KR102679734B1 - Electronic device that generates digital surface model - Google Patents

Abstract

The present invention discloses an electronic device for generating a digital elevation model. The electronic device that generates the digital elevation model identifies the coordinates (x1, y1) of the first pixel included in the image data for the first area, which is a partial area of the total capture area, and the coordinates (x1) of the first pixel , y1), identify possible z1 values including n values on the z-axis that the first pixel can have (where n is a natural number), and identify a first depth map corresponding to the first area. Based on a depth map, identify a first probability that each of the possible z1 values is correct, and based on the difference between each of the possible z1 values and a determined z2 value that is a value on the z axis of a second pixel, 2. Identify a probability, and based on the first probability and the second probability, identify a first z value that is the value on the z axis of the coordinates (x1, y1) of the first pixel, and the remainder of the total captured area. Using each of the plurality of pixels included in the area and each of the plurality of depth maps corresponding to the remaining area, the value on the z-axis for each of the plurality of pixels is identified, and the numerical elevation for the entire captured area Stores instructions to create a model.

Description

Electronic device that generates digital surface model}

The present invention relates to an electronic device that generates a digital elevation model, and relates to an electronic device that generates a digital elevation model using a depth map.

The content described in this section simply provides background information for this embodiment and does not constitute prior art.

In creating a digital elevation model, technology that generates a point cloud of an actual scene using a stereo camera or a sensor that recognizes three dimensions is widely used. However, because these data are point-based, it can be difficult to estimate continuous results.

Additionally, in the case of digital elevation models, because they only express the highest value in a specific plane coordinate, it is difficult to obtain accurate and continuous values using existing interpolation methods.

An object of the present invention is to provide an electronic device that generates a digital elevation model based on a depth map.

The objects of the present invention are not limited to the objects mentioned above, and other objects and advantages of the present invention that are not mentioned can be understood by the following description and will be more clearly understood by the examples of the present invention. Additionally, it will be readily apparent that the objects and advantages of the present invention can be realized by the means and combinations thereof indicated in the patent claims.

An electronic device for generating a digital elevation model according to an embodiment of the present invention includes a processor and a memory operatively connected to the processor, wherein the memory, when executed, causes the processor to determine a portion of the total captured area. Identify the coordinates (x1, y1) of the first pixel included in the image data for the first area, and based on the coordinates (x1, y1) of the first pixel, n that the first pixel may have. Identify possible z1 values including values on the z-axis (where n is a natural number), and based on a first depth map corresponding to the first region, each of the possible z1 values is the correct answer. Identify a first probability, and based on the difference between each of the possible z1 values and a determined z2 value that is a value on the z axis of a second pixel, identify a second probability, and determine the first probability and the second probability. Based on this, a first z value, which is a value on the z axis of the coordinates (x1, y1) of the first pixel, is identified, each of a plurality of pixels included in the remaining area of the total captured area, and each of the plurality of pixels corresponding to the remaining area Using each of the plurality of depth maps, a value on the z-axis for each of the plurality of pixels is identified, and instructions for generating the digital elevation model for the entire captured area are stored.

Additionally, the first probability includes first to nth depth map probabilities for each of the n values on the z-axis included in the possible z1 values.

Additionally, the instructions cause the processor to project each of the possible z1 values and the coordinates (x1, y1) of the first pixel onto the first depth map, and project each of the possible z1 values and the coordinates of the first pixel. Identifying a first depth value in the first depth map onto which (x1, y1) is projected, a first distance from a center point to each of the possible z1 values and coordinates (x1, y1) of the first pixel, A first error between first depth values is identified, and based on the first error, the first probability is identified.

In addition, the first probability includes first to nth depth map probabilities for each of the n values on the z-axis included in the possible z1 values, and the first probability is based on the first depth map. This is the probability that each of the n values on the z-axis included in the possible z1 values is the correct answer.

Additionally, the instructions cause the processor to identify the first probability based on the first error using a Gaussian distribution.

