House calibration method and device and augmented reality equipment

Abstract

The embodiment of the invention discloses a house calibration method and device and augmented reality equipment. According to one specific embodiment, the method comprises the steps that in response to detection of calibration of corner points of a house, a house image is obtained, the house image is obtained in the calibration process, and the shape of the house is a target shape; based on the house image, determining a structure line associated with each corner point; in response to detection of calibration completion, for each corner point in the house, determining a distance from a projection position point of the corner point in the house image to a structure line associated with the corner point; and determining the coordinate value of each corner point based on the distance corresponding to each corner point. According to the embodiment, it is guaranteed that the geometrical shape of the house space better fits the actual situation.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of computer technology, and in particular to a house calibration method, apparatus, and extended reality device. Background Art

[0002] The advent of Extended Reality (XR) technology has brought people a new lifestyle. XR devices have received a lot of attention, and house outline calibration based on XR devices is an important technology. When calibrating a room, the user clicks on the boundaries of the interior walls in turn to calibrate the house outline to form a bounding box, which is convenient for subsequent XR applications. However, due to display and user operation problems, inaccurate calibration often occurs, that is, the virtual house outline after calibration does not fit the actual scene, resulting in a poor user experience. Summary of the invention

[0003] This disclosure section is provided to introduce concepts in a brief form, which will be described in detail in the detailed description section below. This disclosure section is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0004] In a first aspect, an embodiment of the present disclosure provides a house calibration method, which is applied to an extended reality device, comprising: in response to detecting that a corner point of a house is calibrated, obtaining a house image, wherein the house image is obtained during the calibration process and the shape of the house is a target shape; based on the house image, determining a structural line associated with each corner point; in response to detecting that the calibration is finished, determining, for each corner point in the house, a distance between a projection position point of the corner point in the house image and a structural line associated with the corner point; and determining a coordinate value of each corner point based on the distance corresponding to each corner point.

[0005] In a second aspect, an embodiment of the present disclosure provides a house calibration device, which is arranged in an extended reality device, including: an acquisition unit, used to acquire a house image in response to detecting that a corner point of a house is calibrated, wherein the house image is acquired during the calibration process and the shape of the house is the target shape; a first determination unit, used to determine the structural lines associated with each corner point based on the house image; a second determination unit, used to determine, for each corner point in the house, the distance between the projection position point of the corner point in the house image and the structural line associated with the corner point in response to detecting the end of calibration; an adjustment unit, used to determine the coordinate value of each corner point based on the distance corresponding to each corner point.

[0006] In a third aspect, an embodiment of the present disclosure provides an extended reality device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the house calibration method as described in the first aspect.

[0007] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the house calibration method as described in the first aspect.

[0008] The house calibration method, device and extended reality device provided by the embodiments of the present disclosure, in response to detecting the corner points of the house, calibrate the house, and obtain the house image during the calibration process; then, based on the house image, determine the structural lines associated with each corner point; in response to detecting the end of calibration, for each corner point in the house, determine the distance between the projection position point of the corner point in the house image and the structural line associated with the corner point; then, based on the distances corresponding to each corner point, determine the coordinate value of each corner point. In this way, the determined corner points are aligned with the real corner points of the house, ensuring that the geometric shape of the house space is more in line with the actual situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0010] Figure 1 is a flow chart of an embodiment of a house calibration method according to the present disclosure;

[0011] Figure 2 is a schematic diagram of an embodiment of the positional relationship between corner points and structural lines in the house calibration method disclosed in the present invention;

[0012] Figure 3 is a schematic diagram of an embodiment of parameter representation of a quadrilateral in a house calibration method according to the present disclosure;

[0013] Figure 4 is a schematic diagram of an embodiment of a parameter representation of a hexagon in a house calibration method according to the present disclosure;

[0014] Figure 5 is a flow chart of another embodiment of a house calibration method according to the present disclosure;

[0015] Figure 6is a flow chart of an embodiment of determining structural lines associated with corner points in a house calibration method according to the present disclosure;

[0016] Figure 7 is a structural schematic diagram of an embodiment of a house calibration device according to the present disclosure;

[0017] Figure 8 is an exemplary system architecture diagram in which various embodiments of the present disclosure may be applied;

[0018] Fig. 9 It is a schematic diagram of the structure of a computer system suitable for implementing an extended reality device of an embodiment of the present disclosure. DETAILED DESCRIPTION

[0019] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0020] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0021] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0022] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0023] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0024] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0025] Please refer to Figure 1 , shows a process 100 of an embodiment of a house calibration method according to the present disclosure. The house calibration method is generally applied to extended reality devices. XR describes a series of methods for changing reality. Since XR is a general term for multiple technologies such as VR (Virtual Reality), AR (Augmented Reality) and MR (Mixed Reality), XR devices generally include: VR devices, AR devices and MR devices. The method includes the following steps:

[0026] Step 101, in response to detecting and calibrating the corner points of a house, an image of the house is acquired.

[0027] In this embodiment, the execution body of the house calibration method can detect whether to calibrate the corner points of the house. The corner points of the house usually refer to the points where the base line (the line where the wall intersects the ground) and the corner line (the line where the wall intersects the wall) intersect. Here, the base line and the corner line are usually called the structural lines of the house. Usually, each corner point will have three structural lines (i.e., two base lines and one corner line) passing through it.

[0028] When using an XR device, the user usually scans the indoor scene first to determine the safe area during the use of the XR device. When the user uses the XR device, the above-mentioned execution subject can continuously obtain environmental images. If it is detected that the corner points of the house are calibrated, the above-mentioned execution subject can obtain multiple frames of house images during the calibration process. When the user calibrates the corner points of the room, the camera of the XR device usually covers the calibrated position, and the house image obtained in this process will show various corner points.

