Model construction method and device based on pan-tilt scanning recognition

Abstract

The present invention discloses a model construction method and device based on pan-tilt scanning and recognition, which relates to the technical field of three-dimensional modeling. The target scene is scanned in all directions by the pan-tilt, and visible light image data and laser point cloud data are obtained; the visible light image data is enhanced to obtain an enhanced image; the enhanced image and point cloud data are aligned to obtain the color information of the point cloud points; the point cloud data at different angles are registered to generate a preliminary three-dimensional geometric model; the three-dimensional geometric model is colored according to the color information of each point cloud point to obtain the final target model. By performing color restoration and contrast enhancement on the visible light image, the image details are made clearer, which is helpful for texture mapping and coloring processing of the three-dimensional model. This method combines the advantages of laser point cloud and visible light image, which not only improves the geometric accuracy of the model, but also enhances the realism of the visual effect.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional modeling, and in particular to a model construction method and device based on pan-tilt scanning and recognition. Background Art

[0002] With the rapid development of modern intelligent technologies, the application of 3D modeling technology has become increasingly widespread, encompassing a wide range of fields, including autonomous driving, robotic navigation, virtual reality (VR), augmented reality (AR), building scanning, and urban digitization. These applications place higher demands on modeling accuracy, efficiency, and realism. Laser point clouds are a common and efficient way to represent 3D models, containing the 3D coordinates of each point, and optionally including additional information such as intensity. While 3D coordinates alone can identify the geometric structure of a 3D model, they cannot accurately and intuitively represent its appearance. To overcome the limitations of single sensors, the fusion of LiDAR and visible light cameras has become a trend in recent years. By combining the precise depth information of LiDAR with the rich color and texture information of visible light cameras, more comprehensive and accurate 3D models can be generated.

[0003] However, in low-light environments, the camera's white balance and color reproduction capabilities are affected, potentially causing image color distortion or color cast, resulting in low contrast and difficulty extracting details. Especially in dark areas, the color, texture, edges, and object details in the image may completely disappear. Ultimately, unnatural colors or lighting effects may appear when shading and texture mapping 3D models, making the model look unrealistic. Summary of the Invention

[0004] The purpose of the present invention is to solve the problem of low quality of low-light images mentioned in the above background technology, and to propose a model construction method and device based on pan-tilt scanning recognition.

[0005] A first aspect of the present invention provides a model construction method based on pan-tilt scanning recognition, wherein the pan-tilt is equipped with a laser radar and a visible light camera; the method comprises:

[0006] The target scene is scanned in all directions by the pan-tilt platform to obtain visible light image data and laser point cloud data; the laser point cloud data includes coordinate information of the point cloud points;

[0007] Denoising the laser point cloud data to obtain denoised point cloud data;

[0008] Enhance the visible light image data to obtain an enhanced image;

[0009] Align the enhanced image and point cloud data to obtain the color information of the point cloud points;

[0010] Register point cloud data from different angles to generate a preliminary 3D geometric model;

[0011] The three-dimensional geometric model is colored according to the color information of each point cloud point to obtain the final target model.

[0012] Optionally, the enhancing the visible light image data to obtain an enhanced image includes:

[0013] Converting the target visible light image into HSV space to obtain the original hue component, saturation component, and lightness component; the target visible light image is any visible light image;

[0014] According to the adaptive gamma correction algorithm, the original brightness component is enhanced to obtain the target image:

[0015]

[0016] Where V1 is the target image; (x, y) is the pixel position; V0 is the original brightness component; γ is the gamma parameter; α is the preset adjustment coefficient; V mean is the mean luminance value of the luminance component;

[0017] The target image and the original luminance component are fused to obtain the enhanced luminance component;

[0018] The original hue component, saturation component and enhanced lightness component form a new HSV image;

[0019] The new HSV image is converted to RGB space to obtain the enhanced image of the target visible light image.

[0020] Optionally, fusing the target image with the original luminance component to obtain an enhanced luminance component includes:

[0021] According to the target visible light image, the weight coefficient is obtained:

[0022]

[0023] Where k is the weight coefficient to be sought; (x, y) is the pixel position; g is the brightness value obtained from the RGB channels; R, G, and B are the red, green, and blue channel values of the target visible light image, respectively; a, b, and c are constants;

[0024] According to the weight coefficient, the target image and the original luminance component are fused to obtain the enhanced luminance component:

[0025] V2(x,y)=k(x,y)*V0(x,y)+(1-k(x,y))*V1(x,y);

[0026] Among them, V2 is the enhanced brightness component.

[0027] Optionally, aligning the enhanced image and point cloud data to obtain color information of the point cloud points includes:

[0028] Perform temporal alignment on the enhanced image and point cloud data, and extract the paired first image and first point cloud;

[0029] Projecting a target point cloud point onto the surface of the first image according to preset camera internal and external parameters to obtain a pixel position of the target point cloud point in the first image; the target point cloud point is any point cloud point in the first point cloud;

[0030] According to the pixel position, bilinear interpolation is used to obtain the RGB channel value of the target point cloud point.