Additionally, the center point is location information of a photographing device identified based on location information of the first depth map.

Additionally, the instructions cause the processor to identify the second probability using a probability density function.

Additionally, the second probability includes first to nth comparison probabilities calculated based on the difference between the z2 value and each of the n values on the z-axis included in the possible z1 values.

In addition, the first probability includes first to nth depth map probabilities for each of the n values on the z-axis included in the possible z1 values, and the second probability is included in the possible z1 values. and first to nth comparison probabilities calculated based on differences between each of the n values on the z-axis and the z2 value, wherein the instructions are such that the processor, respectively, the possible z1 values, the first to nth comparison probabilities. The first z value is identified based on each of the depth map probabilities and each of the first to nth comparison probabilities.

The electronic device for generating a digital elevation model of the present invention can generate an accurate and continuous digital elevation model by generating a digital elevation model based on a depth map.

In addition, the electronic device for generating a digital elevation model of the present invention generates a digital elevation model based on a depth map, thereby generating a digital elevation model that can solve the problem of difficult height measurement when the height of the site changes suddenly. there is.

In addition to the above-described content, specific effects of the present invention are described below while explaining specific details for carrying out the invention.

1 is a diagram for explaining an electronic device according to an embodiment of the present invention.
FIG. 2 is a diagram for explaining the operation of the processor of FIG. 1.
FIG. 3 is a diagram for explaining step S100 of FIG. 2.
Figures 4 and 5 are diagrams for explaining step S200 of Figure 2.
FIG. 6 is a diagram for explaining step S300 of FIG. 2.
FIG. 7 is a diagram for explaining steps S301, S303, and S305 of FIG. 6.
FIG. 8 is a diagram for explaining step S307 of FIG. 6.
FIG. 9 is a diagram for explaining step S400 of FIG. 2.
FIG. 10 is a diagram for explaining step S500 of FIG. 2.
FIG. 11 is a diagram for explaining steps S600 and S700 of FIG. 2.
FIG. 12 is a diagram illustrating hardware implementation of an electronic device according to some embodiments of the present invention.

Terms or words used in this specification and patent claims should not be construed as limited to their general or dictionary meaning. According to the principle that the inventor can define the term or word concept in order to explain his or her invention in the best way, it should be interpreted with a meaning and concept consistent with the technical idea of the present invention. In addition, the embodiments described in this specification and the configurations shown in the drawings are only one embodiment of the present invention and do not completely represent the technical idea of the present invention, so they cannot be replaced at the time of filing the present application. It should be understood that there may be various equivalents, variations, and applicable examples.

Terms such as first, second, A, and B used in the present specification and claims may be used to describe various components, but the components should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from another. For example, a first component may be named a second component without departing from the scope of the present invention, and similarly, the second component may also be named a first component. The term 'and/or' includes any of a plurality of related stated items or a combination of a plurality of related stated items.

The terms used in the specification and claims are merely used to describe specific embodiments and are not intended to limit the invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, terms such as "include" or "have" should be understood as not precluding the existence or addition possibility of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification. .

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by a person of ordinary skill in the technical field to which the present invention pertains.

Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and unless explicitly defined in the present application, should not be interpreted in an ideal or excessively formal sense. No. Additionally, each configuration, process, process, or method included in each embodiment of the present invention may be shared within the scope of not being technically contradictory to each other.

Hereinafter, an electronic device (hereinafter referred to as an electronic device) that generates a digital elevation model according to an embodiment of the present invention will be described with reference to FIGS. 1 to 11.

1 is a diagram for explaining an electronic device according to an embodiment of the present invention. FIG. 2 is a diagram for explaining the operation of the processor of FIG. 1.

Referring to FIGS. 1 and 2 , the electronic device 100 according to an embodiment of the present invention may include a processor 110 and a memory 120.

One or more other components (eg, communication module) may be added to the electronic device 100. In some embodiments, some of these components may be implemented as a single integrated circuit.

