A method and system for virtual-reality interaction of naked-eye 3D large screen

Abstract

The present invention discloses a method and system for virtual-reality interaction of a naked-eye 3D large screen, relating to the technical field of large-screen interaction. The present invention introduces a three-dimensional evaluation system of crosstalk distance value, crosstalk discrete value and crosstalk deviation value, normalizes the three-dimensional indicators and calculates the crosstalk general excellence value, selects a screening inclination angle with a higher crosstalk general excellence value as the optimal cylindrical mirror grating inclination angle of the corresponding grid unit, takes into account the crosstalk rate level, fluctuation stability and high interference occurrence frequency, and solves the problem in the prior art of only screening parameters based on a single indicator of average crosstalk rate, without considering the stability and reliability of the crosstalk rate.

Description

Technical Field

[0001] The present invention relates to the field of large-screen interaction technology, and in particular to a method and system for virtual-reality interaction of a naked-eye 3D large screen. Background Art

[0002] With the continuous development of display technology, naked-eye 3D large screens have been increasingly widely used in advertising display, entertainment experience, education and training, etc. because they can provide users with 3D visual effects without wearing special glasses.

[0003] However, the current interactive methods for naked-eye 3D large screens still have the following deficiencies in practical applications:

[0004] Traditional methods for parallax compensation and spectroscopic parameter optimization typically screen parameters based solely on the average crosstalk ratio, without considering its stability and reliability. For example, at a certain tilt angle, the average crosstalk ratio may be low, but fluctuate dramatically or occasionally exhibit high crosstalk. A single metric cannot identify such issues, resulting in unstable display effects.

[0005] Without an intelligent caching mechanism, each interaction requires real-time calculation of spectroscopic parameters, resulting in high response delays. Especially in scenarios with multiple people and high frequency of interaction, competition for computing resources intensifies and delays increase significantly.

[0006] To this end, a method and system for virtual-reality interaction of a naked-eye 3D large screen are introduced. Summary of the Invention

[0007] In view of this, the present invention provides a method and system for virtual-reality interaction of a naked-eye 3D large screen to solve the problems raised by the above background technology.

[0008] The purpose of the present invention can be achieved by the following technical solution: A method for virtual-reality interaction of a naked-eye 3D large screen, comprising:

[0009] User position tracking: Utilizes a camera array pre-placed around the large screen to capture the user's visual images in real time, and simultaneously activates the TOF depth sensor to obtain human body depth images and RGB images. The system then calculates the six degrees of freedom parameters of the user's head, namely the three-dimensional coordinates (X, Y, Z) of the head and head posture data; head posture data includes pitch angle, yaw angle, and roll angle.

[0010] Viewing area boundary division: The stereoscopic display effective area in the 3D interactive space in front of the large screen is divided into 20×15 grid units according to the set division mechanism;

[0011] Real-time parallax compensation: Utilizes spectroscopic parameter calibration logic to determine the optimal spectroscopic parameters for each grid cell for different users. The system then identifies the grid cell corresponding to the current user's location in real time, extracts the optimal spectroscopic parameters for each grid cell corresponding to the current user, and dynamically adjusts them. The spectroscopic parameters include the lenticular lens tilt angle and microlens focal length.

[0012] Spectroscopic parameter cache: Analyzes the application data of spectral parameter combinations within a set time window and establishes a cache queue based on the analysis results.

[0013] In some embodiments, the set division mechanism is specifically:

[0014] With the center of the large screen as the origin, establish a right-handed coordinate system, that is, the X-axis is horizontal to the right, the Y-axis is vertically upward, and the Z-axis is perpendicular to the large screen and forward. Divide the space along the X-axis into 20 equal parts and the Y-axis into 15 equal parts, forming 20×15 horizontal grid layers perpendicular to the Z-axis.

[0015] In some embodiments, the use of spectroscopic parameter calibration logic to determine the optimal lenticular lens tilt angle for each grid unit for different users is specifically:

[0016] For the center point of each grid unit, the left-right eye image crosstalk rate of different users at different tilt angles is collected k times by rotating the cylindrical grating.

