Image analysis device, image analysis method, and computer program

Abstract

The image analysis device includes an orientation calculation unit that calculates multiple orientation information indicating the orientation of each of multiple faces that constitute the surface of a three-dimensional model that is placed in a virtual space and represents an object in real space; a reference calculation unit that calculates reference plane information that indicates the reference plane based on three or more surface position information that indicate the respective positions of three or more specific locations on the three-dimensional model; and a relative orientation calculation unit that calculates relative orientation information that indicates the orientation of the face relative to the reference plane based on the orientation information and the reference plane information.

Description

[Technical Field]

[0001] The present disclosure relates to an image analysis device, an image analysis method, and a computer program. [Background technology]

[0002] In the point cloud data utilization system described in Patent Document 1, an operator uses an operation unit to specify a predetermined number of points (e.g., three points) from among the projected coordinate points in an image of an area displaying an inspection target. Next, a control unit generates a plane including each of the coordinate points corresponding to the specified projected coordinate points as a virtual reference plane. The virtual reference plane virtually represents a surface in an ideal state without damage, etc. Next, the control unit extracts each of the coordinate points whose normal distance from the virtual reference plane is equal to or greater than a predetermined value as a feature point. Next, a display unit changes the color of only the projected coordinate points corresponding to the feature points to distinguish them from other projected coordinate points. As a result, areas that protrude or recess beyond a predetermined distance from the virtual reference plane are displayed in a distinctive manner. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-105081 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the point cloud data utilization system described in Patent Document 1, if the amount of protrusion or depression of the inspection object is relatively small, the normal distance from the virtual reference plane to the coordinate point may be less than a predetermined value, and the coordinate point may not be extracted as a feature point. In other words, there is a possibility that an abnormality in the inspection object may not be detected.

[0005] Therefore, the present disclosure has been made in consideration of the above-mentioned problems, and its purpose is to provide an image analysis device, an image analysis method, and a computer program that can reduce the occurrence of missed detection of abnormalities in an object. [Means for solving the problem]

[0006] According to the present disclosure, there is provided an image analysis device comprising: a posture calculation unit that calculates a plurality of posture information indicating the posture of each of a plurality of faces that constitute the surface of a three-dimensional model that is placed in a virtual space and represents an object in real space; a reference calculation unit that calculates reference plane information indicating a reference plane based on three or more surface position information indicating the positions of three or more specific locations on the three-dimensional model; and a relative posture calculation unit that calculates relative posture information indicating the posture of the faces relative to the reference plane based on the posture information and the reference plane information.

[0007] Furthermore, according to the present disclosure, there is provided an image analysis method including the steps of: calculating a plurality of pieces of orientation information indicating the orientation of each of a plurality of faces constituting the surface of a three-dimensional model placed in a virtual space and representing an object in real space; calculating reference plane information indicating a reference plane based on three or more pieces of surface position information indicating the respective positions of three or more specific locations on the three-dimensional model; and calculating relative orientation information indicating the orientation of the faces relative to the reference plane based on the orientation information and the reference plane information.

[0008] Furthermore, according to the present disclosure, there is provided a computer program that causes a computer to execute the steps of: calculating a plurality of pieces of orientation information indicating the orientation of each of a plurality of faces that constitute the surface of a three-dimensional model that is placed in a virtual space and represents an object in real space; calculating reference plane information indicating a reference plane based on three or more pieces of surface position information that indicate the respective positions of three or more specific locations on the three-dimensional model; and calculating relative orientation information that indicates the orientation of the faces relative to the reference plane based on the orientation information and the reference plane information. [Effects of the Invention]

[0009] According to the present disclosure, it is possible to provide an image analysis device, an image analysis method, and a computer program that can reduce the occurrence of missed detection of abnormalities in an object. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a block diagram showing an example of the configuration of an image analysis system according to an embodiment of the present invention. [Figure 2] 1(a) and 1(b) are schematic cross-sectional views showing the phenomenon in which lift occurs in concrete of a reinforced concrete structure, which is an object of construction. [Figure 3] FIG. 2 is a block diagram illustrating an example of the configuration of a server according to the embodiment. [Figure 4] FIG. 2 is a perspective view showing an example of a three-dimensional model according to the embodiment. [Figure 5] FIG. 2 is a perspective view showing an example of a surface constituting the surface of the three-dimensional model according to the embodiment. [Figure 6] FIG. 2 is a perspective view showing an example of a point cloud and a reference plane according to the embodiment. [Figure 7] 10 is a diagram illustrating a cross section of an object superimposed on a point cloud and a reference plane of a three-dimensional model according to the embodiment. FIG. [Figure 8] 2 is a perspective view showing a reference plane, a reference plane vector, a surface, a normal vector, and a reference vector according to the embodiment. FIG. [Figure 9] FIG. 2 is a perspective view showing an example of multiple surfaces sharing one point of the point cloud according to the embodiment. [Figure 10] FIG. 10 is a diagram showing a color table according to the embodiment; [Figure 11] FIG. 2 is a diagram schematically illustrating a colored three-dimensional model according to the embodiment. [Figure 12] 10 is a flowchart showing an image analysis method according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Preferred embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. In this specification and drawings, components having substantially the same functional configurations are designated by the same reference numerals, and redundant description will be omitted.

[0012] Fig. 1 is a diagram showing an example of the configuration of an image analysis system SYS according to an embodiment of the present invention. As shown in Fig. 1, the image analysis system SYS includes a server 1. The server 1 corresponds to an example of the "image analysis device" of the present disclosure.

[0013] As an example, the server 1 assists in detecting an abnormality in an object (hereinafter referred to as "object 100") existing in real space. That is, the server 1 assists in inspecting the object 100. The object 100 is, for example, a reinforced concrete structure. In this case, for example, the reinforced concrete structure has wall surfaces (surfaces) that are approximately parallel to the vertical direction. Furthermore, for example, the abnormality in the object 100 is floating concrete.

[0014] 2(a) and 2(b) are schematic cross-sectional views showing the phenomenon in which lifting occurs in concrete 101 of a reinforced concrete structure, which is an object 100. As shown in FIG. 2(a), the reinforced concrete structure, which is the object 100, includes concrete 101 and reinforcing bars 103. A surface 104 of the concrete 101 is exposed. Direction D indicates the vertically upward direction. The reinforcing bars 103 are, for example, deformed reinforcing bars.

[0015] The reinforcing bars 103 may corrode and expand due to the penetration of water or the like into the concrete 101. As a result, cracks 104 may occur in the concrete 101, originating from the reinforcing bars 103. Then, as shown in FIG. 2(b), a part of the concrete 101 is pushed out, causing a floating portion 102.

[0016] The server 1 assists in the detection of a raised portion 102 in the concrete 101 based on a three-dimensional model of the object 100. In particular, in this embodiment, the server 1 can prevent the raised portion 102 from being overlooked in detection based on the three-dimensional model of the object 100, even when the raised distance L1 of the raised portion 102 is relatively small. The raised distance L1 is, for example, approximately 1 mm to approximately 2 mm. Details will be described later.

