KR102160196B1 - Apparatus and method for 3d modeling - Google Patents

Abstract

A 3D modeling method according to an exemplary embodiment may include obtaining a point cloud for an indoor space by a lidar; Acquiring a plurality of indoor images of the indoor space by a camera; Determining a feature point deficient region from the plurality of indoor images; And 3D modeling the indoor space based on a point cloud corresponding to the feature point deficient region and the plurality of indoor images.

Description

3D modeling device and method {APPARATUS AND METHOD FOR 3D MODELING}

The present invention relates to a 3D modeling apparatus and method for 3D modeling an indoor space.

In recent years, various contents implemented using virtual information such as virtual reality or augmented reality have been proposed. Virtual Reality (VR) is a virtual space created with artificial technology using a computer, etc., and has characteristics that are similar to reality but not real. Augmented Reality (AR) is a field of virtual reality, and refers to a technique that allows users to recognize virtual information as a part of reality by synthesizing virtual information on a real object viewed by the user.

In order to implement such a virtual reality or a virtual space to which augmented reality is applied, 3D modeling in the field of computer graphics may be used. 3D modeling refers to the process of creating a mathematical model that can be reproduced in a virtual three-dimensional space.

If an actual indoor space is to be reproduced as a virtual space, information on the actual space can be used during 3D modeling. For example, if 3D modeling is performed using an image of a real space, a virtual 3D space similar to the real space may be created.

Korean Patent Application Publication No. 10-2015-0129260 (published on December 14, 2011)

The problem to be solved by the present invention is to provide a 3D modeling apparatus and method for 3D modeling an indoor space based on a point cloud corresponding to a feature point deficient region.

However, the problem to be solved by the present invention is not limited to those mentioned above, and another problem to be solved that is not mentioned may be clearly understood by those of ordinary skill in the art to which the present invention belongs from the following description. will be.

A 3D modeling method according to an exemplary embodiment may include obtaining a point cloud for an indoor space by a lidar; Acquiring a plurality of indoor images of the indoor space by a camera; Determining a feature point deficient region from the plurality of indoor images; And 3D modeling the indoor space based on a point cloud corresponding to the feature point deficient region and the plurality of indoor images.

According to another embodiment, a 3D modeling method includes: obtaining a scan image of an indoor space by a 3D scanner; Determining a feature point deficient region from the acquired scan image; Obtaining a point cloud corresponding to the feature point deficient region by the 3D scanner; Acquiring a plurality of indoor images of the indoor space by a camera; And 3D modeling the indoor space based on the acquired point cloud and the plurality of indoor images.

A 3D modeling apparatus according to an embodiment includes a lidar that acquires a point cloud for an indoor space; A camera that acquires a plurality of indoor images of the indoor space; And an image processing unit configured to determine a feature point deficient region from the plurality of indoor images, and 3D model the indoor space based on a point cloud corresponding to the feature point deficiency region and the plurality of indoor images.

The 3D modeling apparatus and method according to an embodiment may obtain a high-quality 3D model because an indoor image acquired by a camera and a point cloud acquired by a lidar are used together.

In addition, since the point cloud obtained by the lidar is used only for the region lacking the feature point among the images of the indoor space, the computational amount of 3D modeling may be reduced compared to the case of using the point cloud for the entire indoor space.

1 is a functional block diagram of a 3D modeling apparatus according to an embodiment of the present invention.
2 is a flowchart of a 3D modeling method according to an embodiment of the present invention.
3 is a diagram illustrating an indoor image according to an embodiment of the present invention.
4 is a diagram illustrating a feature point extraction image according to an embodiment of the present invention.
5 is a diagram illustrating a 3D mesh according to an embodiment of the present invention.
6 is a diagram illustrating a first intermediate 3D model according to an embodiment of the present invention.
7 is a diagram illustrating a region lacking feature points on the first intermediate 3D model of FIG. 6.
8 is a diagram illustrating a point cloud for a feature point deficient region according to an embodiment of the present invention.
9 is a diagram illustrating a 3D mesh for a feature point deficient region of FIG. 8.
FIG. 10 is a diagram illustrating a second intermediate 3D model for a region lacking a feature point of FIG. 8.
11 is a diagram illustrating a 3D model in which a region lacking a feature point in the first intermediate 3D model of FIG. 6 is replaced with the second intermediate 3D model of FIG. 10.
12 is a flowchart of a 3D modeling method according to another embodiment of the present invention.
13 is a diagram illustrating a 3D scanner according to the embodiment of FIG. 1.