The memory 120 may store various data used by at least one component (eg, processor 110) of the electronic device 100. Data may include, for example, input data or output data for software (e.g., a program) and instructions related thereto. Memory 120 may include volatile memory or non-volatile memory.

The memory 120 may store commands, information, or data related to the operation of components included in the electronic device 100. For example, memory 120 may store instructions that, when executed, enable processor 110 to perform various operations described in this document.

The processor 110 may be operatively coupled with the memory 120 to perform the overall function of the electronic device 100. Processor 110 may include, for example, one or more processors. The one or more processors may include, for example, an image signal processor (ISP), an application processor (AP), or a communication processor (CP).

The processor 110 may, for example, execute software (e.g., a program) to control at least one other component (e.g., a hardware or software component) of the electronic device 100 connected to the processor 110. and can perform various data processing or calculations. According to one embodiment, as at least part of data processing or computation, the processor 110 loads commands or data received from another component (e.g., a communication module) into the memory 120 and stores the commands or data stored in the memory 120. Commands or data can be processed and the resulting data can be stored in the memory 120. According to one embodiment, the processor 110 includes a main processor (e.g., a central processing unit or an application processor) and an auxiliary processor that can operate independently or together with the main processor (e.g., a graphics processing unit, an image signal processor, a sensor hub processor, or communication processor). Additionally or alternatively, a coprocessor may be configured to use less power than the main processor or to specialize in designated functions. The auxiliary processor may be implemented separately from the main processor or as part of it. Programs may be stored in the memory 120 as software and may include, for example, an operating system, middleware, or applications.

In order to generate a digital elevation model, the processor 110 may first identify the coordinates of at least one pixel in image data of a partial area of the subject area. The processor 110 may identify the coordinates (x1, y1) of the first pixel included in the first area (S100).

FIG. 3 is a diagram for explaining step S100 of FIG. 2.

Referring to FIGS. 1, 2, and 3, the processor 110 may identify the coordinates (x1, y1) of the first pixel (PX1) in the image data corresponding to the first region (R1). The total imaging area (WA) can be mapped to include the x-axis and y-axis. The x and y axes can be perpendicular to each other. The first area R1 may be a partial area of the total imaging area WA. The first area R1 may be an area to be captured once when the imaging device is located on the entire capturing area WA. The total imaging area WA may include a plurality of areas including the first area R1 and the second area R2. Although it is shown in the drawing that the first area R1 and the second area R2 do not overlap each other, the present invention is not limited thereto. For example, the first area R1 and the second area R2 may partially overlap. The first area R1 may include a first pixel PX1 and a second pixel PX2. The processor 110 may further identify the coordinates (x2, y2) of the second pixel (PX2).

Referring again to FIGS. 1 and 2, the processor 110 may identify possible z values that the first pixel (PX1 in FIG. 3) may have (S200).

Figures 4 and 5 are diagrams for explaining step S200 of Figure 2.

Referring to FIGS. 1, 2, 4, and 5, the processor 110 calculates the Possible z values can be identified. The z-axis may be an axis perpendicular to the x-axis and the y-axis. The possible z values of the first pixel PX1 may be n values on the z-axis that the first pixel PX1 can have on the z-axis (where n is a natural number). For example, the first pixel (PX1) is z1, z2,... , zn can have possible z values. If this is expressed in coordinates, it is as shown in FIG. 5.

The possible z values that the first pixel (PX1) can have on the z-axis exist at the actual shooting site of the image data corresponding to the first pixel (PX1) in the first area (R1), for example, based on the ground. It can be the height that an object (e.g. building, tree, etc.) can have. The z value that is determined among the possible z values that the first pixel (PX1) can have on the z axis is the actual shooting site of the image data corresponding to the first pixel (PX1) in the first area (R1), based on the ground. It may be the actual height of an object (e.g. building, tree, etc.) existing in .

Referring again to FIGS. 1 and 2 , the processor 110 may identify a first probability that each possible z value of the first pixel is correct, based on the first depth map (S300).