[0017] Calculate the average value of the left-eye and right-eye crosstalk rates of k times for different users at different tilt angles to determine the average crosstalk rates of different users at different tilt angles;

[0018] Eliminate the tilt angles whose average crosstalk rate is higher than the set reference rate, mark the remaining tilt angles as screening tilt angles, and integrate them into a tilt angle data set of different users;

[0019] Extract the average crosstalk rate of different users at different screening tilt angles from the tilt angle data set, calculate the difference between the average crosstalk rate and the set reference rate, and take the absolute value as the crosstalk distance value tra for different screening tilt angles; use the standard deviation formula to calculate the crosstalk rate of the left and right eye images at different screening tilt angles k times, and obtain the crosstalk discrete value trb corresponding to different screening tilt angles;

[0020] Identify the crosstalk rate of the left and right eye images at different screening tilt angles k times, count the number of times higher than the set reference rate as the high-disturbance number, calculate the ratio of the high-disturbance number to k, and obtain the crosstalk deviation value trc corresponding to different screening tilt angles;

[0021] Extract the crosstalk distance value tra, crosstalk discrete value trb and crosstalk deviation value trc calculated by different users corresponding to different screening tilt angles, perform normalization and then enter the formula Perform weighted calculation to obtain the crosstalk performance value tq of different users corresponding to different screening tilt angles; are the weight coefficients of the crosstalk distance value tra, the crosstalk discrete value trb, and the crosstalk deviation value trc respectively;

[0022] For different users, the screening tilt angle with a higher crosstalk general merit value tq is selected as the optimal cylindrical mirror grating tilt angle of the corresponding grid unit, which is marked as ; i is the X-axis grid index, j is the Y-axis grid index.

[0023] In some embodiments, the use of spectroscopic parameter calibration logic to determine the optimal microlens focal length for each grid unit for different users is specifically:

[0024] For the center point of each grid cell, the theoretical focal length is calculated according to the object distance-image distance formula, which is expressed as ; where v is the distance from the display screen to the microlens, and u is the vertical distance from the user's eyes to the microlens in each grid unit; for different users, the calculated As the optimal microlens focal length of the corresponding grid unit.

[0025] In some embodiments, the specific steps of establishing the cache queue are:

[0026] Extract the usage count of each spectral parameter combination from the usage data of the spectral parameter combination within a set time window; identify the usage duration of each spectral parameter combination, and determine the record with a usage duration less than the set duration as a false trigger; eliminate the false trigger usage count of the spectral parameter combination; and count the usage count of each spectral parameter combination after elimination within the set time window and record it as a reference count;

[0027] Sum the reference times of each spectral parameter combination to obtain the total times within the set time window; calculate the proportion of the reference times corresponding to each spectral parameter combination in the total times as the usage frequency proportion of each spectral parameter combination in the set time window;

[0028] Extract the usage time of each spectral parameter combination corresponding to each reference number, sum the usage time of each group of each spectral parameter combination, obtain the cumulative usage time of each spectral parameter combination within the set time window, calculate the proportion of the cumulative usage time of each spectral parameter combination within the set time window, and use it as the usage time proportion of each spectral parameter combination within the set time window;

[0029] Identify the number of users using each spectral parameter combination within a set time window, and calculate the proportion of the number of users using each spectral parameter combination to the total number of users as the user concentration proportion of each spectral parameter combination within the set time window;

[0030] Multiply the usage frequency ratio, usage duration ratio, and user concentration ratio of each spectral parameter combination by the preset frequency weight coefficient, duration weight coefficient, and concentration weight coefficient respectively, and then sum them up to obtain the priority index of each spectral parameter combination within the set time window;

[0031] The optical splitting parameter combinations are sorted from large to small according to the priority index, and the top H optical splitting parameter combinations are extracted from the sorting result from left to right to construct a cache queue.

[0032] In some embodiments, further comprising:

[0033] Flexible resource allocation: Real-time evaluation of user activity parameters, including movement speed, interaction frequency, and gaze duration, is used to adjust the user's GPU computing resources based on the evaluation results.

[0034] In some embodiments, the specific process of real-time evaluation of user activity parameters is as follows:

[0035] Calculate the Euclidean distance between adjacent time frames using the user's head 3D coordinates (X, Y, Z) The calculation formula is expressed as ; Calculate the average of the Euclidean distances of each adjacent time frame of the user within the preset time interval as the user's movement speed within the preset time interval;

[0036] Count the number of times a user triggers a valid interaction event within a preset time interval; record this as the number of interactions; calculate the ratio of the number of interactions to the preset time interval to obtain the user's interaction frequency within the preset time interval;

[0037] Determine that the user's gaze area is the large screen. When the user's head posture is stable, that is, the pitch angle and yaw angle are lower than the set angles, and the duration is longer than the set time, it is determined to be effective gaze; count the user's effective gaze time within the preset time interval;

[0038] Convert the user's movement speed, interaction frequency, and effective gaze duration within a preset time interval into a speed activity score, an interaction activity score, and a gaze activity score, respectively;

[0039] The user's speed activity score, interaction activity score, and gaze activity score are multiplied by the set speed weight coefficient, interaction weight coefficient, and gaze weight coefficient respectively, and then the sum is calculated to obtain the user's activity evaluation index.

