KR102543032B1 - Method and system for displaying point group data using Mobile Mapping apparatus - Google Patents

Abstract

The present invention is a mobile 3D imaging device capable of expressing 3D large-capacity point cloud data on a 3D screen of a computer without delay and expressing the entire area of captured data by excluding point cloud data that is not visible from the user's field of view. It relates to a point cloud data representation method and system using, (a) acquiring three-dimensional point cloud data using a mobile 3-dimensional imaging device such as a vehicle or an aircraft in real space; (b) dividing the space by layering into 3D level blocks from the original level block to the lowest level block according to the distance from the 3D imaging device in the virtual space made up of the point group data; (c) storing location information of the point group data included in each of the 3D level blocks; (d) A point to be expressed by adjusting the expression level from the 3D level block corresponding to the highest parent node of the closest object to the 3D level block corresponding to the lowest child node of the farthest object from the point of view of the virtual observer determining the number of; and (e) selectively reading as many as the number of determined points of point group data or 3D data based thereon from the highest parent node to the lowest child node that may overlap with the highest parent node, and overlapping them on the screen. Expressing a 3D object in a specific frame; may include.

Description

Method and system for displaying point group data using Mobile Mapping apparatus}

The present invention relates to a method and system for expressing point cloud data using a mobile 3D imaging device, and more particularly, to a method of excluding point cloud data that is invisible from the user's field of view, to generate 3D large-capacity point cloud data on a computer without delay. The present invention relates to a method and system for representing point cloud data using a movable 3D imaging device capable of expressing the entire region of captured data on a 3D screen.

In general, large-capacity 3D point cloud data is used in computer graphics that require a view from the observer's point of view, map production with precision, highway safety diagnosis, change detection of road facilities, virtual reality, etc. Conventionally, road point cloud sections with a specific file size The method of dividing and saving and reading and expressing each file is widely used.

In particular, in order to express the point cloud data in which the shape of a road facility is formed in the form of a 3D point cloud by laser reflection among the data acquired by the mobile laser surveying equipment MMS (Mobile Mapping System) for a vehicle, a large-capacity memory device is needed

Generally, 3D point cloud data is a large capacity of more than 100 gigabytes depending on the shooting distance, so when expressing it on a computer screen, the data is divided by the amount of data that can be accommodated in memory, and only the part corresponding to the user's point of view is read and expressed. There was no way to recognize the space other than the divided data, and if the computer reads a large number of sections, it may exceed the system memory limit or the expression performance may significantly deteriorate, so when reading multiple data, the computer memory or processing speed Loading beyond the limit of is impossible, resulting in delays, or when a large number of data is loaded, there are problems such as greatly deteriorating screen representation performance.

The present invention is to solve various problems, including the above problems, and to generate relatively less dense point clouds by generating less dense point cloud data step by step from dense large-capacity 3D point cloud data. By hierarchically linking a dense point group in a parent to a child, when saving data as a structured file, processing a method for dividing and storing data into specific blocks, and when reading and expressing stored data, points of a specific density to be expressed with a 3D camera By selectively reading cloud data, spatial segmentation techniques are used for large-capacity point cloud data to hierarchically structure data and distribute data according to the observer's field of view to reduce the visual range and memory usage of large-capacity point cloud data. An object of the present invention is to provide a point cloud data expression method and system using a mobile 3D imaging device that can improve efficiency and expression performance (speed). However, these tasks are illustrative, and the scope of the present invention is not limited thereby.

A method of representing point cloud data using a mobile 3D imaging device according to the spirit of the present invention for solving the above problems is: (a) 3D point group data using a mobile 3D imaging device such as a vehicle or an aircraft in real space obtaining; (b) dividing the space by layering into 3D level blocks from the original level block to the lowest level block according to the distance from the 3D imaging device in the virtual space made up of the point cloud data; (c) storing location information of the point group data included in each of the 3D level blocks; (d) A point to be expressed by adjusting the expression level from the 3D level block corresponding to the highest parent node of the closest object to the 3D level block corresponding to the lowest child node of the farthest object from the point of view of the virtual observer determining the number of; and (e) selectively reading as many as the number of determined points of point group data or 3D data based thereon from the highest parent node to the lowest child node that may overlap with the highest parent node, and overlapping them on the screen. Expressing a 3D object in a specific frame; may include.