[0017] Returning to FIG. 1, the image analysis system SYS includes at least one terminal 2, at least one mobile device 3, at least one imaging device 5, or at least one three-dimensional measuring device 7.

[0018] The server 1, the terminal 2, the mobile device 3, the imaging device 5, and the three-dimensional measuring device 7 are connected to a network NW. The network NW includes, for example, the Internet, a closed network, a public telephone network, a LAN (Local Area Network), and a short-range wireless network.

[0019] The terminal 2 is, for example, a personal computer (for example, a notebook computer, a desktop computer, or a tablet).

[0020] The mobile device 3 is, for example, an unmanned mobile device or a manned mobile device. The unmanned mobile device is, for example, an unmanned aerial vehicle such as a drone, an unmanned ground vehicle, an unmanned underwater vehicle, or an unmanned surface vessel. The unmanned ground vehicle is, for example, an unmanned ground vehicle modeled after a living organism (for example, a snake-shaped unmanned ground vehicle). The manned mobile device is, for example, an aircraft, an automobile, a ship, or a submarine. The mobile device 3 includes a camera 4.

[0021] The imaging device 5 includes a camera 6. The imaging device 5 is, for example, a mobile terminal such as a smartphone. The imaging device 5 may be, for example, the camera 6 itself.

[0022] The cameras 4 and 6 capture images of the object 100 and generate video data representing a video including an image of the object 100. A video is a collection of successive two-dimensional images. The cameras 4 and 6 may also generate multiple still image data representing multiple still images each including an image of the object 100. The still images are two-dimensional images.

[0023] Hereinafter, the video data will be referred to as "video data 511" (FIG. 2), and the plurality of still image data will be referred to as "still image data set 512" (FIG. 2).

[0024] Hereinafter, when there is no need to distinguish between cameras 4 and 6, cameras 4 and 6 will be collectively referred to as "camera CM."

[0025] The three-dimensional measuring device 7 measures the shape of the object 100 and generates three-dimensional data (hereinafter referred to as "three-dimensional data 513") representing the shape of the object 100. The three-dimensional data 513 is typically point cloud data. A three-dimensional model of the object 100 is formed by the three-dimensional data 513. The three-dimensional measuring device 7 may be a contact type or a non-contact type. The three-dimensional measuring device 7 may also use an active method or a passive method. Examples of active methods include an optical radar method (ToF (Time of Flight) method), an active stereo method, or an optical interferometry method. The optical radar method measures the shape of the object 100 by irradiating light onto the object 100 and measuring the change in the time or phase until the reflected light returns to a detector. Examples of the optical radar method include LiDAR (Light Detection and Ranging). The active stereo method measures the shape of the object 100 by projecting a laser beam, a slit light, or a code pattern of light. Optical interferometry involves irradiating light onto an object 100 and utilizing the interference of the light to measure the shape of the object 100. An example of a passive method is the lens focusing method.

[0026] Furthermore, the object 100 captured by the camera CM and the object 100 measured by the three-dimensional measuring device 7 are not particularly limited as long as they can be captured by the camera CM or measured by the three-dimensional measuring device 7. Furthermore, for example, the size, shape, pattern, and color of the object 100 are also not particularly limited. For example, the object 100 is one or more movable or immovable property. The object 100 is, for example, one or more objects. Typically, the object 100 is one or more stationary objects. A stationary object is, for example, an artificial object or a natural object. An artificial object is, for example, a structure, a machine, an electronic device, or a work of authorship. A structure is, for example, a building or an infrastructure facility. A building is, for example, a building or a house. An infrastructure facility is a facility for establishing a social infrastructure. For example, an infrastructure facility is a road, a bridge, a road traffic facility, a power generation facility, a power distribution facility, a water treatment facility, or a gas distribution facility. The machine may be, for example, an automobile, a work vehicle, a train, an aircraft, a ship, a submarine, or a robot. The natural object may be, for example, a tree, a forest, the ground, a cliff, a coast, or a river.

[0027] The terminal 2 acquires video data 511 or still image data set 512 generated by the camera CM, or three-dimensional data 513 generated by the three-dimensional measurement device 7. For example, the terminal 2 receives the video data 511 or still image data set 512 transmitted from the camera CM, or the three-dimensional data 513 transmitted from the three-dimensional measurement device 7, via the network NW. The terminal 2 transmits the video data 511, the still image data set 512, and the three-dimensional data 513 to the server 1 via the network NW. Note that the mobile device 3 and the imaging device 5 may transmit the video data 511 or the still image data set 512 to the server 1 via the network NW. Alternatively, the three-dimensional measurement device 7 may transmit the three-dimensional data 513 to the server 1 via the network NW.

[0028] Fig. 3 is a block diagram showing an example configuration of the server 1 in Fig. 1. As shown in Fig. 3, the server 1 includes a calculation unit 10, a communication unit 40, and a storage unit 50. The server 1 may also include an input unit 20 and a display unit 30.

[0029] The input unit 20 is an input device for inputting various pieces of information to the calculation unit 10. For example, the input unit 20 is a keyboard and pointing device, or a touch panel.

[0030] The display unit 30 displays various types of information and is, for example, a liquid crystal display or an organic electroluminescence display.

[0031] The communication unit 40 is connected to the network NW. The communication unit 40 communicates with external devices connected to the network NW. The external devices are, for example, the terminal 2, the mobile device 3, the imaging device 5, and the three-dimensional measuring device 7. The communication unit 40 is a communication device that communicates according to a predetermined communication protocol, and includes, for example, a network interface controller. The predetermined communication protocol is, for example, a protocol compliant with Ethernet (registered trademark) and the Internet Protocol Suite.

[0032] The communication unit 40 receives video data 511 or still image data set 512 from the terminal 2, the mobile device 3, and the imaging device 5 via the network NW. The communication unit 40 also receives three-dimensional data 513 from the terminal 2 and the three-dimensional measurement device 7 via the network NW.

[0033] The storage unit 50 includes a storage device and stores data and computer programs. The storage unit 50 includes a main storage device such as a semiconductor memory, and an auxiliary storage device such as a semiconductor memory and a hard disk drive. The storage unit 50 may also include removable media such as an optical disk. The storage unit 50 may be, for example, a non-transitory computer-readable storage medium.

[0034] The storage unit 50 stores video data 511, a still image data set 512, and three-dimensional data 513. The video data 511, the still image data set 512, and the three-dimensional data 513 are associated with attribute information (hereinafter, "attribute information AT"). The attribute information AT includes, for example, user information, data acquisition conditions, and object information. The user information includes, for example, identification information of the user of the terminal 2, the mobile device 3, the imaging device 5, or the three-dimensional measurement device 7. The data acquisition conditions include, for example, the imaging time or the measurement time. The imaging time and the measurement time are indicated by one or more of the year, month, date, and time. The data acquisition conditions may include information on the imaging location or the measurement location. The imaging location and the measurement location are indicated by, for example, the position coordinates of the camera CM and the three-dimensional measurement device 7 acquired by a global positioning system (GPS) or a global navigation satellite system (GNSS), respectively. The object information includes, for example, identification information of the object 100. The object information may include coordinates of ground control points (GCPs).