[0029] Here, the shape of the house is usually the target shape, and the shape of the house can also be understood as the shape of the floor plan of the house. Here, if the angle between the line connecting two corner points adjacent to the target corner point and the target corner point is approximately a right angle, for example, within a preset range (for example, 85 degrees to 95 degrees), and the number of target corner points in the house is greater than a preset number threshold, then the shape of the house can be considered to be the target shape.

[0030] Step 102: Determine the structural line associated with each corner point based on the house image.

[0031] In this embodiment, the execution subject may determine the structural lines associated with each corner point based on the house image. Specifically, for each corner point in the house, the execution subject may input the house image into a pre-trained structural line extraction model to obtain the structural line passing through the corner point, and store the corner point in association with the structural line passing through the corner point.

[0032] Typically, at most three structure lines associated with the corner points can be extracted from each target image. In some cases, some structure lines may be blocked, in which case two or one structure line associated with the corner point may be extracted, or no structure line may be extracted.

[0033] like Figure 2 As shown, Figure 2 A schematic diagram of an embodiment of the positional relationship between corner points and structural lines in the house calibration method according to the present disclosure is shown. Figure 2 In the figure, the point shown in the circle is a corner point, and there are three structure lines passing through the corner point, namely the line segments shown in icon 201, icon 202 and icon 203.

[0034] Step 103 , in response to detecting that the calibration is completed, for each corner point in the house, determining the distance between the projection position point of the corner point in the house image and the structural line associated with the corner point.

[0035] In this embodiment, the execution subject can detect whether the calibration of the corner points of the house is completed. Here, when the calibration of all corner points in the room is completed, that is, the calibrated corner points form a closed area, it can be considered that the calibration of the corner points of the house is completed. Generally, if the calibration is completed, the structural lines associated with each corner point are also determined accordingly.

[0036] If the calibration is detected to be completed, for each corner point in the house, the execution entity may determine the projection position of the corner point in the house image, that is, determine the projection position of the corner point in the house image containing the corner point.

[0037] Here, the execution subject can project the corner point onto the image by the projection formula shown in the following formula (1):

[0038]

[0039] in, represents the pixel coordinates of the i-th corner point, K represents the intrinsic parameter matrix of the camera, R and t represent the position and attitude of the camera in the world coordinate system respectively. The position is represented by XYZ coordinates, and the attitude includes pitch angle, roll angle and yaw angle. irepresents the coordinates of the i-th corner point in the world coordinate system, and λ represents the depth value of the corner point, that is, the Z coordinate value of the corner point in the image coordinate system.

[0040] Here, we can obtain The result is (u,v,1) T , where u is the X coordinate value of the i-th corner point in the image coordinate system, v is the Y coordinate value of the i-th corner point in the image coordinate system, and 1 is the Z coordinate value of the i-th corner point in the image coordinate system.

[0041] Then, the execution entity may determine the distance between the projection position point and the structural line associated with the corner point.

[0042] Here, the execution subject can determine the distance between the projection position point of the corner point and the structural line associated with the corner point by the formula shown in the following formula (2):

[0043]

[0044] in, Represents the pixel coordinates of the i-th corner point, E ij Represents the distance between the projection position of the i-th corner point and the j-th structure line. Assume that the equation of the structure line l is: ax+by+c=0, and the pixel coordinates of the i-th corner point are (u, v, 1) T , then formula (2) can be converted into the following formula (3):

[0045]

[0046] Step 104: Determine the coordinate value of each corner point based on the distance corresponding to each corner point.

[0047] In this embodiment, the execution subject can determine the coordinate value of each corner point based on the distance corresponding to each corner point. Specifically, the above formula (1) and formula (3) can be used to determine the coordinate value corresponding to each corner point when the distance corresponding to each corner point is less than the preset distance threshold.

[0048] Here, nonlinear optimization algorithms such as Gauss-Newton iteration method and gradient descent method can be used to determine the coordinate values ​​of each corner point. Gauss-Newton iteration method is an iterative method for least squares regression parameters in nonlinear regression model. This method uses Taylor series expansion to approximately replace the nonlinear regression model, and then through multiple iterations and multiple corrections of regression coefficients, the regression coefficients are continuously approached to the optimal regression coefficients of the nonlinear regression model, and finally the residual sum of squares of the original model is minimized. Gradient descent method is a first-order optimization algorithm. To use gradient descent method to find the local minimum of a function, it is necessary to iteratively search for the specified step distance point in the opposite direction of the gradient (or approximate gradient) corresponding to the current point on the function.

[0049] The method provided by the above embodiment of the present disclosure calibrates the corner points of the house in response to detection, and obtains the house image during the calibration process; then, based on the house image, determines the structural lines associated with each corner point; in response to detecting the end of calibration, for each corner point in the house, determines the distance between the projection position point of the corner point in the house image and the structural line associated with the corner point; then, based on the distances corresponding to each corner point, determines the coordinate value of each corner point. In this way, the determined corner points are aligned with the real corner points of the house, ensuring that the geometric shape of the house space is more in line with the actual situation.

[0050] In some optional implementations, the execution subject may determine the coordinate value of each corner point based on the distance corresponding to each corner point in the following manner: the execution subject may sum the distances corresponding to each corner point and use the sum result as the optimization target; then, the coordinate value of the corner point when the optimization target is minimized may be determined. Here, a nonlinear optimization algorithm such as a Gauss-Newton iteration method and a gradient descent method may be used to determine the coordinate value of each corner point.