The first depth map may be a depth map for the first area. The processor 110 uses the first depth map to determine z1, z2,... , zn, the first probability that each is the correct answer can be identified based on the depth map.

FIG. 6 is a diagram for explaining step S300 of FIG. 2. FIG. 7 is a diagram for explaining steps S301, S303, and S305 of FIG. 6. FIG. 8 is a diagram for explaining step S307 of FIG. 6.

1, 2, 5, 6, 7, and 8, the processor 110 first identifies each of the possible z values of the first pixel PX1 and the first pixel PX1 to identify the first probability. The coordinates (x1, y1) of (PX1) may be projected onto the first depth map (DM1) (S301).

Processor 110 operates z1, z2,... After each of , zn is included in the coordinates (x1, y1) of the first pixel (PX1), each of a plurality of coordinates including x, y, and z values can be projected onto the first depth map (DM1). For example, the processor 110 sets the coordinates PX1_1(x1, y1, z1) to PX1_n(x1, y1, zn) including the possible z-axis values for the coordinates (x1, y1) of the first pixel (PX1), respectively. Can be projected onto the first depth map DM1. The processor 110 may identify each of the coordinates PX1'_1 (x1', y1', z1') to PX1'_n (x1', y1', zn') projected on the first depth map DM1.

The processor 110 may identify the first depth value in the first depth map DM1 (S303). The first depth value may be identified from the first depth map DM1. The processor 110 is configured to project the coordinate PX1_1(x1, y1, z1), which includes the z1 value among the possible values on the z-axis with respect to the coordinates (x1, y1) of the first pixel (PX1), into the projected coordinate PX1'_1(x1' , y1', z1'), the first depth value can be identified.

The processor 110 determines each possible z value of the first pixel PX1 from the center point CP and a first distance D1 from the coordinates (z1, y1) of the first pixel PX1 and the first depth value. The first error can be identified (S305). The center point CP may be location information of the photographing device identified based on the location information of the first depth map DM1. For example, the center point CP may correspond to the location of the photographing device that generated image data to generate the first depth map DM1.

The processor 110 identifies the distance from each of PX1_1 (x1, y1, z1) to PX1_n (x1, y1, zn) to the center point (CP), and PX1_1 (x1, y1, z1) to PX1_n (x1, y1). , zn), the first error between the first depth values when each is projected on the first depth map can be calculated.

For example, the processor 110 may project the first distance D1 between PX1_1 (x1, y1, z1) and the center point (CP) and PX1_1 (x1, y1, z1) onto the first depth map (DM1). The first error can be calculated by identifying the first depth values at the coordinates PX1'_1 (x1', y1', z1'), respectively.

The processor 110 may identify the first probability (P1) based on the first error (S307). The first probability P1 may include first to nth depth map probabilities (Pd_1 to Pd_n) for each of the n values on the z-axis included in the possible z values of the first pixel PX1. For example, the processor 110 may perform PX1_1(x1, y1, z1) based on the first error including a plurality of errors calculated for each of PX1_1(x1, y1, z1) to PX1_n(x1, y1, zn). ) to PX1_n (x1, y1, zn), respectively, the first to nth depth map probabilities (Pd_1 to Pd_n) can be calculated. For example, the processor 110 may calculate the first depth map probability (Pd_1) based on the error calculated for PX1_1 (x1, y1, z1).

The first probability P1 may be a probability that each of the n values on the z-axis included in the possible z values of the first pixel PX1 is the correct answer, based on the first depth map DM1. The probability that each of the possible z values that the first pixel (PX1) can have on the z-axis is correct is that the object present at the actual shooting scene of the image data corresponding to the first pixel (PX1) in the first region (R1) is It may be the probability that it is close to the actual height.

The processor 110 may identify the first probability P1 based on the first error, for example, using a Gaussian distribution.

Referring again to FIGS. 1 and 2 , the processor 110 may identify a second probability based on the difference between the z2 value of the second pixel and each of the possible z values of the first pixel (S400).

FIG. 9 is a diagram for explaining step S400 of FIG. 2.