[0035] The storage unit 50 also stores a three-dimensional model 514, orientation information 515, reference plane information 516, relative orientation information 517, and a color table TB. These data are stored when they are generated. Details of these data will be described later.

[0036] The calculation unit 10 executes various calculations and includes processors such as a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit).

[0037] Specifically, the calculation unit 10 includes a model generation unit 11, an attitude calculation unit 13, a reference calculation unit 14, and a relative attitude calculation unit 15. The calculation unit 10 may also include a reception unit 12, a coloring unit 16, and a display control unit 17. For example, the calculation unit 10 executes a computer program stored in the storage unit 50 to function as the model generation unit 11, the attitude calculation unit 13, the reference calculation unit 14, the relative attitude calculation unit 15, the reception unit 12, the coloring unit 16, and the display control unit 17.

[0038] The model generation unit 11 acquires, from the video data 511 or the still image dataset 512 in the storage unit 50, a plurality of two-dimensional images generated by capturing images of the object 100 from a plurality of different imaging positions. The model generation unit 11 then generates a three-dimensional model 514 of the object 100 based on the plurality of two-dimensional images. The three-dimensional model 514 is placed in a virtual space. The three-dimensional model 514 represents the three-dimensional shape of the object 100. The three-dimensional model 514 is configured by point cloud data. The point cloud data is data representing a point cloud. A point cloud is a collection of a plurality of points. The point cloud data includes three-dimensional coordinates of each point. The point cloud data may further include one or more of color information (e.g., RGB values) of each point, normal vector information of each point, and reflection intensity information.

[0039] As an example, the model generation unit 11 generates the three-dimensional model 514 by executing SfM (Structure from Motion) processing. The SfM processing refers to processing for generating the three-dimensional model 514 by utilizing the principle of triangulation based on multiple two-dimensional images generated by capturing images of the object 100 having multiple feature points from multiple imaging positions. The SfM processing preferably includes bundle adjustment. The bundle adjustment refers to processing for minimizing reprojection error. In addition to the SfM processing, the model generation unit 11 may also execute MVS (Multi View Stereo) processing. The MVS processing refers to processing for calculating depth and normals for each pixel of each two-dimensional image by multi-view stereo measurement, integrating these, and generating a dense point cloud of the object 100.

[0040] Furthermore, the model generation unit 11 adds surfaces to the three-dimensional model 514 based on the point cloud data. The surface of the three-dimensional model 514 is composed of a plurality of surfaces. The surfaces are typically planes. The surfaces are, for example, polygons. The polygons are, for example, triangles. Each surface constituting the surface of the three-dimensional model 514 may be referred to as a "surface element" or a "plane element."

[0041] The surface assignment process by the model generation unit 11 is, for example, a process of converting point cloud data constituting the three-dimensional model 514 into mesh data. In this case, for example, the model generation unit 11 generates a plurality of polygonal (e.g., triangular) surfaces (e.g., polygons) generated by connecting points of the point cloud data, and represents the three-dimensional model 514 using the plurality of polygonal surfaces. In this case, for example, the model generation unit 11 generates TIN (Triangulated Irregular Network) data based on the point cloud data, and represents the three-dimensional model 514 using the TIN data. As a result, the three-dimensional model 514 is represented by a set of triangular surfaces. Note that the surface assignment process may be, for example, a process of converting point cloud data into surface data.

[0042] Furthermore, the model generation unit 11 may add material information based on the two-dimensional image to the surfaces assigned to the three-dimensional model 514. The material information is information including the color and pattern of the object 100. For example, the model generation unit 11 may execute a process of mapping texture to each surface (e.g., each polygon) constituting the mesh data of the three-dimensional model 514.

[0043] The three-dimensional data 513 generated by the three-dimensional measurement device 7 is similar to the point cloud data constituting the three-dimensional model 514 generated by the model generation unit 11. Similarly, surfaces may be added to the three-dimensional model represented by the three-dimensional data 513, and material information may be added to the surfaces.

[0044] The storage unit 50 stores the three-dimensional model 514 generated by the model generation unit 11. Furthermore, the three-dimensional data 513 generated by the three-dimensional measurement device 7 may be treated as the three-dimensional model 514.

[0045] Note that the methods of generating the three-dimensional data 513 and the three-dimensional model 514 are exemplified by the method executed by the three-dimensional measurement device 7 and SfM processing. However, as long as the three-dimensional data or three-dimensional model can be generated, the generation method is not particularly limited. For example, the three-dimensional data or three-dimensional model may be generated by 3D Gaussian splatting or Neural Radiance Field (NeRF).

[0046] Next, the reception unit 12 will be described with reference to FIGS. 2, 3, and 4. FIG. 4 is a perspective view showing an example of a three-dimensional model 514. As shown in FIG. 4, the three-dimensional model 514 is placed in a virtual space VS. The three-dimensional model 514 represents an object 100 existing in real space. A three-dimensional coordinate system CS is set in the virtual space VS. The three-dimensional coordinate system CS is defined by an X-axis, a Y-axis, and a Z-axis that are orthogonal to each other. The three-dimensional coordinate system CS may be a coordinate system whose origin is a predetermined position in the virtual space VS, or may be a coordinate system to which geospatial coordinates (ground coordinates) or actual size information is assigned.

[0047] As shown in FIGS. 3 and 4 , the reception unit 12 receives designation of a target area 530 to be processed in the three-dimensional model 514. For example, the display control unit 17 displays the three-dimensional model 514 on the display unit of the terminal 2 in response to a request from the terminal 2. The user of the terminal 2 operates the input unit of the terminal 2 to designate the target area 530 on the three-dimensional model 514 displayed on the display unit 30. As a result, the terminal 2 transmits information indicating the target area 530 to the server 1 via the network NW. The communication unit 40 of the server 1 receives the information indicating the target area 530. The reception unit 12 then receives the designation of the target area 530 via the communication unit 40. The storage unit 50 stores the information indicating the target area 530. The configurations of the display unit and the input unit of the terminal 2 are similar to the configurations of the display unit 30 and the input unit 20 of the server 1, respectively.

[0048] Next, the orientation calculation unit 13 will be described with reference to Fig. 3 and Fig. 5. Fig. 5 is a perspective view showing faces 525 that make up the three-dimensional model 514. Fig. 5 shows a part of the three-dimensional model 514. The surface of the three-dimensional model 514 is made up of a plurality of faces 525. In the example of Fig. 5, the faces 525 are triangular faces (for example, polygons). Each face 525 is formed by connecting points 519 that make up the point group 518.