[0051] For example, if there are 4 corner points in the room and the number of structural lines associated with each corner point is 10, then the distance between the projection position point corresponding to each corner point and each structural line needs to be calculated, that is, a total of 40 distances are calculated. Here, the coordinate values ​​of the 4 corner points in the three-dimensional space are determined so that the sum of the 40 distances is minimized. At this time, the geometric structure of the house space can be guaranteed to be closer to the actual situation.

[0052] In some optional implementations, the horizontal projection shape of the house can be an N-gon, and the coordinate values ​​of the corner points of the N-gon can be characterized by M parameters. Here, M is usually less than N*2. For example, if N is 8, then M is less than 16. The execution entity can determine the specific values ​​of the M parameters when the optimization target is minimized based on the structural lines of each corner point and the constraints of the M parameters, thereby obtaining the coordinate values ​​of each corner point. Since the conventional optimization method for an N-gon is to optimize the N*2 parameters of the N-gon (i.e., the horizontal coordinates and vertical coordinates of the N corner points of the N-gon), and this scheme optimizes M parameters, since M is less than N*2, the number of parameters that need to be optimized is reduced, so that the corner point alignment can be completed more quickly and stably.

[0053] In some optional implementations, the projection shape of the house in the horizontal direction may be a quadrilateral or a hexagon. Specifically, the coordinate values ​​of the corner points of a quadrilateral house may be represented by 5 parameters, and the coordinate values ​​of the corner points of a hexagonal house may be represented by 7 parameters.

[0054] In some optional implementations, the coordinate values ​​of each corner point of the house may be determined by the following parameters: the horizontal coordinate value and the vertical coordinate value of the target corner point, the length of at least two adjacent sides of the house, and the angle between the first target side and the horizontal coordinate axis of the preset coordinate system. The target corner point may be any corner point of the house, and the first target side may be any side of the house. The preset coordinate system is usually a world coordinate system.

[0055] It should be noted that if the projection shape of the house in the horizontal direction is a quadrilateral, the parameters for determining the coordinate value of the corner point include the lengths of the two adjacent sides of the house; if the projection shape of the house in the horizontal direction is a hexagon, the parameters for determining the coordinate value of the corner point include the lengths of the four adjacent sides of the house.

[0056] Generally, after the user puts on the XR device and initializes the XR device, the execution subject may determine the direction the user faces after the XR device is initialized as the positive direction of the X coordinate axis of the preset coordinate system. The target corner point may be the first corner point calibrated when the user calibrates the house, and the first target edge may be the first edge calibrated when the user calibrates the house.

[0057] As an example, Figure 3 As shown, Figure 3 FIG. 1 is a schematic diagram showing an embodiment of a parameter representation of a quadrilateral in a house calibration method according to the present disclosure. Figure 3In the figure, the house contains four corner points, namely p1, p2, p3 and p4. Here, the quadrilateral only needs 5 parameters to represent, namely (x, y, θ, L1, L2), where the four corner points can be represented by the following formula:

[0058] p1=(x,y) T

[0059] p2=p1+L1*(cosθ,sinθ) T

[0060] p3=p2+L2*(-sinθ,cosθ) T

[0061] p4=p1+L2*(-sinθ,cosθ) T

[0062] For a quadrilateral, the optimized parameters are the corner points p1-p4. Each corner point is represented by two numbers, the horizontal and vertical coordinate values. Therefore, the conventional optimization method for a quadrilateral is to optimize the 8 parameters of the quadrilateral. This solution uses the above 5 parameters to represent the corner points of the quadrilateral. Only these 5 parameters need to be optimized. Therefore, the parameters that need to be optimized are reduced, so that the corner point alignment can be completed more quickly and stably.

[0063] As an example, Figure 4 As shown, Figure 4 FIG. 1 is a schematic diagram showing an embodiment of a parameter representation of a hexagon in a house calibration method according to the present disclosure. Figure 4 In the example, the house contains six corner points, namely p1, p2, p3, p4, p5 and p6. Here, the hexagon only needs 7 parameters to represent, namely (x, y, θ, L1, L2, L3, L4), where the six corner points can be represented by the following formula:

[0064] p1=(x,y) T

[0065] p2=p1+L1*(cosθ,sinθ) T

[0066] p3=p2+L2*(-sinθ,cosθ) T

[0067] p4=p3-L3*(cosθ,sinθ) T

[0068] p5=p4+L4*(sinθ,-cosθ) T

[0069] p6=p1+(L2-L4)*(-sinθ, cosθ) T

[0070] For a hexagon, the optimized parameters are the corner points p1-p6. Each corner point is represented by two numbers, the horizontal and vertical coordinate values. Therefore, the conventional optimization method for a hexagon is to optimize the 12 parameters of the hexagon. This solution uses the above 7 parameters to represent the corner points of the hexagon. Only these 7 parameters need to be optimized. Therefore, the parameters that need to be optimized are reduced, so that the corner point alignment can be completed more quickly and stably.

[0071] It should be noted that the representation of the above corner points is two-dimensional, but in reality each corner point of the room is a three-dimensional point, so it is necessary to add a height information to each corner point in the end.

[0072] Further references Figure 5 , shows a process 500 of another embodiment of a house calibration method according to the present disclosure. The house calibration method comprises the following steps:

[0073] Step 501, in response to detecting that the corner points of the house are calibrated, obtain the house image and the calibration coordinate values ​​of each corner point.

[0074] In this embodiment, the execution body of the house calibration method can detect whether to calibrate the corner points of the house. The corner points of the house usually refer to the points where the base line (the line where the wall intersects the ground) and the corner line (the line where the wall intersects the wall) intersect. Here, the base line and the corner line are usually called the structural lines of the house. Usually, each corner point will have three structural lines (i.e., two base lines and one corner line) passing through it.