Advantages and features of the present invention, and a method of achieving them will become apparent with reference to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms. It is provided to completely inform the scope of the invention to those who have, and the invention is only defined by the scope of the claims.

In describing the embodiments of the present invention, if it is determined that a detailed description of a known function or configuration may unnecessarily obscure the subject matter of the present invention, a detailed description thereof will be omitted. In addition, terms to be described later are terms defined in consideration of functions in an embodiment of the present invention, which may vary according to the intention or custom of users or operators. Therefore, the definition should be made based on the contents throughout this specification.

1 is a functional block diagram of a 3D modeling apparatus according to an embodiment of the present invention.

The 3D modeling apparatus 100 according to an embodiment of the present invention may obtain a 3D model for an indoor space by performing 3D modeling to reproduce an indoor space as a virtual three-dimensional space. The 3D model thus obtained may be used when providing virtual reality or augmented reality (AR) content.

In this case, the higher the similarity between the actual indoor space and the 3D model, the higher the user's immersion. Therefore, in 3D modeling, it is necessary to use accurate information about the actual indoor space.

As a 3D modeling method of an indoor space, there is a method of using an image acquired by the camera 120. Specifically, 3D modeling may be performed by acquiring a plurality of images of an indoor space by the camera 120 and extracting feature points of the acquired plurality of images. The method of using the image acquired by the camera 120 can reproduce the material and color of the indoor space in detail. On the other hand, since the image acquired by the camera 120 performs 3D modeling based on feature points, performance may be degraded when modeling a single color area.

One of the other methods for 3D modeling of an indoor space is to use a point cloud obtained by LiDAR 112. Specifically, a point cloud for an indoor space may be obtained by the lidar 112 and 3D modeling may be performed based on this. In this case, it can exhibit excellent performance in realizing the overall shape of the indoor space. However, due to the nature of the point cloud based on depth information, it may be unsuitable for reproducing the material or color of an indoor space. In addition, when 3D modeling an indoor space, it is necessary to remove a dynamic object, but the point cloud by the lidar 112 may be problematic including information on the dynamic object.

In order to solve the problems of each of the camera 120 and the lidar 112, the indoor space can be 3D modeled by using the image acquired by the camera 120 and the point cloud acquired by the lidar 112 together. May be. This method can exhibit excellent performance when implementing not only the material but also the shape. Nevertheless, when images and point clouds are used at the same time, the amount of computation increases rapidly, and it may be difficult to apply it to 3D modeling of an indoor space.

To solve this, the 3D modeling apparatus 100 according to an embodiment of the present invention can perform 3D modeling using a point cloud for a region lacking a feature point, and using an image acquired by the camera 120 for the remaining region. have.

Specifically, referring to FIG. 1, a 3D modeling apparatus 100 according to an embodiment of the present invention includes a 3D scanner 110 for obtaining a point cloud; A camera 120 for obtaining an image; An image processing unit 130 that performs 3D modeling; And a control unit 140 that controls each component of the 3D modeling apparatus 100.

The 3D scanner 110 may acquire a point cloud for an indoor space in which a 3D model is to be acquired. To this end, the 3D scanner 110 may obtain a point cloud by irradiating a laser into an indoor space and receiving a reflected laser.

To this end, the 3D scanner 110 may include a lidar 112 that irradiates a pulsed laser and receives the reflected laser. Specifically, the lidar 112 may irradiate a pulsed laser into an indoor space, and measure a time and intensity required until the irradiated pulsed laser is reflected and returned. The measured results may include information such as distance, direction, speed, temperature, material distribution, and concentration characteristics for an indoor space. The lidar 112 may finally store the measurement result in the point cloud. Here, since the point cloud means a set of data on the coordinate system, the point cloud acquired by the lidar 112 may include information on the indoor space of the 3D coordinate system.