[0040] In some embodiments, the user's movement speed, interaction frequency, and effective gaze duration within a preset time interval are converted into a speed activity score, an interaction activity score, and a gaze activity score, respectively, as follows:

[0041] Construct the speed set, frequency set and gaze set corresponding to the movement speed, interaction frequency and effective gaze duration respectively;

[0042] The speed set, frequency set, and gaze set each contain a group of speed value ranges, a group of frequency value ranges, and a group of duration value ranges; and each group of speed value ranges corresponds to a group of speed activity scores; each group of frequency value ranges corresponds to a group of interaction activity scores; and each group of duration value ranges corresponds to a group of gaze activity scores;

[0043] The user's movement speed, interaction frequency, and effective gaze duration are respectively input into the speed set, frequency set, and gaze set to match the corresponding value ranges, thereby converting them into the corresponding degree activity score, interaction activity score, and gaze activity score.

[0044] In some embodiments, adjusting the user's GPU computing resources based on the evaluation results is specifically:

[0045] Get the current total GPU resources ,and ,in Represents the total number of threads and total amount of video memory respectively;

[0046] Based on the active score index of each user, the total GPU resources are allocated, that is, through the allocation logic Get the threads allocated to each user in the next preset time interval and video memory ;in Represents the active rating index of each user, i is the user number; It is the sum of the activity rating index of each user.

[0047] A naked-eye 3D large-screen virtual-reality interaction system comprising:

[0048] Data acquisition module: acquires user visual images, human body depth images, and RGB images, and calculates the six-degree-of-freedom parameters of the user's head, namely the three-dimensional coordinates (X, Y, Z) of the head and head posture data;

[0049] Viewing area division module: divides the stereoscopic display effective area in the 3D interactive space in front of the large screen into 20×15 grid units;

[0050] Parallax Compensation Module: This module uses spectroscopic parameter calibration logic to determine the optimal spectroscopic parameters for each grid cell for different users. It then identifies the grid cell corresponding to the current user's location in real time, extracts the optimal spectroscopic parameters for each grid cell corresponding to the current user, and dynamically adjusts them. The spectroscopic parameters include the lenticular lens tilt angle and the microlens focal length.

[0051] Cache setting module: analyzes the application data of the spectral parameter combination within the set time window and establishes a cache queue based on the analysis results;

[0052] Resource Scheduling Module: This module evaluates user activity parameters in real time and adjusts the user's GPU computing resources based on the evaluation results. Activity parameters include user movement speed, interaction frequency, and gaze duration.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The present invention introduces a three-dimensional evaluation system of crosstalk distance, crosstalk discrete value, and crosstalk deviation value. After normalizing the three-dimensional indicators, the crosstalk general excellence value is calculated. The screening tilt angle with a higher crosstalk general excellence value is selected as the optimal cylindrical lens grating tilt angle for the corresponding grid unit. This takes into account the crosstalk rate level, fluctuation stability, and high crosstalk frequency. This solves the problem of the existing technology that only selects parameters based on a single indicator of average crosstalk rate without considering the stability and reliability of the crosstalk rate.

[0055] The present invention establishes a multi-dimensional cache queue. First, it determines that records with usage durations lower than the set duration are false triggers and filters them. It then calculates the priority index by counting usage frequency, usage duration, and user concentration. It extracts the top H records and caches them in advance to the edge node, ensuring that the cached data truly reflects user needs within the current set time window. It captures the temporal patterns of user behavior and loads them into the cache in advance, which can significantly improve the interaction delay between users and the large screen.

[0056] The present invention evaluates user activity in real time, performs multi-dimensional analysis based on movement speed, interaction frequency, and gaze duration, obtains the user's activity score index, and dynamically allocates GPU resources based on the activity score index, thereby improving hardware resource utilization efficiency and user interaction experience satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Further details, features and advantages of the present application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0058] Figure 1 is a flow chart of the present invention;

[0059] Figure 2 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0060] Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings so that those skilled in the art can implement the present application. The present application can be embodied in many different forms and for many different purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the present application comprehensive and complete and to fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0061] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the relevant art and / or the context of this specification, and will not be interpreted in an idealized or overly formal sense unless expressly defined as such herein.