[0075] When using an XR device, users usually scan the indoor scene first to determine the safe area during the use of the XR device. During the scanning process, the user will use the handle that matches the XR device to calibrate the room. The specific calibration process is: the user draws a polygon in the room space, that is, first click the first corner point, draw a line along the footing line to the second corner point, and then draw a line along the footing line to the third corner point, until the area drawn by the user is a closed area. This process can be called the calibration process of the corner points of the room. In the process of calibrating the corner points, the intersection of the rays emitted by the handle and the ground can be determined according to the position of the handle, and the three-dimensional coordinates of the intersection can be obtained as the calibration coordinate value of the corner point.

[0076] When the user uses the XR device, the above-mentioned execution subject can continuously obtain the environment image. If the calibration of the corner points of the house is detected, the above-mentioned execution subject can obtain multiple frames of house images during the calibration process. When the user calibrates the corner points of the room, the camera of the XR device usually covers the calibrated position, and the house image obtained in this process will show each corner point.

[0077] Here, the shape of the house is usually the target shape, and the shape of the house can also be understood as the shape of the floor plan of the house. Here, if the angle between the line connecting two corner points adjacent to the target corner point and the target corner point is approximately a right angle, for example, within a preset range (for example, 85 degrees to 95 degrees), and the number of target corner points in the house is greater than a preset number threshold, then the shape of the house can be considered to be the target shape.

[0078] Step 502: Determine the structural line associated with each corner point based on the house image and the calibrated coordinate values.

[0079] In this embodiment, the execution subject may determine the structural lines associated with each corner point based on the house image and the calibrated coordinate values. Specifically, for each corner point in the house, the execution subject may use the calibrated coordinate values ​​of the corner point to determine the image containing the corner point as the target image; then, the target image may be input into a pre-trained structural line extraction model to obtain the structural line passing through the corner point, and the corner point and the structural line passing through the corner point are associated and stored.

[0080] Here, since the three-dimensional coordinate interval corresponding to the scene displayed by the house image can be obtained in the process of scanning the house environment to obtain the house image, for each corner point in the house, the execution subject can determine whether the calibrated coordinate value of the corner point is within the three-dimensional coordinate interval corresponding to the scene displayed by the room image; if it is within the three-dimensional coordinate interval, it can be determined that the house image contains the corner point, and the house image can be used as the target image.

[0081] Typically, at most three structure lines associated with the corner points can be extracted from each target image. In some cases, some structure lines may be blocked, in which case two or one structure line associated with the corner point may be extracted, or no structure line may be extracted.

[0082] Step 503 , in response to detecting that the calibration is completed, for each corner point in the house, determining the distance between the projection position point of the corner point in the house image and the structural line associated with the corner point.

[0083] Here, when the user completes the calibration of all corner points in the room, that is, the area drawn by the user forms a closed area, it can be considered that the user has completed the calibration of the corner points of the house. Generally, if the user completes the calibration, the structural lines associated with each corner point are also determined accordingly.

[0084] Step 504: Determine the coordinate value of each corner point based on the distance corresponding to each corner point.

[0085] In this embodiment, steps 503 and 504 may be performed in a manner similar to steps 103 and 104, and will not be described in detail herein.

[0086] from Figure 5 It can be seen that Figure 1 Compared with the corresponding embodiment, the process 500 of the house calibration method in this embodiment embodies the steps of obtaining the calibration coordinate values ​​of each corner point during the house calibration process, and determining the structural lines associated with each corner point based on the house image and the calibration coordinate values. Therefore, the solution described in this embodiment can use the calibrated coordinate values ​​to determine the structural lines associated with the corner points from the house image, so that the structural lines can be extracted more accurately from the house image.

[0087] Continue to refer Figure 6 , which shows a process 600 of an embodiment of determining a structural line associated with a corner point in a house calibration method. The process 600 of determining a structural line associated with a corner point includes the following steps:

[0088] Step 601, for each corner point in the house, perform the following structural line extraction steps: use the calibrated coordinate value of the corner point to determine whether the corner point is successfully projected on the target image; if the projection is successful, extract the structural line associated with the corner point from the target image; determine whether the number of structural lines associated with the corner point is less than a preset number threshold; if the number is greater than or equal to the number threshold, determine that the calibration for the corner point is completed.

[0089] In this embodiment, the execution body of the house calibration method can perform a structural line extraction step for each corner point in the house. Here, at most three structural lines can be extracted each time the structural line is extracted for a corner point. The structural line extraction refers to extracting the base line and the corner line of each corner point.

[0090] Here, the structure line extraction step may include sub-steps 6011, 6012, 6013 and 6014. Among them:

[0091] Step 6011, using the calibrated coordinate value of the corner point, determine whether the corner point is successfully projected on the target image.

[0092] In this embodiment, the execution subject may project the corner point into the target image using the calibrated coordinate value of the corner point. Here, the execution subject may project the corner point into the target image using the projection formula shown in the above formula (1); thereafter, it may be determined whether the corner point is successfully projected onto the target image. As an example, it may be determined whether the position point representing the corner point in the target image coincides with the projection position point of the corner point on the target image; if they coincide, it may be determined that the corner point is successfully projected onto the target image; if they do not coincide, it may be determined that the corner point is unsuccessfully projected onto the target image.

[0093] If the corner point is successfully projected onto the target image, step 6012 may be executed.

[0094] Step 6012: If the corner point is successfully projected on the target image, a structure line associated with the corner point is extracted from the target image.

[0095] In this embodiment, if it is determined in step 6011 that the corner point is successfully projected onto the target image, the execution entity may extract the structure line associated with the corner point from the target image.