Referring to FIGS. 1, 2, 4, and 9, the second pixel (PX2) is one of the pixels included in the first region (R1) and may be a pixel located next to the first pixel (PX1). . The second pixel PX2 may have a determined z value on the z axis. The processor 110 may identify the second probability (P2) based on the difference between each of PX1_1 (x1, y1, z1) to PX1_n (x1, y1, zn) and PX2 (x2, y2, z). The second probability P2 is based on the difference between each of the n values on the z axis included in the possible z values of the first pixel PX1 and the confirmed z value of the second pixel PX2. It may include through nth comparison probabilities (Pc_1 to Pc_n). For example, the processor 110 determines the first comparison probability (Pc_1) based on the difference between the z1 value, which is one of the possible z values of the first pixel (PX1), and the confirmed z value of the second pixel (PX2). It can be calculated.

The second probability (P2) is, based on the second pixel (PX2), which is a pixel next to the first pixel (PX1), each of the n values on the z-axis included in the possible z values of the first pixel (PX1) is the correct answer. It may be a probability. The probability that each of the possible z values that the first pixel (PX1) can have on the z-axis is correct is that the object present at the actual shooting scene of the image data corresponding to the first pixel (PX1) in the first region (R1) is It may be the probability that it is close to the actual height.

The processor 110 may identify the second probability P2 using, for example, a probability density function.

Referring again to FIGS. 1 and 2, the processor 110 may identify a first z value, which is a value on the z axis of the coordinates (x1, y1) of the first pixel, based on the first probability and the second probability. (S500). The first z value may be a confirmed z value of the first pixel.

FIG. 10 is a diagram for explaining step S500 of FIG. 2.

Referring to FIGS. 1, 2, and 10, the processor 110 determines the first z value (Zpx1), which is the z value of the first pixel (PX1), the depth map probability for each of the n values on the z axis. (Pd_1 to Pd_n), respectively, the determined z value of the first pixel (PX1) by summing the comparison probabilities (Pc_1 to Pc_n) for each of the n values on the z-axis and the product of each of the n values on the z-axis. (Zpx1) can be calculated. For example, the processor 110 multiplies the z1 value, the first depth map probability (Pd_1), and the first comparison probability (Pc_1) to determine the z value of the first pixel (PX1), and You can add up all the values derived by performing each operation.

Referring again to FIGS. 1 and 2, the processor 110 uses each of a plurality of pixels included in the remaining area of the total shooting area and each of a plurality of depth maps corresponding to the remaining area, The value on the z-axis can be identified (S600).

For example, as shown in FIG. 4, the total imaging area WA may include a remaining area other than the first area R1. The processor 110 may perform steps S100 to S500 for each of all pixels included in the first region R1 to identify a determined z value for each of the pixels in the first region R1. there is. Furthermore, the processor 110 performs steps S100 to S500 for each of all pixels included in the remaining area of the total imaging area (WA), and determines the z value for each of the pixels in the total imaging area (WA). can be identified. Each z value determined for each pixel of the total shooting area (WA) may be height information of an object (eg, a building, a tree, etc.) existing in the actual area corresponding to the area in which the depth map was created.

The processor 110 may generate a digital elevation model for the entire shooting area (S700). The processor 110 may generate a digital elevation model for the entire photographed area (WA) based on the determined z value obtained by performing steps S100 to S500 for each of all pixels of the entire photographed area (WA). .

FIG. 11 is a diagram for explaining steps S600 and S700 of FIG. 2.

Referring to FIG. 11, a digital elevation model (DSM) is generated based on the determined z value obtained by performing steps S100 to S500 for each pixel of the entire captured area (WA). In a digital elevation model (DSM), the determined z value for each pixel of the total capture area (WA) can be expressed in correspondence with the pixel value. For example, the higher the pixel value, the higher the confirmed z value may be, and the lower the pixel value, the lower the confirmed z value may be. The z value determined by the electronic device according to an embodiment of the present invention may be a value approximate to the height of an object existing in an actual location or actual terrain corresponding to a pixel relative to the ground.

