Holographic display system based on AI visual identification technology

Abstract

The invention discloses a holographic display system based on an AI visual identification technology, and relates to the technical field of AI visual identification, and the holographic display system comprises the steps: obtaining real-time image data through at least one image sensor, and carrying out the preprocessing of the image data; analyzing the processed image data by using a deep learning model carried by an algorithm module, generating three-dimensional space information from the processed image data, constructing a holographic image based on the three-dimensional space information, and rendering the holographic image in combination with illumination and material information; the display angle of the holographic image is adjusted according to the position and visual angle information of the user, meanwhile, changes in a scene are monitored, display parameters of the holographic image in the holographic image display system are automatically adjusted, and the holographic image is dynamically modified through interactive input of the user. The method realizes efficient and accurate three-dimensional holographic image generation, has wide application potential, and is particularly suitable for the fields of virtual reality, augmented reality, holographic display and the like which need dynamic display and are high in interactivity.

Description

Technical Field

[0001] The present invention relates to the field of AI visual recognition technology, and in particular to a holographic display system based on AI visual recognition technology. Background Art

[0002] With the development of technologies such as virtual reality (VR), augmented reality (AR), and holographic imaging, three-dimensional images and holographic display technology have become important research directions in modern science and technology. Although traditional three-dimensional display technology can present the three-dimensional effect of objects, it often relies on specific hardware devices, such as stereo glasses, virtual reality helmets, etc., which limits its scope of application. Although holographic display can achieve medium-free and natural three-dimensional image display, existing technologies still face great challenges in generating dynamic scenes and handling complex lighting and material changes. Especially in terms of efficient and real-time generation of high-quality holographic images, the rendering efficiency, calculation accuracy and image quality of existing technologies are still difficult to meet the needs of actual applications. Therefore, how to combine deep learning and computer graphics technology to achieve efficient and realistic holographic image generation has become an important direction for current technological development. Summary of the invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a holographic display system based on AI visual recognition technology, which aims to solve several key problems in existing holographic display technology. First, the existing methods are not accurate enough when extracting image spatial information, especially when dealing with depth, position, size and dynamic changes in complex scenes, and cannot accurately obtain the three-dimensional features of objects. Secondly, the existing holographic image rendering method fails to effectively integrate lighting, material properties and object geometry, resulting in the generated holographic image not being realistic enough, especially in terms of lighting changes and material detail performance. Finally, the existing technology mostly relies on specific hardware equipment, the calculation process is relatively complicated, and it is difficult to achieve real-time rendering and efficient calculation. The present invention combines deep learning technology with computer graphics to propose a new image processing and rendering method, which can accurately extract spatial information, combine the lighting and material characteristics of the object, and quickly generate high-precision three-dimensional holographic images, thereby effectively solving the above-mentioned problems in the prior art.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a holographic display system based on AI visual recognition technology, comprising: Acquire real-time image data through at least one image sensor, and pre-process the image data to extract spatial feature information in the image, including depth, position, size, motion trajectory and lighting conditions of the object; Use the deep learning model carried by the algorithm module to analyze the processed image data, generate three-dimensional spatial information from it, build a holographic image based on the three-dimensional spatial information, and render the holographic image in combination with lighting and material information; Through the gaze tracking system, gesture recognition system, eye tracking system and voice recognition system, the display angle of the holographic image is adjusted according to the user's position and viewing angle information. At the same time, the changes in the scene are monitored, the display parameters of the holographic image in the holographic image display system are automatically adjusted, and the holographic image is dynamically modified through user interactive input.

[0006] As a preferred solution of the holographic display system based on AI visual recognition technology described in the present invention, the image sensor is at least one of a camera, a depth sensor, and a lidar sensor, which is used to obtain video streams or static image data.