[0096] Here, the execution subject may input the target image into a pre-trained structure line extraction model to obtain a structure line passing through the corner point, and associate the corner point with the structure line passing through the corner point and store them.

[0097] Step 6013, determining whether the number of structure lines associated with the corner point is less than a preset number threshold.

[0098] In this embodiment, the execution entity can determine whether the number of structure lines associated with the corner point is less than a preset number threshold. If the number of structure lines currently associated with the corner point is 5 and the number threshold is 10, it can be determined that the number of structure lines associated with the corner point is less than the number threshold.

[0099] If it is determined that the number of structure lines associated with the corner point is less than the number threshold, the execution entity may execute step 602 .

[0100] If it is determined that the number of structure lines associated with the corner point is greater than or equal to the above number threshold, the above execution entity can execute step 6014.

[0101] Step 6014: If the number is greater than or equal to the number threshold, it is determined that the calibration of the corner point is completed.

[0102] In this embodiment, if it is determined in step 6013 that the number of structure lines associated with the corner point is greater than or equal to the number threshold, the execution entity may determine that the calibration of the corner point is completed.

[0103] Step 602 : If the number is less than the number threshold, the target image that has not been projected is determined as the target image, and the structure line extraction step 601 is continued.

[0104] In this embodiment, if it is determined in step 6013 that the number of structure lines associated with the corner point is less than the above-mentioned number threshold, the above-mentioned execution entity can determine the target image that is not projected in the image acquired during the calibration process as the target image, and continue to execute the above-mentioned structure line extraction step 601 (i.e., sub-steps 6011-6014).

[0105] The method provided by the above-mentioned embodiment of the present disclosure performs the following structural line extraction steps for each corner point in the house: projecting the corner point into the target image; determining whether the corner point is successfully projected on the target image; if the corner point is successfully projected on the target image, extracting the structural line associated with the corner point from the target image; determining whether the number of structural lines associated with the corner point is less than a preset number threshold; if the number is greater than or equal to the number threshold, determining that the calibration for the corner point is completed; if the number is less than the number threshold, determining the target image that is not projected as the target image, and continuing to perform the structural line extraction step. In this way, structural lines can be extracted from the image when the corner point is successfully projected on the image, thereby improving the accuracy of the extracted structural lines. In addition, when the number of extracted structural lines is greater than or equal to the preset number threshold, the extraction of structural lines is terminated, thereby saving resources.

[0106] In some optional implementations, the execution subject may determine whether the corner point is successfully projected on the target image in the following manner: the execution subject may determine the distance between the position point representing the corner point in the target image and the projection position point of the corner point on the target image, which may be referred to as the pixel distance; and then, based on the distance, determine whether the corner point is successfully projected on the target image. Here, it may be determined whether the distance is less than a preset pixel distance threshold (e.g., 2 pixels); if so, it may be determined that the corner point is successfully projected on the target image; if not, it may be determined that the corner point fails to be projected on the target image. In this way, it may be more reasonably determined whether the corner point is successfully projected on the image.

[0107] Further references Figure 7 As an implementation of the methods shown in the above figures, the present application provides an embodiment of a house calibration device, and the device embodiment is similar to Figure 1Corresponding to the method embodiment shown, the apparatus can be specifically arranged in an extended reality device.

[0108] like Figure 7 As shown, the house calibration device 700 of this embodiment includes: an acquisition unit 701, a first determination unit 702, a second determination unit 703 and a third determination unit 704. The acquisition unit 701 is used to acquire a house image in response to detecting that a corner point of a house is calibrated, wherein the house image is acquired during the calibration process, and the shape of the house is a target shape; the first determination unit 702 is used to determine the structural lines associated with each corner point based on the house image; the second determination unit 703 is used to determine, for each corner point in the house, the distance between the projection position point of the corner point in the house image and the structural line associated with the corner point in response to detecting that the calibration is finished; the third determination unit 704 is used to determine the coordinate value of each corner point based on the distance corresponding to each corner point.

[0109] In this embodiment, the specific processing of the acquisition unit 701, the first determination unit 702, the second determination unit 703 and the third determination unit 704 of the house calibration device 700 can refer to Figure 1 This corresponds to step 101, step 102, step 103 and step 104 in the embodiment.

[0110] In some optional implementations, the third determination unit 704 may be further used to determine the coordinate value of each corner point based on the distance corresponding to each corner point in the following manner: summing the distances corresponding to each corner point, and using the sum result as an optimization target; and determining the coordinate value of the corner point when the optimization target is minimized.

[0111] In some optional implementations, the projection shape of the house in the horizontal direction is an N-gon, and the coordinate values ​​of the corner points of the N-gon are represented by M parameters, where M is less than N*2.

[0112] In some optional implementations, the projection shape of the house in the horizontal direction is a quadrilateral or a hexagon, wherein the coordinate values ​​of the corner points of the quadrilateral house are represented by 5 parameters, and the coordinate values ​​of the corner points of the hexagonal house are represented by 7 parameters.

[0113] In some optional implementations, the coordinate values ​​of each corner point of the above-mentioned house are determined by the following parameters: the horizontal coordinate value and the vertical coordinate value of the target corner point, the length of at least two adjacent sides of the above-mentioned house, and the angle between the first target side and the horizontal coordinate axis of the preset coordinate system, wherein the above-mentioned target corner point is any corner point of the above-mentioned house, and the above-mentioned first target side is any side of the above-mentioned house.