[0062] Example 1

[0063] See also Figure 1 As shown, a method for virtual-reality interaction of a naked-eye 3D large screen includes:

[0064] User location tracking: Utilizing a camera array pre-placed around the large screen to capture user visual images in real time, the system simultaneously activates the TOF depth sensor to obtain human body depth and RGB images. The system then fuses inertial navigation data using an extended Kalman filter algorithm to calculate the user's head's six degrees of freedom (DOF) parameters, including the head's three-dimensional coordinates (X, Y, Z) and head posture data (pitch, yaw, and roll). The positioning refresh rate reaches 100Hz.

[0065] Supplementary information: 8 sets of binocular structured light cameras (horizontally spaced 45°, vertical height 1.2-1.8m); user depth image (resolution 640×480, frame rate 120fps).

[0066] Viewing area boundary division: The stereoscopic display effective area in the 3D interactive space in front of the large screen is divided into 20×15 grid units according to the set division mechanism;

[0067] Supplementary explanation, the specific division process of the grid unit, for example: with the center of the large screen as the origin, establish a right-handed coordinate system (X axis horizontal to the right, Y axis vertically upward, Z axis perpendicular to the large screen forward), the interactive space range is defined as , divide the space into 20 equal parts along the X axis and 15 equal parts along the Y axis, forming 20×15 horizontal grid layers perpendicular to the Z axis.

[0068] Real-time parallax compensation: Utilizes spectroscopic parameter calibration logic to determine the optimal spectroscopic parameters for each grid cell for different users. The system then identifies the grid cell corresponding to the current user's location in real time, extracts the optimal spectroscopic parameters for each grid cell corresponding to the current user, and dynamically adjusts them. The spectroscopic parameters include the lenticular lens tilt angle and microlens focal length.

[0069] The specific calculation process of the spectroscopic parameter calibration logic is as follows:

[0070] For the center point of each grid unit, the cylindrical grating is rotated; the angle is adjustable from 0° to 30°, with a step of 0.1°; the left and right eye image crosstalk rate of different users at different tilt angles is collected k times, and k>12. The specific number is initially set by the technicians.

[0071] Calculate the average value of the left-eye and right-eye crosstalk rates of k times for different users at different tilt angles to determine the average crosstalk rates of different users at different tilt angles;

[0072] Eliminate the tilt angles whose average crosstalk rate is higher than the set reference rate, mark the remaining tilt angles as screening tilt angles, and integrate them into a tilt angle data set of different users;

[0073] Extract the average crosstalk rate of different users at different screening tilt angles from the tilt angle data set, calculate the difference between the average crosstalk rate and the set reference rate, and take the absolute value as the crosstalk distance value tra at different screening tilt angles;

[0074] Measure the extent to which the crosstalk rate at different screening tilt angles is lower than the set reference rate;

[0075] The standard deviation formula is used to calculate the crosstalk rate of the left and right eye images at different screening tilt angles k times, and the crosstalk discrete value trb corresponding to different screening tilt angles is obtained;

[0076] Measure the stability of crosstalk rate at different screening tilt angles;

[0077] Identify the crosstalk rate of the left and right eye images at different screening tilt angles k times, count the number of times higher than the set reference rate as the high-disturbance number, calculate the ratio of the high-disturbance number to k, and obtain the crosstalk deviation value trc corresponding to different screening tilt angles;

[0078] Extract the crosstalk distance value tra, crosstalk discrete value trb and crosstalk deviation value trc calculated by different users corresponding to different screening tilt angles, perform normalization and then enter the formula Perform weighted calculation to obtain the crosstalk performance value tq of different users corresponding to different screening tilt angles; are the weight coefficients of the crosstalk distance value tra, the crosstalk discrete value trb, and the crosstalk deviation value trc respectively;

[0079] For different users, the screening tilt angle with a higher crosstalk general merit value tq is selected as the optimal cylindrical mirror grating tilt angle of the corresponding grid unit, which is marked as ; i is the X-axis grid index, j is the Y-axis grid index;

[0080] In addition, traditional methods usually screen parameters based on a single indicator, average crosstalk rate, without considering the stability (such as the fluctuation range) and reliability (such as the frequency of high crosstalk) of the crosstalk rate. For example, a certain tilt angle may have a low average crosstalk rate, but occasionally high crosstalk (such as flickering or ghosting) may occur. A single indicator cannot identify such problems.