[0007] As a preferred solution of the holographic display system based on AI visual recognition technology described in the present invention, the preprocessing includes using an adaptive denoising method to denoise the input image, dynamically adjusting the denoising weight according to the local image gradient, assuming that the original image pixel value is , the pixel value of the denoised image is , the denoising operation is expressed as:

[0008] in, is the adaptive filter weight, which is calculated based on the image gradient. The weight formula is:

[0009] in, Represents the position coordinates in the image; n represents the radius of the convolution window; Represents the gradient of the image in the local area, is a parameter that controls the effect of image gradients on weights, is a balancing term, is a normalization factor to ensure that the weights sum to 1; After denoising, the local contrast of the image is not enough to clearly present the details. It is necessary to introduce a contrast enhancement method based on local statistics, calculate the local mean and standard deviation of the image, enhance the image contrast according to the local mean and standard deviation, and dynamically adjust the enhancement intensity through the gradient information of the image. The enhanced image It can be expressed as:

[0010] in, and are the mean and standard deviation of the local area, is the contrast enhancement factor, is the adaptive contrast adjustment coefficient, expressed as:

[0011] in, is the local gradient of the image, is the parameter that controls the effect of the gradient on the enhancement strength, is a constant; Edge detection is achieved using a convolutional neural network (CNN), and the resulting edge image is calculated using a convolution operation:

[0012] in, Indicates that the image is at position The edge detection output value at ; Indicates that the image is at position The pixel value at ; is the weight of the convolution kernel, is the bias term, is the activation function; the loss function is composed of the difference between the edge image and the true edge image and the smoothness constraint of the gradient information:

[0013] in, is the real edge image, is the regularization coefficient; is an image The gradient at is an image The gradient at Get depth maps of images from different viewpoints , through the weighted average method, combined with the correlation of the image to fuse the depth information of multiple perspectives, the fused depth map Calculated by the following formula:

[0014] in, is the weighted coefficient of the kth perspective, and the calculation formula is:

[0015] in, It is Image from one perspective and the baseline perspective The correlation measure between is a parameter that controls the effect of image correlation on the weighting coefficients, is the normalization factor and K is the total number of viewing angles.

[0016] As a preferred solution of the holographic display system based on AI visual recognition technology described in the present invention, the deep learning model includes: the image is subjected to feature extraction through multiple convolutional layers of a convolutional neural network, and the output of each convolutional layer is It is expressed as:

[0017] in, It is Output feature map after layer convolution operation, It is The weights of the convolution kernels of the layers, is the bias term; The depth map is generated by weighted summing the outputs of multiple layers of the convolutional neural network. , where the output of each layer is Assign different weights according to their importance , and add the bias term , and get the depth map:

[0018] in, is the generated depth map, which represents the depth value of each pixel; It is Layer output feature map The weight of is the bias term; is the total number of convolutional layers.

[0019] As a preferred solution of the holographic display system based on AI visual recognition technology described in the present invention, the deep learning model also includes a generative adversarial network GAN to transform the image and noise vector As input, through the generator Generate three-dimensional space information:

[0020] in, is the three-dimensional space information output by the generator, is the mapping function of the generator, which is responsible for transforming the input image and noise vector Convert into three-dimensional space information; Generative Adversarial Network Discriminator Responsible for judging the three-dimensional information output by the generator Is it real? The output of the discriminator is a probability value indicating the possibility that the generated data is real:

[0021] in, is the three-dimensional information generated by the discriminator The judgment value of is the weight of the discriminator, is the bias term of the discriminator; The loss function of the generator It is expressed as:

[0022] in, is the loss function of the generator, which indicates the quality of the generated data; It is expectation; Loss function of the discriminator It is expressed as:

[0023] in, is the loss function of the discriminator, which indicates the classification ability of the discriminator; is the discriminator for the real data The judgment value of .