[0114] In some optional implementations, the house calibration device 700 further includes: a calibration coordinate value acquisition unit (not shown in the figure). The calibration coordinate value acquisition unit can be used to obtain the calibration coordinate value of each corner point in response to detecting that the corner point of the house is calibrated. And the first determination unit 702 can determine the structural line associated with each corner point based on the house image in the following manner: determine the structural line associated with each corner point based on the house image and the calibration coordinate value.

[0115] In some optional implementations, the first determination unit 702 may be further used to determine the structural lines associated with each corner point based on the house image and the calibrated coordinate values ​​in the following manner: for each corner point in the house, perform the following structural line extraction step: using the calibrated coordinate value of the corner point, determine whether the corner point is successfully projected on the target image; if the projection is successful, extract the structural lines associated with the corner point from the target image, wherein the target image is an image of the house image that contains the corner point.

[0116] In some optional implementations, the number of target images is multiple; and the structure line extraction step further includes: determining whether the number of structure lines associated with the corner point is less than a preset number threshold; and the house calibration device 700 may include a feedback unit (not shown in the figure). The feedback unit is used to determine the target image that is not projected as the target image if the number is less than the number threshold, and continue to perform the structure line extraction step.

[0117] In some optional implementations, the structure line extraction step further includes: if the number is greater than or equal to the number threshold, determining that the calibration of the corner point is completed.

[0118] In some optional implementations, the first determination unit 702 may be further used to determine whether the corner point is successfully projected on the target image in the following manner: determine the distance between a position point representing the corner point in the target image and a projection position point of the corner point on the target image; and determine whether the corner point is successfully projected on the target image based on the distance.

[0119] Figure 8 An exemplary system architecture 800 is shown to which embodiments of the house calibration method of the present disclosure may be applied.

[0120] like Figure 8 As shown, the system architecture 800 may include extended reality devices 8011, 8012, 8013, a network 802, and a server 803. The network 802 is used to provide a medium for a communication link between the extended reality devices 8011, 8012, 8013 and the server 803. The network 802 is generally a wireless communication link.

[0121] The user can use the extended reality devices 8011, 8012, 8013 to interact with the server 803 through the network 802 to send or receive messages, etc. For example, the server 803 can receive the structure lines associated with the corner points sent by the extended reality devices 8011, 8012, 8013 and store the information of the structure lines. Various communication client applications can be installed on the extended reality devices 8011, 8012, 8013, such as image processing applications, video shooting applications, game applications, instant messaging software, etc.

[0122] Extended reality devices 8011, 8012, and 8013 can be hardware or software. When extended reality devices 8011, 8012, and 8013 are hardware, they can be various extended reality devices with cameras, display screens, and support information interaction, including but not limited to VR headsets, AR glasses, etc. When extended reality devices 8011, 8012, and 8013 are software, they can be installed in the extended reality devices listed above. It can be implemented as multiple software or software modules (for example, multiple software or software modules used to provide distributed services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0123] In response to detecting that the corner points of a house are calibrated, the extended reality devices 8011, 8012, and 8013 obtain a house image during the calibration process; then, based on the house image, the structural lines associated with each corner point are determined; in response to detecting that the calibration is completed, for each corner point in the house, the distance between the projection position point of the corner point in the house image and the structural line associated with the corner point is determined; then, based on the corresponding distances of each corner point, the coordinate values ​​of each corner point are determined.

[0124] The server 803 may be a server that provides various services, for example, it may be a background server that stores structure line information of structure lines associated with corner points.

[0125] It should be noted that the server 803 can be hardware or software. When the server 803 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server 803 is software, it can be implemented as multiple software or software modules (for example, for providing distributed services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0126] It should also be noted that the house calibration method provided in the embodiment of the present disclosure is usually executed by the extended reality devices 8011, 8012, and 8013, and the house calibration device is usually set in the extended reality devices 8011, 8012, and 8013.

[0127] It should be understood that Figure 8 The number of extended reality devices, networks, and servers in the embodiment is only illustrative. Any number of extended reality devices, networks, and servers may be provided as required.

[0128] Reference below Fig. 9 , which shows a structural schematic diagram of an extended reality device 900 suitable for implementing an embodiment of the present disclosure. Fig. 9 The extended reality device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0129] like Fig. 9 As shown, the extended reality device 900 includes a central processing unit (CPU) 901, a memory 902, an input unit 903, and an output unit 904, wherein the central processing unit 901, the memory 902, the input unit 903, and the output unit 904 are connected to each other via a bus 906. Here, the method according to the embodiment of the present disclosure can be implemented as a computer program and stored in the memory 902. The processing device 901 in the extended reality device 900 specifically implements the anchor point processing function defined in the method of the embodiment of the present disclosure by calling the above-mentioned computer program stored in the memory 902.

[0130] In some implementations, the input unit 903 may be a device such as a camera that can be used to acquire images, and the output unit 904 may be a device such as a display screen that can be used to display the acquired images. Thus, when the processing device 901 calls the above-mentioned computer program to perform the image display function, it may control the input unit 903 to acquire images, and control the output unit 904 to display the acquired images.

[0131] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. It should be noted that the computer-readable medium described in the embodiment of the present disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In an embodiment of the present disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. In an embodiment of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0132] The computer-readable medium may be included in the extended reality device; or it may exist independently without being assembled into the extended reality device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the extended reality device, the extended reality device: in response to detecting that the corner points of the house are calibrated, obtain a house image, wherein the house image is obtained during the calibration process, and the shape of the house is the target shape; based on the house image, determine the structural lines associated with each corner point; in response to detecting that the calibration is completed, for each corner point in the house, determine the distance between the projection position point of the corner point in the house image and the structural line associated with the corner point; based on the distances corresponding to each corner point, determine the coordinate values ​​of each corner point.