The electronic device according to an embodiment of the present invention calculates a depth map probability based on a depth map for a partial area of the total shooting area, calculates a comparison probability derived between two pixels in the partial area, and generates a depth map. By determining the value on the z-axis that is closest to the actual height of the area, continuous and highly accurate height information can be obtained and a digital elevation model can be created even if the height of the actual shooting site changes rapidly.

FIG. 12 is a diagram illustrating hardware implementation of an electronic device according to some embodiments of the present invention.

Referring to FIG. 12, the electronic device 100 according to some embodiments of the present invention may be implemented as the electronic device 1000. The electronic device 1000 may include a processor 1010, an input/output device 1020 (I/O), a memory 1030, an interface 1040, a storage 1050, and a bus 1060. there is. The processor 1010, input/output device 1020, memory 1030, interface 1040, and/or storage 1050 may be coupled to each other through a bus 1060. The bus 1060 corresponds to a path along which data moves.

Specifically, the processor 1010 includes a Central Processing Unit (CPU), Micro Processor Unit (MPU), Micro Controller Unit (MCU), Graphic Processing Unit (GPU), microprocessor, digital signal processor, microcontroller, and application processor (AP). , application processor) and logic elements capable of performing similar functions.

The input/output device 1020 may include at least one of a keypad, a keyboard, a touch screen, and a display device.

The memory 1030 may load data and/or programs. At this time, the memory 1030 is an operating memory for improving the operation of the processor 1010, and may include high-speed DRAM and/or SRAM. The memory 1030 may be one or more volatile memory devices, such as Double Data Rate Static DRAM (DDR SDRAM), Single Data Rate SDRAM (SDR SDRAM), and/or Electrical Erasable Programmable ROM (EEPROM), or flash memory. It may include one or more non-volatile memory devices.

The interface 1040 may perform a function of transmitting data to or receiving data from a communication network. Interface 1040 may be wired or wireless. For example, the interface 1040 may include an antenna or a wired or wireless transceiver.

The storage 1050 may store and store data and/or programs. Storage 1050 may include one or more non-volatile memory devices, such as a solid state drive (SSD), a hard drive, or flash memory. In the present invention, the storage 1050 can store a computer program consisting of instructions for performing an abnormal transaction detection method.

Alternatively, the electronic device 100 according to embodiments of the present invention may be a system formed by connecting a plurality of electronic devices 1000 to each other through a network. In this case, each module or combination of modules may be implemented as the electronic device 1000. However, this embodiment is not limited to this.

Additionally, the electronic device 100 may be a workstation, a data center, an internet data center (IDC), a direct attached storage (DAS) system, a storage area network (SAN) system, or a network attached storage (NAS). ) system and a RAID (redundant array of inexpensive disks, or redundant array of independent disks) system, but the present embodiment is not limited thereto.

Additionally, the electronic device 100 can transmit data through a network. Networks may include networks based on wired Internet technology, wireless Internet technology, and short-distance communication technology. Wired Internet technology may include, for example, at least one of a local area network (LAN) and a wide area network (WAN).

Wireless Internet technologies include, for example, Wireless LAN (WLAN), DMNA (Digital Living Network Alliance), Wibro (Wireless Broadband), Wimax (World Interoperability for Microwave Access: Wimax), and HSDPA (High Speed Downlink Packet). Access), HSUPA (High Speed Uplink Packet Access), IEEE 802.16, Long Term Evolution (LTE), LTE-A (Long Term Evolution-Advanced), Wireless Mobile Broadband Service (WMBS) and 5G NR (New Radio) technology. However, this embodiment is not limited to this.

Short-range communication technologies include, for example, Bluetooth, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra-Wideband (UWB), ZigBee, and Near Field Communication. At least one of NFC), Ultrasound Communication (USC), Visible Light Communication (VLC), Wi-Fi, Wi-Fi Direct, and 5G NR (New Radio) may include. However, this embodiment is not limited to this.