[0024] As a preferred solution of the holographic display system based on AI visual recognition technology described in the present invention, wherein: the construction of the holographic image includes, according to the three-dimensional spatial information generated by the deep learning model, combining the depth map and the spatial layout data of the object, reconstructing the geometric shape of the object in the image, and mapping the depth and position data of the object to the three-dimensional coordinate system by using the three-dimensional modeling technology, generating a mesh model of the object, defining the points, edges and faces on the surface of the object, and forming the spatial structure of the object. This process relies on the spatial features extracted from the depth map, and generates the geometric model of the object through point cloud data or surface reconstruction technology; Calculate the reflection and refraction behavior of the object surface to different light sources based on material properties; the material properties include the smoothness, roughness, transparency, and refractive index of the object surface, which determine the interaction between light and the object surface; the light source, i.e., lighting conditions, includes ambient light, directional light, point light source, and spotlight, which affect the reflection, refraction, and shadow characteristics of the object surface; The Phong model, a lighting model in computer graphics, is used to simulate the interaction between light and the surface of an object by calculating the paths of incident light, reflected light, and refracted light from the light source to the surface of the object. In the rendering process, the intensity and direction of the incident light are calculated first, and then the direction of the reflected light and the surface reflection coefficient are calculated, and the intensity of the reflected light is adjusted according to the material properties of the object. For transparent objects, the path of the refracted light is calculated using the refractive index and refraction angle, and the brightness and color are adjusted according to the ambient light source and the glossiness of the object surface. The image is generated by projecting a three-dimensional object onto a two-dimensional plane. The projection process converts the position, size and posture of the object in the three-dimensional space into two-dimensional image coordinates through perspective projection or orthographic projection.

[0025] As a preferred solution of the holographic display system based on AI visual recognition technology described in the present invention, wherein: the viewing angle information is obtained through a position sensor and a sight tracking system, and the sight tracking system is composed of a depth camera or an infrared sensor, which is used to monitor the position and movement of the user's head or eyes; The changes in the monitoring scene are carried out by obtaining information about the ambient light and the position of objects through sensors.

[0026] As a preferred solution of the holographic display system based on AI visual recognition technology described in the present invention, the user interaction input is obtained through a gesture recognition system, an eye tracking system or a voice recognition system; the interaction input is processed in real time and fed back to the holographic image display system to adjust the objects in the image or the display content.

[0027] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of a holographic display system based on AI visual recognition technology when executing the computer program.

[0028] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of implementing a holographic display system based on AI visual recognition technology are implemented.

[0029] Beneficial effects of the present invention: The present invention can efficiently extract spatial feature information in the image, including the depth, position, size and dynamic changes of the object, by introducing deep learning technology to accurately analyze the image data. This innovation significantly improves the spatial accuracy and authenticity of holographic image generation. Secondly, combined with the lighting and material rendering technology in computer graphics, the present invention can accurately render the surface features of the object based on the generated three-dimensional spatial data, taking into account factors such as lighting, shadows and reflections, to generate more realistic and detailed holographic images. In addition, the present invention optimizes the image processing and rendering algorithms, improves the computing efficiency, and enables the image generation and rendering process to be completed in a shorter time, meeting the holographic display requirements of real-time or dynamic scenes. In general, the present invention realizes efficient and accurate three-dimensional holographic image generation, has a wide range of application potential, and is particularly suitable for fields such as virtual reality, augmented reality and holographic display that require dynamic display and strong interactivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A flow chart of a holographic display system based on AI visual recognition technology is provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] See also Figure 1 , which is the first embodiment of the present invention, and provides a holographic display system based on AI visual recognition technology, including: Acquire real-time image data through at least one image sensor, and pre-process the image data to extract spatial feature information in the image, including depth, position, size, motion trajectory and lighting conditions of the object; Use the deep learning model carried by the algorithm module to analyze the processed image data, generate three-dimensional spatial information from it, build a holographic image based on the three-dimensional spatial information, and render the holographic image in combination with lighting and material information; Through the gaze tracking system, gesture recognition system, eye tracking system and voice recognition system, the display angle of the holographic image is adjusted according to the user's position and viewing angle information. At the same time, the changes in the scene are monitored, the display parameters of the holographic image in the holographic image display system are automatically adjusted, and the holographic image is dynamically modified through user interactive input.

[0033] The image sensor is at least one of a camera, a depth sensor, and a lidar sensor, and is used to obtain video stream or static image data.