[0081] For the center point of each grid cell, the theoretical focal length is calculated according to the object distance-image distance formula, which is expressed as Where v is the distance from the display to the microlens, and u is the vertical distance from the user's eye to the microlens in each grid cell; obtained through the depth sensor;

[0082] Supplementary explanation: if If the focal length of the microlens is not within the adjustable range, the boundary value of the adjustable range is directly taken. For example, if the focal length of the microlens is adjustable within the range of 2mm-5mm, <2, then directly take 2mm, if >5, then directly take 5mm.

[0083] For different users, the calculated as the optimal microlens focal length for the corresponding grid cell;

[0084] Spectral parameter cache: Analyzes the application data of spectral parameter combinations within a set time window and establishes a cache queue based on the analysis results;

[0085] Specifically:

[0086] Extract the usage count of each spectral parameter combination from the usage data of the spectral parameter combination within a set time window; identify the usage duration of each spectral parameter combination, and determine the record with a usage duration shorter than the set duration as a false trigger; the set duration can be set to 50ms; eliminate the false trigger usage count of the spectral parameter combination; count the usage count of each spectral parameter combination after elimination within the set time window and record it as a reference count;

[0087] Sum the reference times of each spectral parameter combination to obtain the total times within the set time window; calculate the proportion of the reference times corresponding to each spectral parameter combination in the total times as the usage frequency proportion of each spectral parameter combination in the set time window;

[0088] Extract the usage time of each spectral parameter combination corresponding to each reference number, sum the usage time of each group of each spectral parameter combination, obtain the cumulative usage time of each spectral parameter combination within the set time window, calculate the proportion of the cumulative usage time of each spectral parameter combination within the set time window, and use it as the usage time proportion of each spectral parameter combination within the set time window;

[0089] Identify the number of users using each spectral parameter combination within a set time window, and calculate the proportion of the number of users using each spectral parameter combination to the total number of users as the user concentration proportion of each spectral parameter combination within the set time window;

[0090] Multiply the usage frequency ratio, usage duration ratio, and user concentration ratio of each spectral parameter combination by the preset frequency weight coefficient, duration weight coefficient, and concentration weight coefficient respectively, and then sum them up to obtain the priority index of each spectral parameter combination within the set time window;

[0091] Sort the spectral parameter combinations from large to small according to the priority index, extract the first H spectral parameter combinations from left to right from the sorting result, and build a cache queue; H is initially set to 10;

[0092] In addition, through time filtering and multi-dimensional statistics, we can ensure that the cached data truly reflects the user needs within the currently set time window, capture the timing patterns of user behavior and load it into the cache in advance, which can significantly improve the interaction delay between users and the large screen.

[0093] Flexible resource allocation: Real-time evaluation of user activity parameters, including movement speed, interaction frequency, and gaze duration, is used to adjust the user's GPU computing resources based on the evaluation results.

[0094] Specifically:

[0095] Calculate the Euclidean distance between adjacent time frames using the user's head 3D coordinates (X, Y, Z) ;Interval 10ms; calculation formula is expressed as ; Calculate the average of the Euclidean distances of each adjacent time frame of the user within the preset time interval as the user's movement speed within the preset time interval;

[0096] Count the number of valid interaction events triggered by the user within the preset time interval. Interaction events include gesture operations, voice commands, and eye movement selections; record this as the number of interactions; calculate the ratio of the number of interactions to the preset time interval to obtain the user's interaction frequency within the preset time interval;

[0097] Through eye tracking, the user's gaze area is determined to be the large screen. When the user's head posture is stable, that is, the pitch and yaw angles are lower than the set angles and the duration is longer than the set time, it is determined to be effective gaze; the user's effective gaze time within the preset time interval is counted;

[0098] Convert the user's movement speed, interaction frequency, and effective gaze duration within a preset time interval into a speed activity score, an interaction activity score, and a gaze activity score, respectively;

[0099] The user's speed activity score, interaction activity score, and gaze activity score are multiplied by the set speed weight coefficient, interaction weight coefficient, and gaze weight coefficient respectively, and then the sum is calculated to obtain the user's activity evaluation index;

[0100] In addition, the activity evaluation index can be used to distinguish between highly active users and less active users. Highly active users (such as users who move quickly and interact frequently) can be given priority to obtain more GPU resources, meeting extremely high real-time requirements, improving hardware resource utilization efficiency and user interaction experience satisfaction.