The lidar 112 may be provided to irradiate a laser to the entire indoor space in order to obtain a point cloud for the entire indoor space. Since the laser has straightness, the lidar 112 according to an exemplary embodiment may be provided to be panned and/or tilted to enable laser irradiation in all directions.

In addition, in order to add color information to the point cloud, the 3D scanner 110 may further include a scan camera 111. The scan camera 111 may acquire a scanned image and provide it to the lidar 112, and the lidar 112 may add color information to the point cloud by matching the scanned image provided on the acquired point cloud. have.

The camera 120 may acquire a plurality of indoor images of the indoor space. Since the camera 120 has limitations according to the angle of view, it may be difficult for one indoor image to include information on the entire indoor space. Accordingly, one single camera 120 may acquire a plurality of indoor images by photographing an indoor space multiple times at different locations. In contrast, each of the plurality of cameras 120 facing different directions may acquire a plurality of indoor images by capturing different locations of the indoor space.

The image processing unit 130 may perform 3D modeling of an indoor space using a point cloud and an indoor image. In particular, the image processing unit 130 according to an exemplary embodiment may determine a feature point deficiency region and perform 3D modeling using a point cloud and an indoor image corresponding to the determined feature point deficiency region. This will be described later.

The controller 140 may control each component of the 3D modeling apparatus 100. For example, the controller 140 may supply or cut off power to each component of the 3D modeling apparatus 100 and control communication between each component. In addition, the controller 140 may start or stop the operation of each component of the 3D modeling apparatus 100.

In particular, the controller 140 may control the laser irradiation direction of the lidar 112 of the 3D scanner 110. As described above, when the lidar 112 is provided to be panned and/or tiltable, the controller 140 may determine the panning angle and/or tilting angle of the lidar 112 to irradiate the laser in a desired direction. For example, the controller 140 may determine a panning angle and/or a tilting angle of the lidar 112 to irradiate a laser to a region lacking a feature point in an indoor space. This will be described later.

The image processing unit 130 and the control unit 140 described above may be implemented as a computing device including a microprocessor, for example, a central processing unit (CPU) and a graphic processing unit (Graphic Processing Unit). , GPU) may be implemented in at least one of. Alternatively, the image processing unit 130 and the control unit 140 may be implemented as one SOC (System On Chip).

So far, each configuration of the 3D modeling apparatus 100 has been described. Hereinafter, a 3D modeling method performed by the 3D modeling apparatus 100 according to various embodiments will be described.

2 is a flowchart of a 3D modeling method according to an embodiment of the present invention.

The 3D modeling apparatus 100 may first acquire a plurality of indoor images of the indoor space by the camera 120 (S100). An indoor image acquired by the camera 120 will be described with reference to FIG. 3.

3 is a diagram illustrating an indoor image according to an embodiment of the present invention.

As described above, the 3D modeling apparatus 100 may acquire a plurality of indoor images for different locations using a single camera 120, and, unlike this, a plurality of cameras 120 facing different directions. Each of them can be used to obtain a plurality of indoor images for different locations.

Each of the plurality of indoor images obtained in this way may overlap at least a portion with at least one other indoor image. In FIG. 3, a plurality of different indoor images including the same area of the indoor space are illustrated.

When a plurality of indoor images are matched with each other based on this, image information on the entire indoor space may be obtained. In addition, since each indoor image photographed in different directions for the same area includes depth information on the indoor space, the 3D modeling apparatus 100 may perform 3D modeling using this later.

Referring back to FIG. 2, the 3D modeling apparatus 100 may determine a feature point deficient region from a plurality of acquired indoor images (S110). Hereinafter, a method of determining a feature point deficiency region will be described with reference to FIGS. 4 to 7.