[0034] The preprocessing includes using an adaptive denoising method to denoise the input image, dynamically adjusting the denoising weight according to the local image gradient, assuming that the original image pixel value is , the pixel value of the denoised image is , the denoising operation is expressed as:

[0035] in, is the adaptive filter weight, which is calculated based on the image gradient. The weight formula is:

[0036] in, Represents the position coordinates in the image; n represents the radius of the convolution window; Represents the gradient of the image in the local area, is a parameter that controls the effect of image gradients on weights, is a balancing term, is a normalization factor to ensure that the weights sum to 1; After denoising, the local contrast of the image is not enough to clearly present the details. It is necessary to introduce a contrast enhancement method based on local statistics, calculate the local mean and standard deviation of the image, enhance the image contrast according to the local mean and standard deviation, and dynamically adjust the enhancement intensity through the gradient information of the image. The enhanced image It can be expressed as:

[0037] in, and are the mean and standard deviation of the local area, is the contrast enhancement factor, is the adaptive contrast adjustment coefficient, expressed as:

[0038] in, is the local gradient of the image, is the parameter that controls the effect of the gradient on the enhancement strength, is a constant; Edge detection is achieved using a convolutional neural network (CNN), and the resulting edge image is calculated using a convolution operation:

[0039] in, Indicates that the image is at position The edge detection output value at ; Indicates that the image is at position The pixel value at ; is the weight of the convolution kernel, is the bias term, is the activation function; the loss function is composed of the difference between the edge image and the true edge image and the smoothness constraint of the gradient information:

[0040] in, is the real edge image, is the regularization coefficient; is an image The gradient at is an image The gradient at Get depth maps of images from different viewpoints , through the weighted average method, combined with the correlation of the image to fuse the depth information of multiple perspectives, the fused depth map Calculated by the following formula:

[0041] in, is the weighted coefficient of the kth perspective, and the calculation formula is:

[0042] in, It is Image from one perspective and the baseline perspective The correlation measure between is a parameter that controls the effect of image correlation on the weighting coefficients, is the normalization factor and K is the total number of viewing angles.

[0043] The deep learning model includes: the image is subjected to feature extraction through multiple convolutional layers of a convolutional neural network, and the output of each convolutional layer It is expressed as:

[0044] in, It is Output feature map after layer convolution operation, It is The weights of the convolution kernels of the layers, is the bias term; The depth map is generated by weighted summing the outputs of multiple layers of the convolutional neural network. , where the output of each layer is Assign different weights according to their importance , and add the bias term , and get the depth map:

[0045] in, is the generated depth map, which represents the depth value of each pixel; It is Layer output feature map The weight of is the bias term; is the total number of convolutional layers.

[0046] The deep learning model also includes generating a confrontation network GAN to transform the image and the noise vector As input, through the generator Generate three-dimensional space information:

[0047] in, is the three-dimensional space information output by the generator, is the mapping function of the generator, which is responsible for transforming the input image and noise vector Convert into three-dimensional space information; Generative Adversarial Network Discriminator Responsible for judging the three-dimensional information output by the generator Is it real? The output of the discriminator is a probability value indicating the possibility that the generated data is real:

[0048] in, is the three-dimensional information generated by the discriminator The judgment value of is the weight of the discriminator, is the bias term of the discriminator; The loss function of the generator It is expressed as:

[0049] in, is the loss function of the generator, which indicates the quality of the generated data; It is expectation; Loss function of the discriminator It is expressed as:

[0050] in, is the loss function of the discriminator, which indicates the classification ability of the discriminator; is the discriminator for the real data The judgment value of .

[0051] The construction of the holographic image includes, according to the three-dimensional spatial information generated by the deep learning model, combining the depth map and the spatial layout data of the object, reconstructing the geometric shape of the object in the image, mapping the depth and position data of the object into a three-dimensional coordinate system by using a three-dimensional modeling technology, generating a mesh model of the object, defining points, edges and faces on the surface of the object, and forming the spatial structure of the object. This process relies on the spatial features extracted from the depth map, and generates a geometric model of the object by using point cloud data or surface reconstruction technology; Calculate the reflection and refraction behavior of the object surface to different light sources based on material properties; the material properties include the smoothness, roughness, transparency, and refractive index of the object surface, which determine the interaction between light and the object surface; the light source, i.e., lighting conditions, includes ambient light, directional light, point light source, and spotlight, which affect the reflection, refraction, and shadow characteristics of the object surface; The Phong model, a lighting model in computer graphics, is used to simulate the interaction between light and the surface of an object by calculating the paths of incident light, reflected light, and refracted light from the light source to the surface of the object. In the rendering process, the intensity and direction of the incident light are calculated first, and then the direction of the reflected light and the surface reflection coefficient are calculated, and the intensity of the reflected light is adjusted according to the material properties of the object. For transparent objects, the path of the refracted light is calculated using the refractive index and refraction angle, and the brightness and color are adjusted according to the ambient light source and the glossiness of the object surface. The image is generated by projecting a three-dimensional object onto a two-dimensional plane. The projection process converts the position, size and posture of the object in the three-dimensional space into two-dimensional image coordinates through perspective projection or orthographic projection.