[0049] As shown in FIGS. 3 and 5, the orientation calculation unit 13 calculates a plurality of pieces of orientation information 515 indicating the orientation of each of a plurality of surfaces 525 in the target region 530 (FIG. 4). The storage unit 50 stores the orientation information 515. In the example of FIG. 5, the orientation information 515 is normal information indicating the normal of the surface 525. Therefore, according to this embodiment, the orientation of the surface 525 can be accurately expressed by the normal. Specifically, the normal information includes information on a normal vector 535 of the surface 525. As described above, as a preferred example, the orientation calculation unit 13 calculates a normal vector 535 for each of the plurality of surfaces 525. The normal vector 535 is, for example, a unit normal vector. Specifically, the orientation calculation unit 13 calculates the normal vector 535 based on the three-dimensional coordinates of a plurality of points 519 located at the vertices of the surface 525. For example, the start point and end point of the normal vector 535 are indicated by three-dimensional coordinates.

[0050] Next, the reference calculation unit 14 will be described with reference to Fig. 2, Fig. 3, and Fig. 6. Fig. 6 is a perspective view showing an example of a point cloud 518 and a reference surface 520. Fig. 6 shows a part of the point cloud 518 that constitutes the three-dimensional model 514.

[0051] As shown in FIGS. 3 and 6, the reference calculation unit 14 calculates reference plane information 516 indicating a reference plane 520 based on three or more pieces of surface position information indicating the respective positions of three or more specific points 521 on the three-dimensional model 514. The reference plane 520 is a plane. The reference plane 520 is, for example, a virtual surface. The storage unit 50 stores the reference plane information 516. The surface position information is typically three-dimensional coordinates in a three-dimensional coordinate system CS. In the example of FIG. 5, the number of specific points 521 is "6", but is not particularly limited as long as it is three or more. The specific points 521 are points on the three-dimensional model 514 that correspond to points (normal points) where no abnormality has occurred on the object 100.

[0052] Specifically, the reference calculation unit 14 calculates reference plane information 516 indicating a reference plane 520 by the least squares method based on the three-dimensional coordinates of three or more points 519 located at three or more specific locations 521. The reference plane information 516 includes, for example, information indicating an equation of a plane. The reference plane 520 is a least-squares plane. According to this embodiment, by performing the least-squares method on the three-dimensional coordinates of the three or more points 519, it is possible to accurately determine the reference plane 520 fitted to the multiple points 519.

[0053] More specifically, the receiving unit 12 receives the designation of the specific location 521. For example, in response to a request from the terminal 2, the display control unit 17 causes the display unit of the terminal 2 to display a three-dimensional model 514. The user of the terminal 2 operates the input unit of the terminal 2 to designate three or more specific locations 521 in a target area 530 ( FIG. 4 ) of the three-dimensional model 514 displayed on the display unit 30. For example, the user operates a cursor displayed on the display unit 30 with a pointing device to click three or more locations within the target area 530, thereby designating three or more specific locations 521. In this case, locations within the target area 530 where no abnormalities have occurred (i.e., normal locations) are designated as the specific locations 521. For example, if the target object 100 is a concrete structure, normal locations in the concrete 101 ( FIG. 2 ) where no lifted portions 102, spalling, or peeling have occurred are designated.

[0054] The terminal 2 transmits information indicating the specified specific location 521 to the server 1 via the network NW. The communication unit 40 of the server 1 receives the information indicating the specific location 521. Then, the reception unit 12 accepts the designation of the specific location 521 via the communication unit 40. That is, the reception unit 12 accepts the designation of three or more specific locations 521 via an input unit operated by a user. Therefore, according to this embodiment, the server 1 does not need to perform processing to designate the specific location 521, and the processing load can be reduced. The storage unit 50 stores information indicating three or more specific locations 521.

[0055] The input unit of the terminal 2 corresponds to an example of an "input device" of the present disclosure.

[0056] The reference calculation unit 14 calculates reference plane information 516 indicating the reference plane 520 based on three or more points 519 arranged in three or more specific locations 521 received by the reception unit 12. In other words, the reference calculation unit 14 calculates the reference plane information 516 based on points 519 arranged in specific locations 521 where no abnormality has occurred (normal specific locations 521). Therefore, the reference plane 520 is suitable as a reference for detecting an abnormality in the object 100.

[0057] For example, the reference calculation unit 14 may regard the point 519 that is closest to the specific location 521 among the multiple points 519 as the point 519 located at the specific location 521. Furthermore, for example, the reference calculation unit 14 may regard the point 519 that is located within a circle or sphere of a predetermined radius centered on the specific location 521 among the multiple points 519 as the point 519 located at the specific location 521.

[0058] Next, the relative attitude calculation unit 15 will be described with reference to FIGS. 3 and 7. FIG. 7 is a diagram showing a cross section of the object 100 superimposed on the point cloud 518 and reference plane 520 of the three-dimensional model 514. The object 100 in FIG. 7 is, as an example, the object 100 shown in FIG. 2(b). Note that, in FIG. 7, for the sake of simplicity, the dot hatching indicating the concrete 101 is omitted. Hereinafter, attention will be focused on a boundary region 540 between the floating portion 102 of the concrete 101 and the non-floating portion of the concrete 101. FIG. 7 also shows a surface 525 in the boundary region 540, with points 519a and 519b of the point cloud 518 as two of its three vertices.

[0059] 3 and 7, the relative orientation calculation unit 15 calculates relative orientation information 517 indicating the orientation of the surface 525 with respect to the reference surface 520. The relative orientation information 517 is calculated for each surface 525. As an example, the relative orientation information 517 includes information indicating the inclination of a normal vector 535 with respect to the reference surface 520. Therefore, according to this embodiment, the presence or absence of the floating portion 102 can be determined based on the inclination of the normal vector 535 with respect to the reference surface 520. As a result, even if the floating distance L1 of the floating portion 102 is relatively small, it is possible to prevent the floating portion 102 from being overlooked when being detected.

[0060] In contrast, in a comparative example (e.g., Patent Document 1), if the floating distance L1 of the floating portion 102 is relatively small, the floating portion 102 may not be detected. That is, in the comparative example, point 519, for which the normal distance L2 from the reference plane 520 to point 519 is equal to or greater than a predetermined value, is extracted as a feature point. Therefore, if the floating distance L1 is relatively small, depending on the setting of the reference plane 520, the normal distance L2 to point 519 indicating the surface of the floating portion 102 may be less than the predetermined value. As a result, point 519 indicating the surface of the floating portion 102 may not be extracted as a feature point, and the floating portion 102 may not be detected. That is, in the comparative example, the accuracy of setting the reference plane 520 has a significant impact on the accuracy of detection of the floating portion 102.

[0061] In contrast, in this embodiment, the presence or absence of the floating portion 102 can be determined based on the inclination of the normal vector 535 with respect to the reference surface 520, and therefore, compared to the comparative example, the accuracy of setting the reference surface 520 has a smaller effect on the accuracy of detecting the floating portion 102. Therefore, compared to the comparative example, it is possible to prevent the floating portion 102 from being missed when detected, and it is easy to set the reference surface 520.

[0062] Next, the reference calculation unit 14 and the relative attitude calculation unit 15 will be described in detail with reference to Fig. 3 and Fig. 8. Fig. 8 is a perspective view showing a reference plane 520, a reference plane vector 560, a plane 525, a normal vector 535, and a reference vector 550. Note that, for the sake of simplicity, Fig. 8 shows only one plane 525, one normal vector 535, and one reference vector 550.