4 is a diagram illustrating a feature point extraction image according to an embodiment of the present invention, FIG. 5 is a diagram illustrating a 3D mesh according to an embodiment of the present invention, and FIG. 6 is a diagram illustrating a feature point extraction image according to an embodiment of the present invention. A diagram illustrating a first intermediate 3D model, and FIG. 7 is a diagram illustrating a feature point deficient region on the first intermediate 3D model of FIG. 6.

In order to determine the region lacking the feature point, the image processing unit 130 of the 3D modeling apparatus 100 may first extract the feature point from a plurality of indoor images acquired by the camera 120. Specifically, the image processing unit 130 may extract feature points from each of the plurality of indoor images based on a corner, an edge, and a gradient.

In addition, the image processing unit 130 may add depth information to feature points extracted from each indoor image. As described above, since each indoor image has been captured for the same area in different directions, the image processing unit 130 may add depth information to the feature point based on the difference between each indoor image. To this end, the image processing unit 130 according to an embodiment may apply a triangulation method.

The image processing unit 130 may acquire coordinates of the feature point in a 3D space by adding depth information to the feature point. Through this, the image processing unit 130 may arrange the feature points extracted from the indoor image in the 3D space. Referring to FIG. 4, feature points arranged on a 3D space may include shape, material, and/or color information about an indoor space.

In this case, the image processing unit 130 may check the number of extracted feature points in the unit area. When there is an area in which the identified number is less than or equal to the predetermined reference number, the image processing unit 130 may determine the area as a feature point deficient area. Here, the reference number may mean the maximum number of feature points insufficient for 3D modeling.

In addition, after extracting the feature points, the image processing unit 130 may generate a 3D mesh based on the feature points. Specifically, the image processing unit 130 may generate a 3D mesh in which a plurality of vertices are rigged by the bone by using the extracted feature points as bones and vertices.

The 3D mesh generated in this way may be composed of a plurality of polygons comprising at least three vertices. Referring to FIG. 5, it can be seen that the 3D mesh includes a myriad of vertices and polygons formed by them.

In this case, the image processing unit 130 may check the area among a plurality of polygons constituting the 3D mesh. If there is a polygon having the identified area equal to or greater than the reference area, the image processing unit 130 may determine an area formed by the corresponding polygon as a feature point deficient area. Here, the reference area may mean the minimum area of a polygon that is inappropriate for 3D modeling.

Furthermore, after generating the 3D mesh, the image processing unit 130 may generate a first intermediate 3D model by performing texturing on the 3D mesh. Specifically, the image processing unit 130 may perform texturing by applying a color to a corresponding polygon based on color information of each vertex forming each polygon with respect to the 3D mesh. Referring to FIG. 6, it can be seen that the first intermediate 3D model obtained by texturing the 3D mesh forms a virtual space similar to the actual indoor space.

However, as shown in the upper part of FIG. 6, an untextured area may exist. Such an area may be configured differently from an actual indoor space, thereby lowering the user's sense of experience. Accordingly, the image processing unit 130 may determine an untextured region of the first intermediate 3D model as the feature point deficient region. In FIG. 7, a case where the feature point deficient region S is determined for the first intermediate 3D model of FIG. 6 is illustrated.

Also, the image processing unit 130 may determine an area selected by a user's input among the first intermediate 3D model as a feature point deficient area. In this case, the user may directly determine the feature point deficient region or the image processing unit 130 may determine the feature point deficient region based on the user's input.

As such, the image processing unit 130 includes a first condition in which the number of feature points is too small in an indoor image, a second condition in which the polygon width is too large among the 3D meshes, a third condition that is not textured in the first 3D model, and the first 3D In the model, a region that satisfies at least one of the fourth conditions selected by the user's input may be determined as the feature point deficient region. Since the determined area cannot be 3D modeled using only the indoor image, the 3D modeling apparatus 100 may use the point cloud obtained by the lidar 112 for 3D modeling of the feature point deficient area.