[0052] The viewing angle information is obtained through a position sensor and a gaze tracking system, wherein the gaze tracking system is composed of a depth camera or an infrared sensor for monitoring the position and movement of the user's head or eyes; The changes in the monitoring scene are carried out by obtaining information about the ambient light and the position of objects through sensors.

[0053] The user interaction input is obtained through a gesture recognition system, an eye tracking system or a voice recognition system; the interaction input is processed in real time and fed back to the holographic image display system to adjust the objects in the image or the displayed content.

[0054] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0055] Embodiment 2, the second embodiment of the present invention, is different from the previous embodiment in that: If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0056] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0057] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0058] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0059] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0060] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A holographic display system based on AI visual recognition technology, characterized by: include, Acquire real-time image data through at least one image sensor, and pre-process the image data to extract spatial feature information in the image, including depth, position, size, motion trajectory and lighting conditions of the object; Use the deep learning model carried by the algorithm module to analyze the processed image data, generate three-dimensional spatial information from it, build a holographic image based on the three-dimensional spatial information, and render the holographic image in combination with lighting and material information; Through the gaze tracking system, gesture recognition system, eye tracking system and voice recognition system, the display angle of the holographic image is adjusted according to the user's position and viewing angle information. At the same time, the changes in the scene are monitored, the display parameters of the holographic image in the holographic image display system are automatically adjusted, and the holographic image is dynamically modified through user interactive input.

2. A holographic display system based on AI visual recognition technology as claimed in claim 1, characterized in that: The image sensor is at least one of a camera, a depth sensor, and a lidar sensor, and is used to obtain video stream or static image data.

3. A holographic display system based on AI visual recognition technology as claimed in claim 2, characterized in that: The preprocessing includes using an adaptive denoising method to denoise the input image, dynamically adjusting the denoising weight according to the local image gradient, assuming that the original image pixel value is , the pixel value of the denoised image is , the denoising operation is expressed as: ， in, is the adaptive filter weight, which is calculated based on the image gradient. The weight formula is: ， in, Represents the position coordinates in the image; n represents the radius of the convolution window; Represents the gradient of the image in the local area, is a parameter that controls the effect of image gradients on weights, is a balancing term, is a normalization factor to ensure that the weights sum to 1; After denoising, the local contrast of the image is not enough to clearly present the details. It is necessary to introduce a contrast enhancement method based on local statistics, calculate the local mean and standard deviation of the image, enhance the image contrast according to the local mean and standard deviation, and dynamically adjust the enhancement intensity through the gradient information of the image. The enhanced image It can be expressed as: , in, and are the mean and standard deviation of the local area, is the contrast enhancement factor, is the adaptive contrast adjustment coefficient, expressed as: ， in, is the local gradient of the image, is the parameter that controls the effect of the gradient on the enhancement strength, is a constant; Edge detection is achieved using a convolutional neural network (CNN), and the resulting edge image is calculated using a convolution operation: ， in, Indicates that the image is at position The edge detection output value at ; Indicates that the image is at position The pixel value at ; is the weight of the convolution kernel, is the bias term, is the activation function; the loss function is composed of the difference between the edge image and the true edge image and the smoothness constraint of the gradient information: ， in, is the real edge image, is the regularization coefficient; is an image The gradient at is an image The gradient at Get depth maps of images from different viewpoints , through the weighted average method, combined with the correlation of the image to fuse the depth information of multiple perspectives, the fused depth map Calculated by the following formula: ， in, is the weighted coefficient of the kth perspective, and the calculation formula is: ， in, It is Image from one perspective and the baseline perspective The correlation measure between is a parameter that controls the effect of image correlation on the weighting coefficients, is the normalization factor and K is the total number of viewing angles.