[0101] Construct the speed set, frequency set and gaze set corresponding to the movement speed, interaction frequency and effective gaze duration respectively;

[0102] The speed set, frequency set, and gaze set each contain a set of speed value ranges, a set of frequency value ranges, and a set of duration value ranges; each set of speed value ranges corresponds to a set of speed activity scores; each set of frequency value ranges corresponds to a set of interaction activity scores; each set of duration value ranges corresponds to a set of gaze activity scores; the speed activity scores, interaction activity scores, and gaze activity scores are all set in the range of 1-10, and higher movement speed, interaction frequency, and effective gaze duration correspond to higher matching speed activity scores, interaction activity scores, and gaze activity scores;

[0103] The user's movement speed, interaction frequency, and effective gaze duration are input into the speed set, frequency set, and gaze set respectively, and matched to the corresponding value ranges, thereby converting them into corresponding degree activity scores, interaction activity scores, and gaze activity scores;

[0104] Supplementary explanation: the speed value ranges of each group, the frequency value ranges of each group, and the duration value ranges of each group in the speed set, frequency set, and gaze set are initially set by technical personnel.

[0105] Get the current total GPU resources ,and ,in Represents the total number of threads and total amount of video memory respectively;

[0106] Based on the active score index of each user, the total GPU resources are allocated, that is, through the allocation logic Get the threads allocated to each user in the next preset time interval and video memory ;in Represents the active rating index of each user, i is the user number; It is the sum of the activity rating index of each user.

[0107] Example 2

[0108] See also Figure 2 As shown, based on the method for virtual-reality interaction with a naked-eye 3D large screen provided in Example 1 of this application, Example 2 of this application proposes a virtual-reality interaction system with a naked-eye 3D large screen. Example 2 is merely a preferred embodiment of Example 1, and the implementation of Example 2 will not affect the independent implementation of Example 1.

[0109] Specifically, the difference of the naked-eye 3D large-screen virtual-reality interaction system provided in Example 2 of the present application is that it includes a data acquisition module, a viewing area division module, a parallax compensation module, a cache setting module, and a resource scheduling module;

[0110] The data acquisition module is used to obtain the user's visual image, human body depth image and RGB image, and calculate the six degrees of freedom parameters of the user's head, namely the three-dimensional coordinates (X, Y, Z) of the head and the head posture data;

[0111] The viewing area division module is used to divide the stereoscopic display effective area in the 3D interactive space in front of the large screen into 20×15 grid units;

[0112] The parallax compensation module uses spectroscopic parameter calibration logic to determine the optimal spectroscopic parameters for each grid cell for different users. It then identifies the grid cell corresponding to the current user's location in real time, extracts the optimal spectroscopic parameters for each grid cell corresponding to the current user, and dynamically adjusts them. The spectroscopic parameters include the lenticular lens tilt angle and the microlens focal length.

[0113] The cache setting module is used to analyze the application data of the spectral parameter combination within the set time window and establish a cache queue based on the analysis results;

[0114] The resource scheduling module is used to evaluate user activity parameters in real time and adjust the user's GPU computing resources based on the evaluation results. Activity parameters include user movement speed, interaction frequency, and gaze duration.