[0063] As shown in Fig. 8, the reference calculation unit 14 sets a reference vector 550 with respect to the normal vector 535. The reference vector 550 is parallel to the reference plane 520. The reference vector 550 is, for example, a unit vector. The start point and end point of the reference vector 550 are indicated by three-dimensional coordinates. The reference plane information 516 (Fig. 3) includes information on the reference vector 550.

[0064] Specifically, the reference calculation unit 14 determines a reference plane vector 560. The reference plane vector 560 is a vector that is parallel to the reference plane 520 and is included in the reference plane 520. For example, the reference plane vector 560 is represented by three-dimensional coordinates. The reference plane information 516 (FIG. 3) includes information on the reference plane vector 560. The reference plane vector 560 is, for example, a unit vector.

[0065] As an example, the reference plane vector 560 is parallel to the intersection line between the XY plane and the reference plane 520 in the three-dimensional coordinate system CS, the intersection line between the YZ plane and the reference plane 520, or the intersection line between the ZX plane and the reference plane 520. An intersection line is a straight line formed at the intersection of two planes. In this case, for example, the X-axis and Y-axis in the three-dimensional coordinate system CS are parallel to the horizontal direction, and the Z-axis is parallel to the vertical direction. The XY plane is a plane that includes the X-axis and the Y-axis. The YZ plane is a plane that includes the Y-axis and the Z-axis. The ZX plane is a plane that includes the Z-axis and the X-axis.

[0066] For example, when inspecting a change in the vertical direction of the surface 104 of the object 100, the reference calculation unit 14 sets, among the multiple vectors in the reference plane 520, a vector parallel to the intersection line between the YZ plane and the reference plane 520 or a vector parallel to the intersection line between the ZX plane and the reference plane 520 as the reference plane vector 560. For example, when inspecting a change in the horizontal direction of the surface 104 of the object 100, the reference calculation unit 14 sets, among the multiple vectors in the reference plane 520, a vector parallel to the intersection line between the XY plane and the reference plane 520 as the reference plane vector 560.

[0067] As described above, the reference calculation section 14 can determine the reference plane vector 560 from among a plurality of vectors in the reference plane 520 according to the purpose of the inspection.

[0068] The reference calculation unit 14 translates the reference plane vector 560 so that the starting point of the reference plane vector 560 coincides with the starting point of the normal vector 535. The reference plane vector 560 after the translation is the reference vector 550. In this way, the reference vector 550 is set with respect to the normal vector 535.

[0069] The reference plane vector 560 is information indicating a feature (for example, a posture) of the reference plane 520. Therefore, the reference vector 550 is also information indicating a feature (for example, a posture) of the reference plane 520. Furthermore, the reference vector 550 is parallel to the reference plane vector 560. Therefore, for example, the reference vector 550 is parallel to the intersection line between the XY plane and the reference plane 520 in the three-dimensional coordinate system CS, the intersection line between the YZ plane and the reference plane 520, or the intersection line between the ZX plane and the reference plane 520.

[0070] The reference calculation unit 14 sets a reference vector 550 for each of the normal vectors 535 of all the faces 525 in the target region 530 (FIG. 4).

[0071] The relative orientation calculation unit 15 calculates the dot product (hereinafter referred to as "dotted product value IP1") of the normal vector 535 and the reference vector 550 for each surface 525 in the target region 530. The dot product value IP1 indicates the inclination of the normal vector 535 with respect to the reference vector 550. In other words, the dot product value IP1 indicates the inclination of the normal vector 535 with respect to the reference surface 520. The dot product value IP1 is an example of the relative orientation information 517. The storage unit 50 stores the relative orientation information 517 including information indicating the dot product value IP1.

[0072] As described above, according to the present embodiment, the relative orientation calculation unit 15 can easily calculate the inclination of the normal vector 535 with respect to the reference plane 520, i.e., the inclination of the plane 525 with respect to the reference plane 520, by calculating the dot product value IP1 between the normal vector 535 and the reference vector 550. For example, the larger the absolute value of the dot product value IP1, the greater the inclination of the normal vector 535 (plane 525) with respect to the reference plane 520. Therefore, the relative orientation calculation unit 15 can estimate that the larger the absolute value of the dot product value IP1, the more likely it is that an abnormality has occurred in the region of the object 100 corresponding to the plane 525 that was the subject of calculation of the dot product value IP1. Note that, for example, the relative orientation calculation unit 15 may calculate the angle between the reference vector 550 and the normal vector 535. In this case, the angle is an example of the relative orientation information 517.

[0073] As described above with reference to FIGS. 7 and 8 , according to this embodiment, the relative orientation calculation unit 15 calculates relative orientation information 517 indicating the orientation of the surface 525 relative to the reference surface 520, based on the orientation information 515 (normal vector 535) of each surface 525 in the target region 530 and the reference surface information 516 (reference vector 550) indicating the characteristics of the reference surface 520. Therefore, an abnormality (e.g., a floating portion 102) of the object 100 can be detected based on the relative orientation information 517. As a result, in this embodiment, compared to the comparative example described above, it is possible to suppress the occurrence of an oversight of abnormality detection even when the degree of abnormality of the object 100 (e.g., the floating distance L1) is relatively small. Furthermore, in this embodiment, since the presence or absence of an abnormality in the object 100 can be determined based on the relative orientation information 517, the accuracy of setting the reference surface 520 has a smaller impact on the accuracy of abnormality detection, compared to the comparative example described above. Therefore, it is easier to set the reference surface 520, compared to the comparative example.

[0074] Furthermore, in this embodiment, the relative orientation information 517 (for example, the absolute value of the dot product value IP1) indicates the degree of difference between the orientation of the surface 525 and the orientation of the reference surface 520. For example, a large difference between the orientation of the surface 525 and the orientation of the reference surface 520 indicates that an abnormality has occurred in the region of the object 100 corresponding to the surface 525. For example, if there is no or only a small difference between the orientation of the surface 525 and the orientation of the reference surface 520, this indicates that the region of the object 100 corresponding to the surface 525 is normal.

[0075] Next, the coloring unit 16 will be described with reference to Fig. 3 and Figs. 9 to 11. The coloring unit 16 shown in Fig. 3 colors the three-dimensional model 514 based on a plurality of pieces of relative orientation information 517 for each of a plurality of faces 525 that form the surface of the three-dimensional model 514. The display control unit 17 displays the colored three-dimensional model 514 on the terminal 2. Therefore, by looking at the three-dimensional model 514, the user can recognize the relative orientation information 517 by the color. As a result, according to this embodiment, the user can easily infer that an abnormality has occurred in an area that is colored in a way that indicates a large difference between the orientation of the reference face 520 and the orientation of the face 525.

[0076] Specifically, the coloring unit 16 colors the three-dimensional model 514 based on the dot product value IP1 indicated by the relative orientation information 517. An example of this point will be described with reference to FIGS.