Referring back to FIG. 2, the 3D modeling apparatus 100 may acquire an indoor image and at the same time acquire a point cloud for an indoor space by the lidar 112 (S120). To this end, the control unit 140 of the 3D modeling apparatus 100 may pan and/or tilt the lidar 112 to irradiate a laser to the entire area of the indoor space.

The lidar 112 may obtain a point cloud by receiving a result of the irradiated laser reflected from the indoor space and placing it on the 3D space. In addition, the lidar 112 may receive a scanned image from the scan camera 111 of the 3D scanner 110 and add color information to the point cloud. The obtained point cloud may include shape, material, and/or color information about the indoor space.

Finally, the 3D modeling apparatus 100 may 3D model an indoor space based on a point cloud corresponding to the feature point deficient region and a plurality of indoor images (S130). Hereinafter, a method of 3D modeling based on a point cloud and an indoor image will be described with reference to FIGS. 8 to 11.

8 is a diagram illustrating a point cloud for a feature point deficient region according to an embodiment of the present invention, FIG. 9 is a diagram illustrating a 3D mesh for the feature point deficient region of FIG. 8, and FIG. 10 is a feature point of FIG. FIG. 11 is a diagram illustrating a second intermediate 3D model for the deficient region, and FIG. 11 is a diagram illustrating a 3D model in which the feature point deficient region in the first intermediate 3D model of FIG. 6 is replaced with the second intermediate 3D model of FIG. 10.

In order to perform 3D modeling, first, the 3D modeling apparatus 100 may check a point cloud corresponding to the previously determined feature point deficient region. Specifically, the image processing unit 130 of the 3D modeling apparatus 100 may match the point cloud obtained by the lidar 112 with a first intermediate 3D model generated based on a plurality of indoor images. Since each point data of the point cloud has 3D coordinates, the image processing unit 130 may match each point data of the point cloud on the first intermediate 3D model according to the 3D coordinates.

Then, the image processing unit 130 may check a point cloud within the feature point deficiency region of the plurality of matched indoor images. Referring to FIG. 8, the image processing unit 130 may check a point cloud existing in the feature point deficiency region S of FIG. 7.

When the point cloud in the feature point deficiency region is identified, the image processing unit 130 may generate a second intermediate 3D model using the identified point cloud. To this end, the image processing unit 130 may generate a 3D mesh in which a plurality of vertices are rigged by the bone by first using point data of the point cloud in the feature point deficient region S as a bone and a vertex. Referring to FIG. 9, the image processing unit 130 may generate a 3D mesh based on a point cloud existing in the feature point deficiency region S of FIG. 8. Then, the image processing unit 130 may generate a second intermediate 3D model by performing texturing on the 3D mesh for the region lacking the feature point. Referring to FIG. 10, the image processing unit 130 may generate a second intermediate 3D model by performing texturing on a 3D mesh existing in the feature point deficient region S of FIG. 9.

Finally, the image processing unit 130 may replace a region lacking a feature point among the first intermediate 3D model with a second intermediate 3D model. As a result, the image processing unit 130 may obtain a 3D model for the indoor image as shown in FIG. 11.

Compared with FIG. 6, it can be seen that the 3D model of FIG. 11 has an upper area similar to an actual indoor space. As a result, the user can feel more about the 3D model of FIG. 11. In addition, since 3D modeling is not performed using all the point clouds acquired for the entire area of the indoor space, the amount of computation for 3D modeling may be reduced.

Until now, after acquiring point clouds for all areas of an indoor space, an embodiment of selectively using a point cloud in an area lacking a feature point has been described. Unlike this, after selectively acquiring a point cloud for a region lacking a feature point in an indoor space, 3D modeling may be performed based on this. Hereinafter, a method of selectively acquiring a point cloud and performing 3D modeling will be described with reference to FIGS. 12 to 13.

12 is a flowchart of a 3D modeling method according to another embodiment of the present invention, and FIG. 13 is a diagram illustrating a 3D scanner according to the embodiment of FIG. 1.