[0133] Computer program code for performing the operations of embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on a user's computer, partially on a user's computer, as a separate software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0134] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0135] According to one or more embodiments of the present disclosure, a house calibration method is provided, which is applied to an extended reality device, and the method includes: in response to detecting that a corner point of a house is calibrated, obtaining a house image, wherein the house image is obtained during the calibration process, and the shape of the house is a target shape; based on the house image, determining a structural line associated with each corner point; in response to detecting that the calibration is finished, determining, for each corner point in the house, a distance between a projection position point of the corner point in the house image and a structural line associated with the corner point; and determining a coordinate value of each corner point based on the distance corresponding to each corner point.

[0136] According to one or more embodiments of the present disclosure, based on the distances corresponding to each corner point, the coordinate values ​​of each corner point are determined, including: summing the distances corresponding to each corner point and taking the summation result as an optimization target; and determining the coordinate values ​​of the corner points when the above optimization target is minimized.

[0137] According to one or more embodiments of the present disclosure, the projection shape of the house in the horizontal direction is an N-gon, and the coordinate values ​​of the corner points of the N-gon are characterized by M parameters, where M is less than N*2.

[0138] According to one or more embodiments of the present disclosure, the projection shape of the house in the horizontal direction is a quadrilateral or a hexagon, wherein the coordinate values ​​of the corner points of the quadrilateral house are represented by 5 parameters, and the coordinate values ​​of the corner points of the hexagonal house are represented by 7 parameters.

[0139] According to one or more embodiments of the present disclosure, the coordinate values ​​of each corner point of the above-mentioned house are determined by the following parameters: the horizontal coordinate value and the vertical coordinate value of the target corner point, the length of at least two adjacent sides of the above-mentioned house, and the angle between the first target side and the horizontal coordinate axis of the preset coordinate system, wherein the above-mentioned target corner point is any corner point of the above-mentioned house, and the above-mentioned first target side is any side of the above-mentioned house.

[0140] According to one or more embodiments of the present disclosure, the method further includes: in response to detecting that the corner points of the house are calibrated, obtaining the calibration coordinate values ​​of each corner point; and based on the above-mentioned house image, determining the structural lines associated with each corner point, including: based on the above-mentioned house image and the above-mentioned calibration coordinate values, determining the structural lines associated with each corner point.

[0141] According to one or more embodiments of the present disclosure, based on the above-mentioned house image and the above-mentioned calibrated coordinate values, the structural lines associated with each corner point are determined, including: for each corner point in the above-mentioned house, the following structural line extraction steps are performed: using the calibrated coordinate value of the corner point, it is determined whether the corner point is successfully projected on the target image, and if the projection is successful, the structural line associated with the corner point is extracted from the target image, wherein the target image is an image containing the corner point in the above-mentioned house image.

[0142] According to one or more embodiments of the present disclosure, there are multiple target images; and the above-mentioned structure line extraction step also includes: determining whether the number of structure lines associated with the corner point is less than a preset number threshold; and if the above-mentioned number is less than the above-mentioned number threshold, the target image that is not projected is determined as the target image, and the above-mentioned structure line extraction step is continued.

[0143] According to one or more embodiments of the present disclosure, the structure line extraction step further includes: if the number is greater than or equal to the number threshold, determining that the calibration of the corner point is completed.

[0144] According to one or more embodiments of the present disclosure, determining whether the corner point is successfully projected on the target image includes: determining the distance between a position point representing the corner point in the target image and a projection position point of the corner point on the target image; and determining whether the corner point is successfully projected on the target image based on the above distance.

[0145] According to one or more embodiments of the present disclosure, there is provided a house calibration apparatus, which is arranged in an extended reality device, and the apparatus comprises: an acquisition unit, for acquiring a house image in response to detecting that a corner point of a house is calibrated, wherein the house image is acquired during the calibration process, and the shape of the house is a target shape; a first determination unit, for determining, based on the house image, a structural line associated with each corner point; a second determination unit, for determining, for each corner point in the house, a distance between a projection position point of the corner point in the house image and a structural line associated with the corner point in response to detecting that the calibration is finished; and a third determination unit, for determining, based on the distances corresponding to the corner points, a coordinate value of each corner point.

[0146] According to one or more embodiments of the present disclosure, the third determination unit is further used to determine the coordinate value of each corner point based on the distance corresponding to each corner point in the following manner: summing the distances corresponding to each corner point, and using the summation result as an optimization target; determining the coordinate value of the corner point when the above optimization target is minimized.

[0147] According to one or more embodiments of the present disclosure, the projection shape of the house in the horizontal direction is an N-gon, and the coordinate values ​​of the corner points of the N-gon are characterized by M parameters, where M is less than N*2.

[0148] According to one or more embodiments of the present disclosure, the projection shape of the house in the horizontal direction is a quadrilateral or a hexagon, wherein the coordinate values ​​of the corner points of the quadrilateral house are represented by 5 parameters, and the coordinate values ​​of the corner points of the hexagonal house are represented by 7 parameters.

[0149] According to one or more embodiments of the present disclosure, the coordinate values ​​of each corner point of the above-mentioned house are determined by the following parameters: the horizontal coordinate value and the vertical coordinate value of the target corner point, the length of at least two adjacent sides of the above-mentioned house, and the angle between the first target side and the horizontal coordinate axis of the preset coordinate system, wherein the above-mentioned target corner point is any corner point of the above-mentioned house, and the above-mentioned first target side is any side of the above-mentioned house.