[0077] FIG. 9 is a perspective view showing an example of multiple surfaces 525 sharing one point 519. In the example of FIG. 9, the single point 519 forms a vertex of six surfaces 525. The relative orientation calculation unit 15 calculates an average value (hereinafter referred to as "scalar product IP2") of multiple scalar product values ​​IP1 calculated for the multiple surfaces 525 sharing the single point 519. Then, the relative orientation calculation unit 15 associates the scalar product IP2 with the point 519 shared by the multiple surfaces 525. The scalar product IP2 is an example of relative orientation information 517. The relative orientation calculation unit 15 calculates the scalar product IP2 for each point 519 and associates the scalar product IP2 with each point 519.

[0078] Then, the coloring unit 16 colors each point 519 according to the range to which the dot product value IP2 associated with each point 519 belongs.

[0079] The relative attitude calculation unit 15 may calculate the average value of the plurality of normal vectors 535 of the plurality of surfaces 525. In this case, the relative attitude calculation unit 15 calculates the dot product value of the average value of the normal vectors 535 and the reference vector 550 (FIG. 8). The dot product value in this case is associated with the point 519 by the relative attitude calculation unit 15 as the dot product value IP2.

[0080] FIG. 10 is a diagram showing a color table TB referred to by the coloring unit 16. As shown in FIG. 10, the color table TB associates a plurality of ranges R1 to R10 with a plurality of color information C1 to C10. The ranges R1 to R10 indicate the range of the dot product value IP2 associated with the point 519. The color information C1 to C10 indicate different colors. The color information C1 to C10 is represented by, for example, RGB values. The color table TB is stored in advance in the storage unit 50. Note that the number and values ​​of the ranges and color information are merely examples and are not particularly limited. Furthermore, although the ranges have positive and negative signs, absolute value ranges may also be set.

[0081] The coloring unit 16 refers to the color table TB and colors each point 519 in accordance with color information corresponding to the range to which the dot product value IP2 associated with each point 519 of the point cloud 518 belongs. For example, if the dot product value IP2 belongs to range R2, the coloring unit 16 colors the point 519 in the color indicated by the color information C2.

[0082] Preferably, the coloring unit 16 colors the three-dimensional model 514 in a manner that allows a distinction to be made between when the normal vector 535 of the surface 525 faces vertically upward and when it faces vertically downward with respect to the horizontal direction. According to this preferred example, by looking at the colored three-dimensional model 514, the user can easily estimate a location of the object 100 that shows signs of peeling or falling off (for example, the lifted portion 102 in FIG. 2(b)). The vertically upward and vertically downward include directions oblique to the horizontal direction.

[0083] The color table TB in this preferred example will be described in detail. Fig. 10 shows a reference plane 520, a normal vector 535, and a reference vector 550. In the example of Fig. 10, the reference plane 520 is parallel to the ZX plane. The reference vector 550 is parallel to the Z axis and points vertically upward. The reference vector 550 is parallel to the intersection of the YZ plane and the reference plane 520. The Z axis points vertically. The X axis and the Y axis point horizontally. The normal vector 535a points vertically downward relative to the horizontal direction. The normal vector 535b points vertically upward relative to the horizontal direction.

[0084] When normal vector 535a points vertically downward, dot product value IP2 associated with point 519 located at a vertex of surface 525 has, for example, a negative sign. On the other hand, when normal vector 535b points vertically upward, dot product value IP2 associated with point 519 located at a vertex of surface 525 has, for example, a positive sign.

[0085] In the color table TB, color information C1-C5 associated with ranges R1-R5 corresponding to cases where the dot product value IP2 has a negative sign can be distinguished from color information C6-C10 associated with ranges R6-R10 corresponding to cases where the dot product value IP2 has a positive sign. In this case, for example, color information C1-C5 associated with ranges R1-R5 includes colors that belong to one of cool colors and warm colors. Also, for example, color information C6-C10 associated with ranges R6-R10 includes colors that belong to the other of cool colors and warm colors.

[0086] FIG. 11 is a diagram schematically illustrating a colored three-dimensional model 514. As shown in FIG. 11, the display control unit 17 displays the colored three-dimensional model 514 on the terminal 2. In FIG. 11, color information C1 to C10 and coordinate axes are shown for ease of understanding. The display control unit 17 may also display the color information C1 to C10 on the terminal 2. In the example of FIG. 11, the three-dimensional model 514 is assigned colors indicated by the color information C1 and C5. The user can infer that an abnormality has occurred in the area assigned the color indicated by the color information C1. For example, the user can infer that a raised portion 102 (FIG. 2(b)) has occurred in the area assigned the color indicated by the color information C1. This is because the color information C1 corresponds to the dot product value IP2 belonging to the range R1.

[0087] As described above with reference to FIGS. 10 and 11 , according to this embodiment, the coloring unit 16 colors the three-dimensional model 514 according to the multiple pieces of relative orientation information 517 (scalar product values ​​IP2) associated with the multiple points 519 in the point cloud 518. Therefore, by looking at the three-dimensional model 514, the user can recognize the relative orientation information 517 by color. As a result, the user can estimate the area in the object 100 where an abnormality has occurred by the color indicating an abnormality. In particular, because the reference vector 550 is parallel to the intersection line between the YZ plane and the reference plane 520, the user can easily recognize a change in the vertical direction of the surface of the three-dimensional model 514 (the surface 104 of the object 100) by color.

[0088] As another example, if the reference vector 550 is parallel to the intersection of the XY plane and the reference surface 520, the user can easily recognize horizontal changes in the surface of the three-dimensional model 514 (the surface 104 of the object 100) by color. In this case, for example, the coloring unit 16 colors the three-dimensional model 514 so as to distinguish between when the normal vector 535 faces the vertical direction and when it faces the horizontal direction. The vertical direction and horizontal direction include directions oblique to the vertical or horizontal direction. In this case, for example, the vertical direction side is when the angle is greater than or equal to a threshold angle and less than 90 degrees toward the vertical direction relative to the horizontal direction, and the horizontal direction is when the angle is less than the threshold angle toward the vertical direction relative to the horizontal direction. The threshold angle is, for example, 45 degrees, but is not particularly limited to this.

[0089] Next, an image analysis method according to this embodiment will be described with reference to Fig. 3 and Fig. 12. Fig. 12 is a flowchart showing the image analysis method. The image analysis method is executed by the server 1. As shown in Fig. 12, the image analysis method includes steps S1 to S10. A computer program stored in the storage unit 50 causes the calculation unit 10 to execute steps S1 to S10. In other words, the computer program product realizes steps S1 to S10 when the computer program is executed by the calculation unit 10. The calculation unit 10 corresponds to an example of a "computer" in the present disclosure.

[0090] 3 and 12, first, in step S1, the model generation unit 11 acquires a three-dimensional model 514. Specifically, the model generation unit 11 generates a three-dimensional model 514 representing the object 100 based on a plurality of two-dimensional images generated by capturing images of the object 100 from a plurality of different imaging positions. Alternatively, the model generation unit 11 sets three-dimensional data 513 based on the measurement results of the object 100 in the three-dimensional model 514.