First, the 3D modeling apparatus 100 may acquire a scan image of an indoor space by the 3D scanner 110 (S200). Here, the scan image is an image acquired by the scan camera 111 of the 3D scanner 110 and may have a lower resolution than the image acquired by the camera 120 of the 3D modeling apparatus 100.

Then, the 3D modeling apparatus 100 may determine a feature point deficiency region from the acquired scan image (S210). To this end, the image processing unit 130 of the 3D modeling apparatus 100 extracts a feature point from the obtained scanned image, adds depth information to the extracted feature point by applying a triangulation method, etc., and then applies the feature point to which the depth information is added. It can be placed in a three-dimensional space. The image processing unit 130 may check the number of feature points arranged in the 3D space within the unit area. When there is an area in which the identified number is equal to or less than the predetermined reference number, the image processing unit 130 may determine the area as a feature point deficient area. Here, the reference number may mean the maximum number of feature points that are insufficient for 3D modeling.

When the feature point deficient region is determined, the 3D modeling apparatus 100 may determine a laser irradiation angle of the 3D scanner 110 corresponding to the feature point deficient region (S220). Specifically, the control unit 140 of the 3D modeling apparatus 100 may determine a panning angle and/or a tilting angle at which the lidar 112 of the 3D scanner 110 can irradiate a laser to the feature point deficient region.

Referring to FIG. 13, the lidar 112 may irradiate a laser L going straight. Accordingly, the controller 140 may change the panning angle and/or the tilting angle of the lidar 112 so that the laser L reaches the feature point deficient region.

When the laser irradiation angle is determined, the 3D modeling apparatus 100 may obtain a point cloud corresponding to the feature point deficient region by irradiating the laser at the determined irradiation angle (S230). In this case, the acquired point cloud may include only information on a region lacking a feature point in the indoor space.

At the same time as the scan image is acquired by the 3D scanner 110, the 3D modeling apparatus 100 may acquire a plurality of indoor images of the indoor space by the camera 120 (S240 ). Each of the plurality of indoor images obtained in this way may overlap at least a portion with at least one other indoor image.

Finally, the 3D modeling apparatus 100 may 3D model an indoor space based on the acquired point cloud and a plurality of indoor images (S250). To this end, first, the image processing unit 130 of the 3D modeling apparatus 100 extracts a feature point from the obtained indoor image, adds depth information to the extracted feature point by applying a triangulation method, etc., and then the feature point to which the depth information is added. Can be placed in a three-dimensional space. Then, after matching the feature point and the point cloud, the image processing unit 130 may perform 3D modeling using the matching result.

When acquiring the 3D model obtained according to the method of FIG. 12, the load of acquiring the point cloud may be reduced. In particular, since laser irradiation can be performed only on an area in which point cloud acquisition is required without laser irradiation on the entire area of an indoor space, the time required for acquiring a point cloud can be shortened.

The 3D modeling apparatus and method according to the various embodiments described above use together an indoor image acquired by a camera and a point cloud acquired by a lidar, thereby obtaining a high-quality 3D model. In addition, since the point cloud obtained by the lidar is used only for the region lacking the feature point among the images of the indoor space, the computational amount of 3D modeling may be reduced compared to the case of using the point cloud for the entire indoor space.

Meanwhile, each step included in the 3D modeling method according to the above-described embodiment may be implemented in a computer program programmed to perform such a step or a computer-readable recording medium recording a computer program.

The above description is merely illustrative of the technical idea of the present invention, and those of ordinary skill in the art to which the present invention pertains will be able to make various modifications and variations without departing from the essential quality of the present invention. Accordingly, the embodiments disclosed in the present invention are not intended to limit the technical idea of the present invention, but to explain the technical idea, and the scope of the technical idea of the present invention is not limited by these embodiments. The scope of protection of the present invention should be interpreted by the following claims, and all technical ideas within the scope equivalent thereto should be construed as being included in the scope of the present invention.

According to an embodiment, the 3D modeling apparatus and method described above can be used in various fields such as indoors or industrial sites, and thus has industrial applicability.