[0115] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for virtual-reality interaction of a naked-eye 3D large screen, characterized in that: include: User position tracking: Utilizes a camera array pre-placed around the large screen to capture the user's visual images in real time, and simultaneously activates the TOF depth sensor to obtain human body depth images and RGB images. The system then calculates the six degrees of freedom parameters of the user's head, namely the three-dimensional coordinates (X, Y, Z) of the head and head posture data; head posture data includes pitch angle, yaw angle, and roll angle. Viewing area boundary division: The stereoscopic display effective area in the 3D interactive space in front of the large screen is divided into 20×15 grid units according to the set division mechanism; Real-time parallax compensation: Utilizes spectroscopic parameter calibration logic to determine the optimal spectroscopic parameters for each grid cell for different users. The system then identifies the grid cell corresponding to the current user's location in real time, extracts the optimal spectroscopic parameters for each grid cell corresponding to the current user, and dynamically adjusts them. The spectroscopic parameters include the lenticular lens tilt angle and microlens focal length. The optimal spectroscopic parameters for each grid unit are determined by using the spectroscopic parameter calibration logic. Specifically, For the center point of each grid unit, the left-right eye image crosstalk rate of different users at different tilt angles is collected k times by rotating the cylindrical grating. Calculate the average value of the left-eye and right-eye crosstalk rates of k times for different users at different tilt angles to determine the average crosstalk rates of different users at different tilt angles; Eliminate the tilt angles whose average crosstalk rate is higher than the set reference rate, mark the remaining tilt angles as screening tilt angles, and integrate them into a tilt angle data set of different users; Extract the average crosstalk rate of different users at different screening tilt angles from the tilt angle data set, calculate the difference between the average crosstalk rate and the set reference rate, and take the absolute value as the crosstalk distance value tra for different screening tilt angles; use the standard deviation formula to calculate the crosstalk rate of the left and right eye images at different screening tilt angles k times, and obtain the crosstalk discrete value trb corresponding to different screening tilt angles; Identify the crosstalk rate of the left and right eye images at different screening tilt angles k times, count the number of images that are higher than the set reference rate as the high-disturbance number, calculate the ratio of the high-disturbance number to k, and obtain the crosstalk deviation value trc corresponding to different screening tilt angles; Extract the crosstalk distance value tra, crosstalk discrete value trb and crosstalk deviation value trc calculated by different users corresponding to different screening tilt angles, perform normalization and then enter the formula Perform weighted calculation to obtain the crosstalk performance value tq of different users corresponding to different screening tilt angles; are the weight coefficients of the crosstalk distance value tra, the crosstalk discrete value trb, and the crosstalk deviation value trc respectively; For different users, the screening tilt angle with a higher crosstalk general merit value tq is selected as the optimal cylindrical mirror grating tilt angle of the corresponding grid unit, which is marked as ; i is the X-axis grid index, j is the Y-axis grid index; Spectroscopic parameter cache: Analyzes the application data of spectral parameter combinations within a set time window and establishes a cache queue based on the analysis results.

2. The method for virtual-reality interaction of a naked-eye 3D large screen according to claim 1, characterized in that: The specific division mechanism of the setting is: With the center of the large screen as the origin, establish a right-handed coordinate system, that is, the X-axis is horizontal to the right, the Y-axis is vertically upward, and the Z-axis is perpendicular to the large screen and forward. Divide the space along the X-axis into 20 equal parts and the Y-axis into 15 equal parts, forming 20×15 horizontal grid layers perpendicular to the Z-axis.

3. The method for virtual-reality interaction of a naked-eye 3D large screen according to claim 2, characterized in that: The method of using the spectral parameter calibration logic to determine the optimal spectral parameters for each grid unit for different users also includes: For the center point of each grid cell, the theoretical focal length is calculated according to the object distance-image distance formula, which is expressed as ; where v is the distance from the display screen to the microlens, and u is the vertical distance from the user's eyes to the microlens in each grid unit; for different users, the calculated As the optimal microlens focal length of the corresponding grid unit.

4. The method for virtual-reality interaction of a naked-eye 3D large screen according to claim 3, characterized in that: The specific steps of establishing the cache queue are: Extract the usage count of each spectral parameter combination from the usage data of the spectral parameter combination within a set time window; identify the usage duration of each spectral parameter combination, and determine the record with a usage duration less than the set duration as a false trigger; eliminate the false trigger usage count of the spectral parameter combination; and count the usage count of each spectral parameter combination after elimination within the set time window and record it as a reference count; Sum the reference times of each spectral parameter combination to obtain the total times within the set time window; calculate the proportion of the reference times corresponding to each spectral parameter combination in the total times as the usage frequency proportion of each spectral parameter combination in the set time window; Extract the usage time of each spectral parameter combination corresponding to each reference number, sum the usage time of each group of each spectral parameter combination, obtain the cumulative usage time of each spectral parameter combination within the set time window, calculate the proportion of the cumulative usage time of each spectral parameter combination within the set time window, and use it as the usage time proportion of each spectral parameter combination within the set time window; Identify the number of users using each spectral parameter combination within a set time window, and calculate the proportion of the number of users using each spectral parameter combination to the total number of users as the user concentration proportion of each spectral parameter combination within the set time window; Multiply the usage frequency ratio, usage duration ratio, and user concentration ratio of each spectral parameter combination by the preset frequency weight coefficient, duration weight coefficient, and concentration weight coefficient respectively, and then sum them up to obtain the priority index of each spectral parameter combination within the set time window; The optical splitting parameter combinations are sorted from large to small according to the priority index, and the top H optical splitting parameter combinations are extracted from the sorting result from left to right to construct a cache queue.