[0091] Next, in step S2, the display control unit 17 causes the terminal 2 to display the three-dimensional model 514.

[0092] Next, in step S3, the receiving unit 12 receives a designation of the target region 530 in the three-dimensional model 514 from the terminal 2 via the communication unit 40.

[0093] Next, in step S4, the orientation calculation unit 13 calculates a plurality of pieces of orientation information 515 that indicate the orientation of each of a plurality of faces 525 that form the surface of the target region 530 of the three-dimensional model 514.

[0094] Next, in step S5, the display control unit 17 causes the target region 530 of the three-dimensional model 514 to be displayed on the terminal 2.

[0095] Next, in step S6, the receiving unit 12 receives designation of three or more specific locations 521 in the target area 530 from the terminal 2 via the communication unit 40.

[0096] Next, in step S7, the reference calculation unit 14 calculates reference surface information 516 indicating the reference surface 520 based on three or more pieces of surface position information indicating the respective positions of the three or more specific points 521. Specifically, the reference calculation unit 14 calculates the reference surface information 516 by the least squares method.

[0097] Next, in step S8, the relative orientation calculation unit 15 calculates, for each surface 525, relative orientation information 517 that indicates the orientation of the surface 525 relative to the reference surface 520 based on the orientation information 515 and the reference surface information 516.

[0098] Next, in step S9, the coloring unit 16 colors the three-dimensional model 514 based on the plurality of pieces of relative orientation information 517.

[0099] Next, in step S10, the display control unit 17 causes the terminal 2 to display the colored three-dimensional model 514. Then, the image analysis method ends.

[0100] 12, according to the image analysis method of this embodiment, the relative orientation calculation unit 15 calculates relative orientation information 517 indicating the orientation of the surface 525 relative to the reference surface 520 (step S8). Therefore, an abnormality in the object 100 can be detected based on the relative orientation information 517. As a result, compared to the comparative example described above, it is possible to prevent an abnormality from being overlooked even when the degree of abnormality in the object 100 is relatively small.

[0101] The image analysis method according to this embodiment is particularly effective when the object 100 is a reinforced concrete structure. For example, as shown in Fig. 7, even when the lifting distance L1 of the lifting portion 102 is relatively small, it is possible to prevent the lifting portion 102 from being overlooked in detection.

[0102] (First Modification) In a first modified example of this embodiment, relative orientation calculation unit 15 executes threshold processing. Specifically, when the absolute value of scalar product value IP1 or the absolute value of scalar product value IP2 indicated by relative orientation information 517 is equal to or greater than a threshold, relative orientation calculation unit 15 determines that the region of object 100 indicated by surface 525 corresponding to scalar product value IP1 or point 519 corresponding to scalar product value IP2 is abnormal.

[0103] Furthermore, for example, the relative orientation calculation unit 15 may set multiple thresholds. In this case, for example, when the absolute value of the scalar product value IP1 or the absolute value of the scalar product value IP2 is equal to or greater than a first threshold, the relative orientation calculation unit 15 determines that the region of the object 100 indicated by the surface 525 corresponding to the scalar product value IP1 or the point 519 corresponding to the scalar product value IP2 is abnormal. Furthermore, for example, when the absolute value of the scalar product value IP1 or the absolute value of the scalar product value IP2 is equal to or greater than a second threshold and less than the first threshold, the relative orientation calculation unit 15 determines that the region of the object 100 indicated by the surface 525 corresponding to the scalar product value IP1 or the point 519 corresponding to the scalar product value IP2 is quasi-abnormal. The second threshold is smaller than the first threshold. Quasi-abnormal indicates a state that is not abnormal but is closer to abnormal than normal. Note that the multiple thresholds may be three or more thresholds.

[0104] (Second Modification) The second modified example of this embodiment differs from the above embodiment in that the coloring unit 16 colors each surface 525. That is, in the second modified example, in the color table TB of Fig. 10, a plurality of ranges R6 to R10 indicate the ranges of the dot product value IP1 of each surface 525. Therefore, the coloring unit 16 refers to the color table TB according to the second modified example, and colors each surface 525 in accordance with color information corresponding to the range to which the dot product value IP1 for that surface 525 belongs.

[0105] Although the preferred embodiments and modifications of the present disclosure have been described in detail above with reference to the accompanying drawings, the technical scope of the present disclosure is not limited to such examples. It is clear that a person skilled in the art of the present disclosure can conceive of various modified or altered examples within the scope of the technical idea described in the claims, and it is understood that these also naturally fall within the technical scope of the present disclosure.

[0106] The devices or systems described herein may be realized as a single device, or may be realized by multiple devices (e.g., cloud servers) partially or entirely connected via a network. For example, some or all of the model generation unit 11, the reception unit 12, the orientation calculation unit 13, the reference calculation unit 14, the relative orientation calculation unit 15, the coloring unit 16, and the display control unit 17 may be realized by the same computer or server. For example, the model generation unit 11, the reception unit 12, the orientation calculation unit 13, the reference calculation unit 14, the relative orientation calculation unit 15, the coloring unit 16, and the display control unit 17 may each be realized by a separate computer or server. Furthermore, for example, the model generation unit 11, the reception unit 12, the orientation calculation unit 13, the reference calculation unit 14, the relative orientation calculation unit 15, the coloring unit 16, and the display control unit 17 may be realized by the terminal 2 or the three-dimensional measurement device 7. For example, the video data 511, still image data set 512, three-dimensional data 513, three-dimensional model 514, posture information 515, reference plane information 516, relative posture information 517, and color table TB may each be stored in a separate storage device or server.

[0107] The series of processes performed by the device described herein may be implemented using software, hardware, or a combination of software and hardware. A computer program for implementing each function of the calculation unit 10 according to this embodiment may be created and installed on a PC or the like. A computer-readable recording medium storing such a computer program may also be provided. Examples of the recording medium include a magnetic disk, an optical disk, a magneto-optical disk, and a flash memory. The computer program may also be distributed, for example, via a network, without using a recording medium.

[0108] Furthermore, the processes described herein using flowcharts do not necessarily have to be performed in the order shown. For example, in FIG. 12, step S4 may be performed between steps S7 and S8. Some processing steps may be performed in parallel. Furthermore, additional processing steps may be employed, or some processing steps may be omitted.

[0109] Furthermore, the effects described herein are merely descriptive or exemplary and are not limiting. In other words, the technology according to the present disclosure may achieve other effects that will be apparent to those skilled in the art from the description of this specification, in addition to or in place of the above-described effects.

[0110] The following configurations also fall within the technical scope of the present disclosure.

[0111] (Item 1) a posture calculation unit that calculates a plurality of posture information pieces that indicate the postures of a plurality of faces that constitute a surface of a three-dimensional model that is placed in a virtual space and represents an object in a real space; a reference calculation unit that calculates reference surface information indicating a reference surface based on three or more pieces of surface position information indicating the respective positions of three or more specific points on the three-dimensional model; a relative orientation calculation unit that calculates relative orientation information indicating an orientation of the surface relative to the reference surface based on the orientation information and the reference surface information.