In addition, according to the present invention, in step (d), the number of points expressed from the highest parent node to the lowest child node may be reduced step by step.

Further, according to the present invention, the topmost parent node is formed in a rectangular shape as a whole, and the specific topmost parent node may overlap four lower child nodes divided into four equal parts.

In addition, according to the present invention, the sum of the number of dots corresponding to the top parent node and the number of dots of the four lower child nodes is equal to each other so that the resolution of all parts in a specific frame on the screen can be constant. can do.

In addition, according to the present invention, a plurality of sub-nodes may be derived and generated from the top-level parent node, and a plurality of sub-nodes may be derived and overlapped with a specific sub-node.

Also, according to the present invention, the point group of the highest parent node may have a first density, and the point group of one of the lower child nodes corresponding thereto may have a second density lower than the first density.

Also, according to the present invention, the second density may be 1/4 lower than the first density.

Also, according to the present invention, the 3D level block may be a regular hexahedron-shaped area as a whole.

Also, according to the present invention, the 3D imaging device is a laser surveying device of a Mobile Mapping System (MMS), and the point group data may be formed by laser reflection.

Further, according to the present invention, the step (d) may include: (d-1) calculating a distance value from the position of the observer's viewpoint to the center position of the 3D level block; (d-2) calculating an LOD value by multiplying a diagonal length value of the 3D level block by an LOD ratio; (d-3) if the distance value is less than the LOD value, stopping after setting the point density level of the corresponding node; and (d-4) if the distance value is equal to or greater than the LOD value, doubling the LOD value.

In addition, according to the present invention, in the step (d-2), if the LOD ratio increases, the point density seen from the same distance may increase, and if the LOD ratio decreases, the point density may decrease.

On the other hand, a point cloud data expression system using a mobile 3D imaging device according to the idea of the present invention for solving the above problems is a three-dimensional point cloud data using a mobile 3D imaging device such as a vehicle or an aircraft in real space. a point group data acquisition unit to acquire; a space divider for classifying a space into 3D level blocks from an original level block to a lowest level block according to a distance from the 3D imaging device in a virtual space composed of the point cloud data; a location information storage unit that stores location information of the point group data included in each of the 3D level blocks; The number of points to be expressed by adjusting the expression level from the 3D level block corresponding to the highest parent node of the object closest to the viewpoint of the virtual observer to the 3D level block corresponding to the lowest child node of the farthest object a point number determination unit to determine; and selectively reading only as many as the number of determined points of point group data or 3D data based thereon from the highest parent node to the lowest child node that may overlap with the highest parent node, overlapping them, and displaying them in a specific frame on the screen. A node overlapping expression unit representing a 3D object may be included.

In addition, according to the present invention, the point number determining unit may include: a distance value calculating unit calculating a distance value from the position of the viewpoint of the observer to the central position of the 3D level block; an LOD value calculation unit that calculates an LOD value by multiplying a diagonal length value of the 3D level block by an LOD ratio; a point density level setting unit that sets the point density level of the corresponding node and then stops it if the distance value is smaller than the LOD value; and an LOD value increasing unit that doubles the LOD value when the distance value is greater than or equal to the LOD value.

According to some embodiments of the present invention made as described above, by generating less dense point cloud data step by step from dense large-capacity 3D point cloud data, a relatively less dense point cloud is mapped to a parent dense point cloud. By hierarchically connecting to children, when saving data as a structured file, processing a method for dividing and storing data into specific blocks, and selectively reading 3D camera and point cloud data of a specific density to be expressed when reading and expressing stored data. , structure the data hierarchically using spatial segmentation techniques for large-capacity point cloud data and distribute the data according to the observer's field of view to obtain the visible range of large-capacity point cloud data, the efficiency of memory use, and the performance (speed) of expression. It can be improved up to, and the image can be expressed naturally without interruption on a general personal computer, user convenience is provided through movement through natural 3D screen manipulation, and the same screen as reality can be provided at the point where the car moves on the road. Through this, it has the effect of reproducing data related to roads, such as precision road maps. Of course, the scope of the present invention is not limited by these effects.