The electronic device 100 that communicates through a network can comply with technical standards and standard communication methods for mobile communication. For example, standard communication methods include GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), CDMA2000 (Code Division Multi Access 2000), and EV-DO (Enhanced Voice-Data Optimized or Enhanced Voice-Data Only). , Wideband CDMA (WCDMA), High Speed Downlink Packet Access (HSDPA), High Speed Uplink Packet Access (HSUPA), Long Term Evolution (LTE), Long Term Evolution-Advanced (LTEA), and 5G New Radio (NR) may include. However, this embodiment is not limited to this.

The above description is merely an illustrative explanation of the technical idea of the present embodiment, and those skilled in the art will be able to make various modifications and variations without departing from the essential characteristics of the present embodiment. Accordingly, the present embodiments are not intended to limit the technical idea of the present embodiment, but rather to explain it, and the scope of the technical idea of the present embodiment is not limited by these examples. The scope of protection of this embodiment should be interpreted in accordance with the claims below, and all technical ideas within the equivalent scope should be interpreted as being included in the scope of rights of this embodiment.

Claims (10)

In the electronic device for generating a digital elevation model,
processor; and
a memory operatively coupled to the processor,
The memory, when executed, causes the processor to:
Identifying the coordinates (x1, y1) of the first pixel included in the image data for the first area, which is a partial area of the total shooting area,
Based on the coordinates (x1, y1) of the first pixel, identify possible z1 values that include n values on the z-axis that the first pixel can have (where n is a natural number),
Identifying a first probability that each of the possible z1 values is correct based on a first depth map corresponding to the first area,
identify a second probability based on the difference between each of the possible z1 values and a confirmed z2 value, which is a value on the z-axis of a second pixel;
Based on the first probability and the second probability, identify a first z value that is a value on the z axis of the coordinates (x1, y1) of the first pixel,
Identifying a value on the z-axis for each of the plurality of pixels using each of a plurality of pixels included in the remaining area of the total shooting area and each of a plurality of depth maps corresponding to the remaining area,
Storing instructions for generating the digital elevation model for the entire shooting area.
Electronic devices.
According to claim 1,
The first probability includes first to nth depth map probabilities for each of the n values on the z axis included in the possible z1 values.
Electronic devices.
According to claim 1,
The instructions allow the processor to:
Projecting each of the possible z1 values and the coordinates (x1, y1) of the first pixel onto the first depth map,
Identifying a first depth value in the first depth map onto which each of the possible z1 values and the coordinates (x1, y1) of the first pixel are projected,
identify a first error between the first depth value and a first distance from a center point to each of the possible z1 values and coordinates (x1, y1) of the first pixel;
Based on the first error, to identify the first probability
Electronic devices.
According to clause 3,
The first probability includes first to nth depth map probabilities for each of the n values on the z-axis included in the possible z1 values,
The first probability is the probability that each of the n values on the z-axis included in the possible z1 values is the correct answer based on the first depth map.
Electronic devices.
According to clause 3,
The instructions are such that the processor,
Identifying the first probability based on the first error using a Gaussian distribution
Electronic devices.
According to clause 3,
The center point is location information of the photographing device identified based on the location information of the first depth map.
Electronic devices.
According to claim 1,
The instructions are such that the processor,
To identify the second probability using a probability density function
Electronic devices.
According to claim 1,
The second probability includes first to nth comparison probabilities calculated based on the difference between the z2 value and each of the n values on the z axis included in the possible z1 values.
Electronic devices.
According to claim 1,
The first probability includes first to nth depth map probabilities for each of the n values on the z-axis included in the possible z1 values,
The second probability includes first to nth comparison probabilities calculated based on the difference between the z2 value and each of the n values on the z-axis included in the possible z1 values,
The instructions are such that the processor,
Based on each of the possible z1 values, each of the first to nth depth map probabilities, and each of the first to nth comparison probabilities, to identify the first z value
Electronic devices.
A computer-readable recording medium recording a program capable of executing the method according to any one of claims 1 to 9.

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