[0112] (Item 2) Item 2. The image analysis device according to item 1, wherein the orientation information is normal information indicating a normal to the surface.

[0113] (Item 3) the normal information includes information on a normal vector of the surface, 3. The image analysis device according to item 2, wherein the relative orientation information includes information indicating the inclination of the normal vector with respect to the reference plane.

[0114] (Item 4) the reference plane information includes information on a reference vector parallel to the reference plane, 4. The image analysis device according to item 3, wherein the relative orientation information includes information indicating an inner product of the normal vector and the reference vector.

[0115] (Item 5) 5. The image analyzing device according to claim 1, further comprising a coloring unit that colors the three-dimensional model based on a plurality of pieces of relative orientation information.

[0116] (Item 6) the orientation information includes information on a normal vector of the surface, 6. The image analysis device according to item 5, wherein the coloring unit colors the three-dimensional model so as to distinguish between a case where the normal vector is directed vertically upward and a case where the normal vector is directed vertically downward with respect to the horizontal direction.

[0117] (Item 7) the orientation information includes information on a normal vector of the surface, 6. The image analyzing device according to item 5, wherein the coloring unit colors the three-dimensional model so as to distinguish between a case where the normal vector faces a vertical direction and a case where the normal vector faces a horizontal direction.

[0118] (Item 8) 8. The image analyzing device according to any one of items 1 to 7, further comprising a receiving unit that receives designation of the three or more specific locations via an input device operated by a user.

[0119] (Item 9) 9. The image analyzing device according to any one of items 1 to 8, wherein the reference calculation unit calculates the reference surface information by a least squares method based on the three or more pieces of surface position information.

[0120] (Item 10) 10. The image analyzing device according to any one of items 1 to 9, wherein the object is a reinforced concrete structure.

[0121] (Item 11) calculating a plurality of pieces of orientation information indicating the orientations of a plurality of faces constituting a surface of a three-dimensional model placed in a virtual space and representing an object in a real space; calculating reference surface information indicating a reference surface based on three or more pieces of surface position information indicating the respective positions of three or more specific points on the three-dimensional model; and calculating relative orientation information indicating the orientation of the surface relative to the reference surface based on the orientation information and the reference surface information.

[0122] (Item 12) On the computer, calculating a plurality of pieces of orientation information indicating the orientations of a plurality of faces constituting a surface of a three-dimensional model placed in a virtual space and representing an object in a real space; calculating reference surface information indicating a reference surface based on three or more pieces of surface position information indicating the respective positions of three or more specific points on the three-dimensional model; and calculating relative orientation information indicating an orientation of the surface relative to the reference surface based on the orientation information and the reference surface information. [Industrial Applicability]

[0123] The present disclosure provides an image analysis device, an image analysis method, and a computer program, and has industrial applicability. [Explanation of symbols]

[0124] 1 Server (image analysis device), 11 Model generation unit, 12 Reception unit, 13 Orientation calculation unit, 14 Reference calculation unit, 15 Relative orientation calculation unit, 16 Coloring unit, 17 Display control unit

Claims

1. a posture calculation unit that calculates a plurality of posture information pieces that indicate the postures of a plurality of faces that constitute a surface of a three-dimensional model that is placed in a virtual space and represents an object in a real space; a reference calculation unit that calculates reference surface information indicating a reference surface based on three or more pieces of surface position information indicating the respective positions of three or more specific points on the three-dimensional model; a relative orientation calculation unit that calculates relative orientation information indicating an orientation of the surface with respect to the reference surface based on the orientation information and the reference surface information, the reference plane information includes information on a reference vector parallel to the reference plane, The reference calculation unit sets the reference vector to a vector parallel to the intersection between the reference plane and either a plane parallel to the vertical direction or a plane parallel to the horizontal direction, depending on the direction in which the surface of the object is inspected.

2. The image analysis device according to claim 1 , wherein the orientation information is normal information indicating a normal to the surface.

3. the normal information includes information on a normal vector of the surface, The image analysis device according to claim 2 , wherein the relative orientation information includes information indicating an inclination of the normal vector with respect to the reference plane.

4. An image analysis device as described in Claim 3, wherein the relative posture information includes information indicating the dot product of the normal vector and the reference vector.

5. The image analysis device according to claim 1 , further comprising a coloring unit that colors the three-dimensional model based on a plurality of pieces of relative orientation information.

6. the orientation information includes information on a normal vector of the surface, 6. The image analysis device according to claim 5, wherein the coloring unit colors the three-dimensional model in a manner that enables a distinction to be made between a case where the normal vector points vertically upward and a case where the normal vector points vertically downward with respect to the horizontal direction.

7. the orientation information includes information on a normal vector of the surface, The image analyzing device according to claim 5 , wherein the coloring unit colors the three-dimensional model in such a way that it is possible to distinguish between a case where the normal vector faces a vertical direction and a case where the normal vector faces a horizontal direction.

8. The image analyzing device according to claim 1 , further comprising a receiving unit that receives designation of the three or more specific locations via an input device operated by a user.

9. The image analysis device according to claim 1 , wherein the reference calculation unit calculates the reference surface information by a least squares method based on the three or more pieces of surface position information.

10. 3. The image analysis device according to claim 1, wherein the object is a reinforced concrete structure.

11. An image analysis device as described in any one of claims 5 to 7, wherein different color information is respectively associated with multiple ranges, and the coloring unit colors the three-dimensional model based on the color information associated with the range to which the relative posture information belongs.

12. calculating a plurality of pieces of orientation information indicating the orientations of a plurality of faces constituting a surface of a three-dimensional model placed in a virtual space and representing an object in a real space; calculating reference surface information indicating a reference surface based on three or more pieces of surface position information indicating respective positions of three or more specific points on the three-dimensional model; calculating relative orientation information indicating an orientation of the surface relative to the reference surface based on the orientation information and the reference surface information; the reference plane information includes information on a reference vector parallel to the reference plane, An image analysis method in which, in the step of calculating the reference plane information, the reference vector is set to a vector parallel to the intersection between the reference plane and either a plane parallel to the vertical direction or a plane parallel to the horizontal direction, depending on the direction in which the surface of the object is inspected.

13. On the computer, calculating a plurality of pieces of orientation information indicating the orientations of a plurality of faces constituting a surface of a three-dimensional model placed in a virtual space and representing an object in a real space; calculating reference surface information indicating a reference surface based on three or more pieces of surface position information indicating respective positions of three or more specific points on the three-dimensional model; calculating relative orientation information indicating the orientation of the surface relative to the reference surface based on the orientation information and the reference surface information; the reference plane information includes information on a reference vector parallel to the reference plane, A computer program in which, in the step of calculating the reference plane information, a vector parallel to the intersection between the reference plane and either a plane parallel to the vertical direction or a plane parallel to the horizontal direction is set as the reference vector, depending on the direction in which the surface of the object is inspected.

Images