1 is a flowchart illustrating a method of expressing point cloud data using a mobile 3D imaging device according to some embodiments of the present invention.
FIG. 2 is a flowchart illustrating step (d) of the point cloud data representation method using the mobile 3D imaging device of FIG. 1 in more detail.
3 is a block diagram illustrating a point cloud data representation system using a mobile 3D imaging device according to some embodiments of the present invention.
FIG. 4 is a block diagram showing a point number determination unit of the point cloud data representation system using the mobile 3D imaging device of FIG. 1 in more detail.
FIG. 5 is diagrams showing the form of large-capacity point cloud data acquired by the point cloud data acquisition unit of the point cloud data representation system using the mobile 3D imaging device of FIG. 2 .
FIG. 6 is a flowchart illustrating a process of generating level blocks in the spatial division unit of the point cloud data representation system using the mobile 3D imaging device of FIG. 2 .
7 to 9 are diagrams illustrating block division and hierarchical structure of 3D point cloud data in the point number determining unit of the point cloud data representation system using the mobile 3D imaging device of FIG. 2 .
FIG. 10 is a diagram illustrating blocks divided when expressing an actual 3D point cloud object in the node overlapping representation unit of the point cloud data representation system using the mobile 3D imaging device of FIG. 2 .
FIG. 11 is a flowchart illustrating a data loading process according to the movement of the field of view in the node overlapping expression unit by the point number determination unit of the point group data representation system using the mobile 3D imaging device of FIG. 2 .
FIG. 12 shows the process of recursively overlapping and calling data from the upper level to the lower level in the node overlapping representation unit of the point cloud data representation system using the mobile 3D imaging device of FIG. 2 from (1) to (6). It is a conceptual diagram in order.
FIG. 13 is a diagram illustrating an example of blocks excluded by a 3D space division of the entire block structure and a field of view in the node overlapping representation unit of the point group data representation system using the mobile 3D imaging device of FIG. 2 .
FIG. 14 is a diagram illustrating the expression of a hierarchically structured large-capacity point cloud responding to a 3D view in the node overlapping representation unit of the point cloud data representation system using the mobile 3D imaging device of FIG. 2 .

Hereinafter, several preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

The embodiments of the present invention are provided to more completely explain the present invention to those skilled in the art, and the following examples may be modified in many different forms, and the scope of the present invention is as follows It is not limited to the examples. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the spirit of the invention to those skilled in the art. In addition, the thickness or size of each layer in the drawings is exaggerated for convenience and clarity of explanation.

Hereinafter, embodiments of the present invention will be described with reference to drawings schematically showing ideal embodiments of the present invention. In the drawings, variations of the depicted shape may be expected, depending on, for example, manufacturing techniques and/or tolerances. Therefore, embodiments of the inventive concept should not be construed as being limited to the specific shape of the region shown in this specification, but should include, for example, a change in shape caused by manufacturing.

1 is a flowchart illustrating a method of expressing point cloud data using a mobile 3D imaging device according to some embodiments of the present invention.

First, as shown in FIG. 1, a method of representing point group data using a mobile 3D imaging device according to some embodiments of the present invention is (a) using a mobile 3D imaging device such as a vehicle or an aircraft in real space. (b) obtaining 3-dimensional point cloud data, and (b) converting the original level block to the lowest level block into 3-dimensional level blocks according to the distance from the 3-dimensional imager in a virtual space composed of the point cloud data. (c) storing location information of the point group data included in each of the 3D level blocks; (d) the topmost parent node of the object closest to the viewpoint of the virtual observer Determining the number of points to be expressed by adjusting expression levels from the 3D level block corresponding to the 3D level block to the 3D level block corresponding to the lowest child node of the farthest object; Expressing a 3D object in a specific frame on the screen by selectively reading the point group data or 3D data based thereon as much as the number of determined points up to the lowest child node that may overlap with the highest parent node and overlapping them can include

Here, for example, the 3D level block is a cube-shaped area as a whole, the 3D imaging device is a laser surveying device of MMS (Mobile Mapping System), and the point group data is in the form of a point formed by laser reflection. can be data.

