method comprising: measuring an object to be measured at a number of measurement points that is ±25% of a recommended number of measurement points calculated by the information processing device described in any one of 1. to 7.; and generating point cloud data for generating a three-dimensional image.
23. To the computer:
22. A procedure for acquiring point cloud data for generating a three-dimensional image generated by the measurement method described in 2.
A program for executing a procedure for generating a three-dimensional image using the acquired point cloud data.

1 Image processing system 3, 3a, 3b, 3c, 3d, 3e Measuring device 5 Communication network 60 Computer 62 CPU
64 Memory 66 Storage 68 I/O
69 Bus 70 Communication I/F
72 Input device 74 Output device 80 Program 100 Information processing device 102 Acquisition section 104 Calculation section 120 Table 200 Three-dimensional image generating device 220 Storage device 300 Screen 302 Path length input section 304 Class selection section 310 Calculation button 320 Scan number display section 322 List display section 332 Attribute selection section 334 Area input section 336 Scan number display section 340 Row 342 Attribute selection section 344 Area input section 346 Class selection section 348 Scan number display section 350 Total display section

Claims (13)

An acquisition means for acquiring the area of the site to be measured, the attributes of the object to be measured, and the required reproducibility;
a calculation means for calculating a recommended number of measurement points to be measured by a measurement device using the acquired site area, attributes of the object to be measured, and the required reproducibility. An acquisition means for acquiring the length of a path within a site to be measured and a required reproducibility;
a calculation means for calculating a recommended value for the number of measurement points to be measured by a measurement device, using the acquired length of the path and the required reproducibility. 3. The information processing device according to claim 1,
The calculation means is an information processing device that sets a plurality of spaces for the measurement object and performs calculation processing for each of the spaces. In the information processing device according to claim 1 or claim 3 which cites claim 1,
The calculation means calculates the recommended value by performing a first calculation process based on an attribute of the object to be measured and a second calculation process according to the required reproduction accuracy on the site area. 5. The information processing device according to claim 4,
An information processing device, wherein the first calculation process is a process of calculating an estimate of the length of a route within the premises, and the second calculation process is a process of dividing the route by an installation interval of measurement points within the route. 6. The information processing device according to claim 5,
The information processing device, wherein the first calculation process calculates an estimated value L of the path length using any one of the following equations:
L=iA+jB...Formula (a)
L= 4 √(AB) ...Formula (b)
L=AB÷k...Formula (c)
Here, i and j are each 2, A is the length of the short side of the exterior of the site, B is the length of the long side of the exterior of the site, and k is the conversion passage width. In the information processing device according to claim 2 or claim 3 which cites claim 2,
The calculation means calculates the recommended value by dividing a length of a route within the premises by an installation interval of measurement points within the route. On the computer,
Procedures for obtaining the area of the site to be measured, the attributes of the object to be measured, and the required reproducibility;
A program for executing a procedure for calculating a recommended number of measurement points to be measured by a measuring device using the acquired site area, attributes of the object to be measured, and the required reproducibility accuracy. On the computer,
The length of the path through the premises to be measured and the procedure for obtaining the required repeatability;
A program for executing a procedure for calculating a recommended number of measurement points to be measured by a measuring device, using the acquired path length and the required reproducibility. An information processing device,
Obtain the area of the site to be measured, the attributes of the object to be measured, and the required reproducibility accuracy,
An information processing method, comprising: calculating a recommended number of measurement points to be measured by a measuring device using the acquired site area, attributes of the object to be measured, and the required reproducibility accuracy. An information processing device,
Obtain the length of the path within the site to be measured and the required repeatability accuracy,
An information processing method, comprising: calculating a recommended number of measurement points to be measured by a measurement device using the acquired path length and the required reproducibility. A measurement method in which a measurement target is measured at a number of measurement points that is ±25% of a recommended value for the number of measurement points calculated by an information processing device according to any one of claims 1 to 7, and point cloud data for generating a three-dimensional image is generated. On the computer,
A step of acquiring point cloud data for generating a three-dimensional image generated by the measurement method according to claim 12;
A program for executing a procedure for generating a three-dimensional image using the acquired point cloud data.

Images