[0150] According to one or more embodiments of the present disclosure, the house calibration device further includes: a calibration coordinate acquisition unit for acquiring calibration coordinate values ​​of each corner point in response to detecting that a corner point of a house is calibrated; and the first determination unit is further used to determine the structural lines associated with each corner point based on the house image in the following manner: determining the structural lines associated with each corner point based on the house image and the calibration coordinate values.

[0151] According to one or more embodiments of the present disclosure, the first determination unit is further used to determine the structural lines associated with each corner point based on the house image and the calibrated coordinate values ​​in the following manner: for each corner point in the house, perform the following structural line extraction step: use the calibrated coordinate value of the corner point to determine whether the corner point is successfully projected on the target image; if the projection is successful, extract the structural line associated with the corner point from the target image, wherein the target image is an image containing the corner point in the house image.

[0152] According to one or more embodiments of the present disclosure, there are multiple target images; and the above-mentioned structure line extraction step also includes: determining whether the number of structure lines associated with the corner point is less than a preset number threshold; and the above-mentioned house calibration device also includes a feedback unit, which is used to determine the target image that is not projected as the target image if the above-mentioned number is less than the above-mentioned number threshold, and continue to execute the above-mentioned structure line extraction step.

[0153] According to one or more embodiments of the present disclosure, the structure line extraction step further includes: if the number is greater than or equal to the number threshold, determining that the calibration of the corner point is completed.

[0154] According to one or more embodiments of the present disclosure, the first determination unit is further used to determine whether the corner point is successfully projected on the target image in the following manner: determine the distance between a position point representing the corner point in the target image and a projection position point of the corner point on the target image; based on the distance, determine whether the corner point is successfully projected on the target image.

[0155] The units involved in the embodiments described in the present disclosure may be implemented by software or by hardware. The described units may also be arranged in a processor, for example, may be described as: a processor includes an acquisition unit, a first determination unit, a second determination unit, and a third determination unit. The names of these units do not constitute a limitation on the units themselves in certain cases, for example, the first determination unit may also be described as "a unit for determining structural lines associated with each corner point based on a house image".

[0156] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) to form a technical solution.

Claims

1. A house calibration method, applied to an extended reality device, characterized in that: include: In response to detecting that a corner point of a house is calibrated, an image of the house is acquired, wherein the image of the house is acquired during the calibration process, and the shape of the house is a target shape; Based on the house image, determining a structural line associated with each corner point; In response to detecting that the calibration is finished, determining, for each corner point in the house, a distance between a projection position point of the corner point in the house image and a structural line associated with the corner point; Based on the distances corresponding to the corner points, the coordinate values ​​of the corner points are determined.

2. The method according to claim 1, characterized in that The step of determining the coordinate value of each corner point based on the distance corresponding to each corner point includes: Sum the distances corresponding to each corner point, and use the sum result as the optimization target; Determine the coordinate value of the corner point when the optimization objective is minimized.

3. The method according to claim 2, characterized in that The projection shape of the house in the horizontal direction is an N-gon, and the coordinate values ​​of the corner points of the N-gon are represented by M parameters, where M is less than N*2.

4. The method according to claim 3, characterized in that The projection shape of the house in the horizontal direction is a quadrilateral or a hexagon, wherein the coordinate values ​​of the corner points of the quadrilateral house are represented by 5 parameters, and the coordinate values ​​of the corner points of the hexagonal house are represented by 7 parameters.

5. The method according to claim 1, characterized in that The coordinate values ​​of each corner point of the house are determined by the following parameters: the horizontal coordinate value and the vertical coordinate value of the target corner point, the length of at least two adjacent sides of the house, and the angle between the first target side and the horizontal coordinate axis of the preset coordinate system, wherein the target corner point is any corner point of the house, and the first target side is any side of the house.

6. The method according to claim 1, characterized in that The method further comprises: In response to detecting that the corner points of the house are calibrated, obtaining the calibration coordinate values ​​of each corner point; and The step of determining the structural line associated with each corner point based on the house image includes: Based on the house image and the calibrated coordinate values, a structural line associated with each corner point is determined.

7. The method according to claim 6, characterized in that The step of determining the structural line associated with each corner point based on the house image and the calibrated coordinate value includes: For each corner point in the house, the following structural line extraction steps are performed: using the calibrated coordinate value of the corner point, it is determined whether the corner point is successfully projected on the target image; if the projection is successful, the structural line associated with the corner point is extracted from the target image, wherein the target image is an image of the house image that contains the corner point.

8. The method according to claim 7, characterized in that The number of target images is multiple; and The structure line extraction step further includes: determining whether the number of structure lines associated with the corner point is less than a preset number threshold; and If the number is less than the number threshold, the target image that is not projected is determined as the target image, and the structure line extraction step is continued.

9. The method according to claim 8, characterized in that The structure line extraction step further includes: if the number is greater than or equal to the number threshold, determining that the calibration of the corner point is completed.

10. The method according to claim 7, characterized in that The step of determining whether the corner point is successfully projected onto the target image comprises: Determine the distance between a position point representing the corner point in the target image and a projection position point of the corner point on the target image; Based on the distance, it is determined whether the corner point is successfully projected on the target image.

11. A house calibration device, arranged in an extended reality device, characterized in that: include: an acquisition unit, configured to acquire a house image in response to detecting and calibrating a corner point of a house, wherein the house image is acquired during the calibration process, and the shape of the house is a target shape; A first determining unit, configured to determine a structural line associated with each corner point based on the house image; A second determination unit is configured to determine, in response to detecting that the calibration is finished, for each corner point in the house, a distance between a projection position point of the corner point in the house image and a structural line associated with the corner point; The third determining unit is used to determine the coordinate value of each corner point based on the distance corresponding to each corner point.

12. An extended reality device, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 10.

13. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 10 is implemented.

Images