Specifically, for example, in the step (d), the object close to the observer is expressed relatively microscopically, and the object far from the observer is expressed relatively macroscopically, from the highest parent node to the lowest child node. The number of dots can be reduced step by step.

In addition, for example, the uppermost parent node is formed in a rectangular shape as a whole so that the resolution expressed in all parts of the screen of an object near or far from the screen is uniform without partially falling, and the specific highest parent node, Can be nested with 4 lower child nodes divided into 4 parts of the same size.

That is, the sum of the number of dots corresponding to the uppermost parent node and the number of dots of the four lower child nodes may be equal to each other so that the resolution of all parts in a specific frame on the screen may be constant.

Accordingly, a plurality of sub-nodes may be derived and generated from the top-level parent node, and a plurality of sub-nodes may be derived and overlapped with a specific sub-node.

In addition, a point group of the uppermost parent node may have a first density, and a point group of one of the lower child nodes corresponding thereto may have a second density lower than the first density.

Here, as described above, the second density may be set to be lower than the first density by 1/4 so that resolutions expressed in all parts of the screen of objects close to or far from the screen are uniform without partially falling. .

FIG. 2 is a flowchart illustrating step (d) of the point cloud data representation method using the mobile 3D imaging device of FIG. 1 in more detail.

More specifically, for example, as shown in FIG. 2, the step (d) includes (d-1) calculating a distance value from the position of the observer's viewpoint to the center position of the 3D level block; , (d-2) calculating an LOD value by multiplying a diagonal length value of the 3D level block by an LOD ratio; (d-3) if the distance value is smaller than the LOD value, the point density level of the corresponding node and (d-4) doubling the LOD value if the distance value is greater than or equal to the LOD value.

Here, in the step (d-2), if the LOD ratio increases, the point density seen from the same distance may increase, and if the LOD ratio decreases, the point density may decrease.

As an example, an example of a pseudocode of a relationship between a distance and a point density implementing the above step (d) is as follows.

set point density() {

Get the distance between the camera's position and the center value of the "Point Block".

Calculate the LOD value (diagonal length of "Point Block" * "LOD Ratio")

For (LOD max level) do {

If (if the distance is smaller than the LOD value), stop after setting the corresponding point density level

Else double the LOD value

}

}

(reference)

"LOD Ratio"

: As the value increases, the "dot density" seen at the same distance increases, and as it decreases, the "dot density" also decreases.

Therefore, if the facility monitoring method using the 3D virtual model of the present invention is used, an object close to the observer can be expressed relatively microscopically, an object far from the observer can be expressed relatively macroscopically, and an object close to the screen can be expressed. For all distant objects, the resolution expressed in all parts of the screen is uniform rather than partially degraded, so that the overall screen quality is maintained and the amount of data read by the computer can be drastically reduced, so there is no delay or interruption. It can continuously provide very smooth images.

Therefore, according to the present invention, by generating less dense point cloud data step by step from dense large-capacity three-dimensional point cloud data and hierarchically connecting relatively less dense point clouds to parents and dense point clouds to children. When saving as a structured file, processing the method for dividing into specific blocks and storing them, and selectively reading point cloud data of a specific density to be expressed with a 3D camera when reading and expressing the stored data. It is possible to hierarchically structure data using spatial segmentation techniques and distribute data according to the observer's field of view to improve the visible range of large-capacity point cloud data, efficiency of memory use, and expression performance (speed).

3 is a block diagram illustrating a point cloud data representation system 100 using a mobile 3D imaging device according to some embodiments of the present invention.