5. The method for virtual-reality interaction of a naked-eye 3D large screen according to claim 4, characterized in that: Also includes: Flexible resource allocation: Real-time evaluation of user activity parameters and adjustment of user GPU computing resources based on the evaluation results; The activity parameters include the user's movement speed, interaction frequency, and gaze duration.

6. The method for virtual-reality interaction of a naked-eye 3D large screen according to claim 5, characterized in that: The specific process of real-time evaluation of user activity parameters is as follows: Calculate the Euclidean distance between adjacent time frames using the user's head 3D coordinates (X, Y, Z) ; The calculation formula is expressed as ; Calculate the average of the Euclidean distances of adjacent time frames within a preset time interval as the user's movement speed within the preset time interval; Count the number of times a user triggers a valid interaction event within a preset time interval; record this as the number of interactions; calculate the ratio of the number of interactions to the preset time interval to obtain the user's interaction frequency within the preset time interval; Determine that the user's gaze area is the large screen. When the user's head posture is stable, that is, the pitch angle and yaw angle are lower than the set angles, and the duration is longer than the set time, it is determined to be effective gaze; count the user's effective gaze time within the preset time interval; Convert the user's movement speed, interaction frequency, and effective gaze duration within a preset time interval into a speed activity score, an interaction activity score, and a gaze activity score, respectively; The user's speed activity score, interaction activity score, and gaze activity score are multiplied by the set speed weight coefficient, interaction weight coefficient, and gaze weight coefficient respectively, and then the sum is calculated to obtain the user's activity evaluation index.

7. The method for virtual-reality interaction of a naked-eye 3D large screen according to claim 6, characterized in that: The user's movement speed, interaction frequency, and effective gaze duration within a preset time interval are converted into a speed activity score, an interaction activity score, and a gaze activity score, respectively, as follows: Construct the speed set, frequency set and fixation set corresponding to the movement speed, interaction frequency and effective fixation duration respectively; The speed set, frequency set, and gaze set each contain a group of speed value ranges, a group of frequency value ranges, and a group of duration value ranges; and each group of speed value ranges corresponds to a group of speed activity scores; each group of frequency value ranges corresponds to a group of interaction activity scores; and each group of duration value ranges corresponds to a group of gaze activity scores; The user's movement speed, interaction frequency, and effective gaze duration are input into the speed set, frequency set, and gaze set respectively, and matched with the corresponding value ranges, thereby converting them into the corresponding degree activity score, interaction activity score, and gaze activity score.

8. The method for virtual-reality interaction of a naked-eye 3D large screen according to claim 7, characterized in that: The adjustment of the user's GPU computing resources based on the evaluation results is specifically as follows: Get the current total GPU resources ,and ,in Represents the total number of threads and total amount of video memory respectively; Based on the active score index of each user, the total GPU resources are allocated, that is, through the allocation logic Get the threads allocated to each user in the next preset time interval and video memory ;in Represents the active rating index of each user, i is the user number; It is the sum of the activity rating index of each user.

9. A naked-eye 3D large-screen virtual-reality interaction system, applied to a naked-eye 3D large-screen virtual-reality interaction method according to any one of claims 1 to 8, characterized in that: include: Data acquisition module: acquires user visual images, human body depth images, and RGB images, and calculates the six-degree-of-freedom parameters of the user's head, namely the three-dimensional coordinates (X, Y, Z) of the head and head posture data; Viewing area division module: divides the stereoscopic display effective area in the 3D interactive space in front of the large screen into 20×15 grid units; Parallax Compensation Module: This module uses spectroscopic parameter calibration logic to determine the optimal spectroscopic parameters for each grid cell for different users. It then identifies the grid cell corresponding to the current user's location in real time, extracts the optimal spectroscopic parameters for each grid cell corresponding to the current user, and dynamically adjusts them. The spectroscopic parameters include the lenticular lens tilt angle and the microlens focal length. Cache setting module: analyzes the application data of the spectral parameter combination within the set time window and establishes a cache queue based on the analysis results; Resource scheduling module: evaluates user activity parameters in real time and adjusts the user's GPU computing resources based on the evaluation results; The activity parameters include the user's movement speed, interaction frequency, and gaze duration.

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