4. A holographic display system based on AI visual recognition technology as claimed in claim 3, characterized in that: The deep learning model includes: the image is subjected to feature extraction through multiple convolutional layers of a convolutional neural network, and the output of each convolutional layer It is expressed as: , in, It is Output feature map after layer convolution operation, It is The weights of the convolution kernels of the layers, is the bias term; The depth map is generated by weighted summing the outputs of multiple layers of the convolutional neural network. , where the output of each layer is Assign different weights according to their importance , and add the bias term , and get the depth map: ， in, is the generated depth map, which represents the depth value of each pixel; It is Layer output feature map The weight of is the bias term; is the total number of convolutional layers.

5. The holographic display system based on AI visual recognition technology as claimed in claim 4, characterized in that: The deep learning model also includes generating a confrontation network GAN to transform the image and the noise vector As input, through the generator Generate three-dimensional space information: ， in, is the three-dimensional space information output by the generator, is the mapping function of the generator, which is responsible for transforming the input image and noise vector Convert into three-dimensional space information; Generative Adversarial Network Discriminator Responsible for judging the three-dimensional information output by the generator Is it real? The output of the discriminator is a probability value indicating the possibility that the generated data is real: ， in, is the three-dimensional information generated by the discriminator The judgment value of is the weight of the discriminator, is the bias term of the discriminator; The loss function of the generator It is expressed as: ， in, is the loss function of the generator, which indicates the quality of the generated data; It is expectation; Loss function of the discriminator It is expressed as: ， in, is the loss function of the discriminator, which indicates the classification ability of the discriminator; is the discriminator for the real data The judgment value of .

6. A holographic display system based on AI visual recognition technology as claimed in claim 5, characterized in that: The construction of the holographic image includes, based on the three-dimensional spatial information generated by the deep learning model, combining the depth map and the spatial layout data of the object, reconstructing the geometric shape of the object in the image, mapping the depth and position data of the object into a three-dimensional coordinate system by using three-dimensional modeling technology, generating a mesh model of the object, defining points, edges and faces on the surface of the object, and forming the spatial structure of the object; Calculate the reflection and refraction behavior of the object surface to different light sources based on material properties; the material properties include the smoothness, roughness, transparency, and refractive index of the object surface, which determine the interaction between light and the object surface; the light source, i.e., lighting conditions, includes ambient light, directional light, point light source, and spotlight, which affect the reflection, refraction, and shadow characteristics of the object surface; The Phong model, a lighting model in computer graphics, is used to simulate the interaction between light and the surface of an object by calculating the paths of incident light, reflected light, and refracted light from the light source to the surface of the object. In the rendering process, the intensity and direction of the incident light are calculated first, and then the direction of the reflected light and the surface reflection coefficient are calculated, and the intensity of the reflected light is adjusted according to the material properties of the object. For transparent objects, the path of the refracted light is calculated using the refractive index and refraction angle, and the brightness and color are adjusted according to the ambient light source and the glossiness of the object surface. The image is generated by projecting a three-dimensional object onto a two-dimensional plane. The projection process converts the position, size and posture of the object in the three-dimensional space into two-dimensional image coordinates through perspective projection or orthographic projection.

7. The holographic display system based on AI visual recognition technology as claimed in claim 6, characterized in that: The viewing angle information is obtained through a position sensor and a gaze tracking system, wherein the gaze tracking system is composed of a depth camera or an infrared sensor for monitoring the position and movement of the user's head or eyes; The changes in the monitoring scene are carried out by obtaining information about the ambient light and the position of objects through sensors.

8. The holographic display system based on AI visual recognition technology as claimed in claim 7, characterized in that: The user interaction input is obtained through a gesture recognition system, an eye tracking system or a voice recognition system; the interaction input is processed in real time and fed back to the holographic image display system to adjust the objects in the image or the displayed content.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the system according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the system according to any one of claims 1 to 8 are implemented.

Images