As shown in FIG. 3, the point cloud data representation system 100 using a mobile 3D imaging device according to some embodiments of the present invention uses a mobile 3D imaging device such as a vehicle or an aircraft in real space to capture 3D images. A 3D level from the original level block to the lowest level block according to the distance between the point group data acquisition unit 10 that acquires dimensional point cloud data and the 3D imaging device in a virtual space composed of the point group data. A space divider 20 that divides space by layering into blocks; a position information storage unit 30 that stores position information of the point group data included in each of the 3D level blocks; A point number determination unit for determining the number of points to be expressed by adjusting expression levels from the 3D level block corresponding to the highest parent node of the nearest object to the 3D level block corresponding to the lowest child node of the farthest object (40) Selectively read only as many points as the number of determined points of point group data or 3D data based thereon from the highest parent node to the lowest child node that may overlap with the highest parent node, and overlap them on the screen. A node overlapping expression unit 50 that expresses a 3D object in a specific frame may be included.

Here, the point group data acquisition unit 10, the space division unit 20, the location information storage unit 30, the point number determination unit 40, or the node overlapping expression unit 50 ) is applied to electronic devices such as various control devices, microprocessors, semiconductor chips, integrated circuits, circuit boards, arithmetic devices, central processing units, and program-stored storage devices included in computers, smartphones, smart pads, and smart terminals. can

FIG. 4 is a block diagram showing the point number determining unit 40 of the point cloud data representation system 100 using the mobile 3D imaging device of FIG. 1 in more detail.

More specifically, for example, the point number determining unit 40 includes a distance value calculating unit 41 that calculates a distance value from the viewpoint position of the observer to the central position of the 3D level block, and the 3D level block. An LOD value calculation unit 42 that calculates an LOD value by multiplying a diagonal length value of a level block by an LOD ratio, and if the distance value is smaller than the LOD value, set the point density level of the corresponding node and then stop it A unit 43 and an LOD value increasing unit 44 that doubles the LOD value when the distance value is greater than or equal to the LOD value.

Therefore, even in a general personal computer, images can be naturally expressed without interruption, user convenience is provided through movement through natural 3D screen manipulation, and the same screen as reality can be implemented at the point of time when a vehicle moves on the road.

FIG. 5 is diagrams showing the form of large-capacity point cloud data acquired by the point cloud data acquisition unit 10 of the point cloud data representation system 100 using the mobile 3D imaging device of FIG. 2 .

For example, as shown in the left picture of FIG. 5, the point cloud data may include RGB color information, and as shown in the middle picture of FIG. , z) 3-dimensional coordinates and (r, g, b) colors and reflection values of light, and as shown in the right picture of FIG. can be accurately measured.

FIG. 6 is a flowchart illustrating a process of generating level blocks in the spatial divider 20 of the point cloud data representation system 100 using the mobile 3D imaging device of FIG. 2 .

As shown in FIG. 6 , for example, when data is input to store point group data in a hierarchical structure, a block size may be set, and a hierarchical level may be calculated to generate a level block. Here, as many points as the number of blocks may be stored, and such block files may be created as many as the number of blocks. At this time, such point cloud data is stored in units of regular hexahedral blocks, and point clouds included in the block may be stored at the lowest level of the original point cloud.

7 to 9 are diagrams showing block division and hierarchical structure of 3D point cloud data in the point number determining unit 40 of the point cloud data representation system 100 using the mobile 3D imaging device of FIG. 2 .

As shown in FIGS. 7 to 9, the level of detail of the point group is loosened to 1/4 as the level goes up to the upper level, so that the number of points inside the block at the top level and the bottom level is the same can be maintained, and the size of 8 lower-level blocks can be the upper-level block size. That is, it can be seen that the number of blocks per hierarchical level increases as the level increases.

Here, in order to represent a large-capacity point cloud, a hierarchical structure can be constructed in advance by dividing the large-capacity point cloud into blocks, and the block included in the top parent node in the large-capacity point cloud hierarchy is initially read into the computer It can always reside in memory.

FIG. 10 is a diagram illustrating blocks divided when expressing an actual 3D point cloud object in the node overlapping representation unit 50 of the point cloud data representation system 100 using the mobile 3D imaging device of FIG. 2 .

As shown in FIG. 10, visualization is performed with child nodes derived from a parent node, and the structure of the highest parent node and the block structure of the lowest child node may be the same.

FIG. 11 is a flowchart illustrating a data loading process according to the movement of the field of view in the node overlapping expression unit 50 by the point number determining unit 40 of the point group data representation system 100 using the mobile 3D imaging device of FIG. 2. .