100: 3D modeling device
110: 3D scanner
111: scan camera
112: Lida
120: camera
130: image processing unit
140: control unit

Claims (12)

In the 3D modeling method performed by the 3D modeling device,
Acquiring a point cloud for an indoor space using a lidar included in the 3D modeling device;
Acquiring a plurality of indoor images of the indoor space by a camera included in the 3D modeling apparatus;
Determining a feature point deficient region in the indoor space from the plurality of indoor images; And
3D modeling the indoor space by using a point cloud for the feature point deficient region for the feature point deficient region, and using the plurality of indoor images in an area other than the feature point deficient region among the indoor spaces.
3D modeling method. The method of claim 1,
The step of determining the feature point deficient region comprises:
Extracting feature points from each of the plurality of indoor images; And
Including the step of determining an area in which the number of extracted feature points is less than or equal to a reference number as the feature point deficient area
3D modeling method. The method of claim 1,
The step of determining the feature point deficient region comprises:
Extracting feature points from each of the plurality of indoor images;
Generating a 3D mesh based on the extracted feature points; And
Including the step of determining a region formed by a polygon having an area greater than or equal to a reference area among the plurality of polygons constituting the 3D mesh as the feature point deficient area
3D modeling method. The method of claim 1,
The step of determining the feature point deficient region comprises:
Extracting feature points from each of the plurality of indoor images;
Generating a 3D mesh based on the extracted feature points;
Generating a first intermediate 3D model by performing texturing on the generated 3D mesh
3D modeling method. The method of claim 4,
The step of determining the feature point deficient region comprises:
Determining the untextured region of the first intermediate 3D model as the feature point deficient region, or determining a region selected by a user input as the feature point deficient region
3D modeling method. The method of claim 4,
The step of 3D modeling the indoor space,
Generating a second intermediate 3D model based on a point cloud corresponding to the feature point deficient region; And
Replacing the feature point deficient region of the first intermediate 3D model with the second intermediate 3D model
3D modeling method. The method of claim 6,
Generating the second intermediate 3D model,
Matching the point cloud acquired by the lidar with the first intermediate 3D model generated based on the plurality of indoor images;
Checking a point cloud in the feature point deficient region of the matched first intermediate 3D model; And
Including the step of generating the second intermediate 3D model using the identified point cloud
3D modeling method. In the 3D modeling method performed by the 3D modeling device,
Obtaining a scan image of an indoor space by a 3D scanner included in the 3D modeling apparatus;
Determining a feature point deficient region in the indoor space from the obtained scanned image;
Acquiring a point cloud for the feature point deficient region by the 3D scanner;
Acquiring a plurality of indoor images of the indoor space by a camera included in the 3D modeling apparatus; And
3D modeling the indoor space by using the point cloud for the feature point deficient region, and using the plurality of indoor images in the indoor space that is not the feature point deficient region.
3D modeling method. The method of claim 8,
Acquiring a point cloud corresponding to the feature point deficient region,
Determining a laser irradiation angle of the 3D scanner corresponding to the feature point deficient region; And
Including the step of obtaining a point cloud corresponding to the feature point deficient region by irradiating the laser at the determined irradiation angle
3D modeling method. ◈ Claim 10 was abandoned upon payment of the set registration fee. Lida to acquire a point cloud for an indoor space;
A camera that acquires a plurality of indoor images of the indoor space; And
An image processing unit configured to determine a feature point deficient region in the indoor space from the plurality of indoor images,
The image processing unit,
The feature point deficiency region uses a point cloud for the feature point deficiency region, and a region of the indoor space that is not the feature point deficiency region uses the plurality of indoor images, thereby 3D modeling the indoor space.
3D modeling device. As a computer program stored in a computer-readable recording medium,
The computer program,
Comprising instructions for causing a processor to perform a method according to any one of claims 1 to 9,
Computer program. ◈ Claim 12 was abandoned upon payment of the set registration fee. As a computer-readable recording medium storing a computer program,
The computer program,
A computer-readable recording medium comprising instructions for causing a processor to perform the method according to any one of claims 1 to 9.

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