As shown in FIG. 11, the node structure is data of a predetermined area obtained by dividing a group of points into squares of a specific size, and the expression level of the block is determined according to the viewpoint of the observer (camera), and the number of points expressed inside the block may depend on

That is, if a block node is input to the top node, the detail can be determined by the distance between the field of view and the block. At this time, for example, if the LOD is less than 2, 8 child nodes are read and connected to the parent node and the adjacent node, otherwise block loading for the view may be completed.

FIG. 12 shows the process of recursively overlapping and calling data from the upper level to the lower level in the node overlapping representation unit 50 of the point cloud data representation system 100 using the mobile 3D imaging device of FIG. 2 (1) It is a conceptual diagram shown in order from to (6).

As shown in FIG. 12 , ownership relationships between parent nodes and child nodes are maintained through inheritance, and correlations between nodes at the same level or neighboring nodes between parent and child nodes can be logically maintained.

That is, when child nodes are continuously created by inheriting parent nodes in the order of t = 0 to t = 1, t = m, t = m + 1, t = n, t = q in FIG. 12, the observer's point of view and The detail of the child node is determined by considering the relationship of the point cloud, and large-capacity point clouds can implement real-time representation by overlapping with a recursive method of recreating the child node according to the degree of detail.

FIG. 13 is a diagram showing an example of blocks excluded by the 3D spatial division of the entire block structure and the field of view in the node overlapping representation unit 50 of the point cloud data representation system 100 using the mobile 3D imaging device of FIG. 2 .

That is, as shown in FIG. 13, the part close to the field of view is divided and loaded, and the part far from the field of view maintains the upper level with a loose point group point density, so that memory can be saved by recognizing it as if actual dense data is expressed. As possible, the number of blocks and the number of internal point groups expressed in one frame can be drastically reduced by excluding the part excluded from the observer's field of view box in the hierarchical data structure from the graphic representation.

FIG. 14 is a diagram illustrating the expression of a hierarchically structured large-capacity point cloud responding to a 3D view in the node overlapping expression unit 50 of the point cloud data representation system 100 using the mobile 3D imaging device of FIG. 2 .

As shown in FIG. 14, in the network service for a large-capacity point cloud, the method of transmitting the entire large-capacity to the network has the disadvantage that the user has to wait until the download is completed, but it is divided into blocks such as the top level, middle level, and bottom level. It is possible to provide services by sequentially increasing the point density by hierarchical structure, and the divided data can be optimized by minimizing the number of expression point groups in response to the 3D view and maximizing space utilization.

Meanwhile, the present invention can also be implemented as computer readable codes in a computer readable recording medium.

A computer-readable recording medium may include all types of recording devices storing data that can be read by a computer system.

Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, magneto-optical disk, optical data storage device, flash memory, USB memory, etc., as well as a server computer. It may also include what is implemented in the form of a wave (for example, transmission over the Internet).

In addition, the computer-readable recording medium is distributed to computer systems connected through a network, so that computer-readable codes can be stored and executed in a distributed manner.

Although the present invention has been described with reference to the embodiments shown in the drawings, this is merely exemplary, and those skilled in the art will understand that various modifications and equivalent other embodiments are possible therefrom. Therefore, the true technical protection scope of the present invention should be determined by the technical spirit of the appended claims.

10: point cloud data acquisition unit
20: space dividing unit
30: location information storage unit
40: point number determination unit
41: distance value calculation unit
42: LOD value calculation unit
43: point density level setting unit
44: LOD value increasing unit
50: node overlap expression unit
100: Point cloud data expression system using a mobile 3-dimensional imaging device

Claims (13)

(a) acquiring 3-dimensional point cloud data using a mobile 3-dimensional imaging device in real space;
(b) dividing the space by layering into 3D level blocks from the original level block to the lowest level block according to the distance from the 3D imaging device in the virtual space made up of the point cloud data;
(c) storing location information of the point group data included in each of the 3D level blocks;
(d) A point to be expressed by adjusting the expression level from the 3D level block corresponding to the highest parent node of the closest object to the 3D level block corresponding to the lowest child node of the farthest object from the point of view of the virtual observer determining the number of; and
(e) Selectively read only as many points as the number of points determined from the point group data or 3D data based thereon from the topmost parent node to the lowest child node that may overlap with the topmost parent node, and overlap them to specify a specific value on the screen. Expressing a 3D object in a frame; including,
In step (d),
Step by step reducing the number of points expressed from the highest parent node to the lowest child node,
The uppermost parent node has a rectangular shape as a whole,
The specific top-level parent node overlaps four lower child nodes divided into 4 equal parts,
A mobile 3D imaging device in which the sum of the number of dots corresponding to the uppermost parent node and the total number of dots of the four lower child nodes is equal to each other so that the resolution of all parts in a specific frame on the screen is constant. Point cloud data representation method used. delete delete delete According to claim 1,
A method of expressing point cloud data using a mobile 3-dimensional imaging device in which a plurality of lower-order nodes are derived and overlapped at the specific lower-order child node. According to claim 1,
Point cloud data using a mobile 3D imaging device in which the point cloud of the uppermost parent node has a first density, and any one of the corresponding lower child nodes has a second density lower than the first density. expression method. According to claim 6,
A method of expressing point cloud data using a mobile three-dimensional imaging device in which the second density is lower than the first density by 1/4. According to claim 1,
The 3D level block is a cube-shaped area as a whole, a point cloud data representation method using a mobile 3D imaging device. According to claim 1,
The three-dimensional imaging device is a laser surveying device of a mobile mapping system (MMS), and the point cloud data is formed by laser reflection. According to claim 1,
In step (d),
(d-1) calculating a distance value from the position of the observer's viewpoint to the center position of the 3D level block;
(d-2) calculating an LOD value by multiplying a diagonal length value of the 3D level block by an LOD ratio;
(d-3) if the distance value is less than the LOD value, stopping after setting the point density level of the corresponding node; and
(d-4) if the distance value is greater than or equal to the LOD value, doubling the LOD value;
Point cloud data representation method using a mobile three-dimensional imaging device comprising a. According to claim 10,
In the step (d-2), if the LOD ratio increases, the point density seen at the same distance increases, and if the LOD ratio decreases, the point density decreases. a point cloud data acquisition unit that acquires three-dimensional point cloud data using a mobile 3D imaging device in real space;
a space divider for classifying a space into 3D level blocks from an original level block to a lowest level block according to a distance from the 3D imaging device in a virtual space composed of the point cloud data;
a location information storage unit that stores location information of the point cloud data included in each of the 3D level blocks;
The number of points to be expressed by adjusting the expression level from the 3D level block corresponding to the highest parent node of the object closest to the viewpoint of the virtual observer to the 3D level block corresponding to the lowest child node of the farthest object a point number determination unit to determine; and
Point cloud data or 3D data based on it from the topmost parent node to the lowest child node that can overlap with the topmost parent node is selectively read as many as the number of determined points, and overlapped to display 3 points in a specific frame on the screen. a node overlapping representation unit representing a dimensional object; including,
The point number determination unit,
Step by step reducing the number of points expressed from the highest parent node to the lowest child node,
The uppermost parent node has a rectangular shape as a whole,
The specific top-level parent node overlaps four lower child nodes divided into 4 equal parts,
A mobile 3D mobile 3D method that determines that the sum of the number of dots corresponding to the uppermost parent node and the number of dots of the four lower child nodes is equal to each other so that the resolution of all parts in a specific frame on the screen is constant. A point cloud data expression system using an imaging device. According to claim 12,
The point number determination unit,
a distance value calculation unit that calculates a distance value from the position of the observer's viewpoint to the center position of the 3D level block;
an LOD value calculation unit that calculates an LOD value by multiplying a diagonal length value of the 3D level block by an LOD ratio;
a point density level setting unit that sets the point density level of the corresponding node and then stops it if the distance value is smaller than the LOD value; and
an LOD value increaser doubling the LOD value if the distance value is greater than or equal to the LOD value;
Point cloud data representation system using a mobile three-dimensional imaging device including a.

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