[0031] Optionally, the pan / tilt platform has a horizontal rotation mechanism and a vertical rotation mechanism; and the registering of point cloud data at different angles to generate a preliminary three-dimensional geometric model includes:

[0032] According to the pan-tilt rotation angle when acquiring point cloud data, coordinate system conversion is performed, and point cloud data of different angles are projected into the preset global coordinate system to obtain the spatial coordinates of each point cloud point in the global coordinate system, thus obtaining a unified point cloud;

[0033] A triangular mesh is generated based on the unified point cloud to obtain a three-dimensional plane as a preliminary three-dimensional geometric model.

[0034] A second aspect of the present invention provides a model construction device based on pan-tilt scanning and recognition, wherein the pan-tilt is equipped with a laser radar and a visible light camera; the device comprises:

[0035] A data acquisition module is used to perform an omnidirectional scan of the target scene through a pan-tilt platform to obtain visible light image data and laser point cloud data; the laser point cloud data includes coordinate information of the point cloud points;

[0036] Point cloud denoising module, used to denoise laser point cloud data to obtain denoised point cloud data;

[0037] An image enhancement module is used to enhance visible light image data to obtain an enhanced image;

[0038] The color matching module is used to align the enhanced image and point cloud data to obtain the color information of the point cloud points;

[0039] The 3D surface generation module is used to register point cloud data from different angles and generate a preliminary 3D geometric model;

[0040] The model coloring module is used to color the three-dimensional geometric model according to the color information of each point cloud point to obtain the final target model.

[0041] Optionally, the image enhancement module includes:

[0042] A first conversion module is configured to convert a target visible light image into an HSV space to obtain an original hue component, a saturation component, and a lightness component; the target visible light image is any visible light image;

[0043] The gamma correction module is used to enhance the original brightness component according to the adaptive gamma correction algorithm to obtain the target image:

[0044]

[0045] Where V1 is the target image; (x, y) is the pixel position; V0 is the original brightness component; γ is the gamma parameter; α is the preset adjustment coefficient; V mean is the mean luminance value of the luminance component;

[0046] An image fusion module is used to fuse the target image with the original luminance component to obtain an enhanced luminance component;

[0047] A replacement module is used to compose a new HSV image from the original hue component, saturation component and enhanced lightness component;

[0048] The second conversion module is used to convert the new HSV image into RGB space to obtain an enhanced image of the target visible light image.

[0049] Optionally, the image fusion module includes:

[0050] The weight calculation module is used to obtain a weight coefficient according to the target visible light image:

[0051]

[0052] Where k is the weight coefficient to be sought; (x, y) is the pixel position; g is the brightness value obtained from the RGB channels; R, G, and B are the red, green, and blue channel values of the target visible light image, respectively; a, b, and c are constants;

[0053] The weighted fusion module is used to fuse the target image and the original luminance component according to the weight coefficient to obtain an enhanced luminance component:

[0054] V2(x,y)=k(x,y)*V0(x,y)+(1-k(x,y))*V1(x,y);

[0055] Among them, V2 is the enhanced brightness component.

[0056] Optionally, the color matching module includes:

[0057] A temporal alignment module, configured to temporally align the enhanced image and point cloud data, and extract a pair of first images and first point clouds;

[0058] a mapping module, configured to project a target point cloud point onto the surface of the first image based on preset camera intrinsic and extrinsic parameters to obtain a pixel position of the target point cloud point in the first image; the target point cloud point is any point cloud point in the first point cloud;

[0059] The color determination module is used to obtain the RGB channel value of the target point cloud point by using bilinear interpolation according to the pixel position.

[0060] Optionally, the pan / tilt platform is equipped with a horizontal rotation mechanism and a vertical rotation mechanism;

[0061] The three-dimensional surface generation module includes:

[0062] The coordinate system conversion module is used to perform coordinate system conversion according to the pan / tilt rotation angle when acquiring point cloud data, project point cloud data of different angles into the preset global coordinate system, obtain the spatial coordinates of each point cloud point in the global coordinate system, and obtain a unified point cloud;

[0063] The meshing module is used to generate a triangular mesh based on the unified point cloud to obtain a three-dimensional plane as a preliminary three-dimensional geometric model.

[0064] Beneficial effects of the present invention:

[0065] The present invention proposes a model construction method based on pan-tilt scanning and recognition, wherein the pan-tilt is equipped with a laser radar and a visible light camera; the method comprises: performing an omnidirectional scanning of a target scene by the pan-tilt to obtain visible light image data and laser point cloud data; the laser point cloud data comprises coordinate information of point cloud points; denoising the laser point cloud data to obtain denoised point cloud data; enhancing the visible light image data to obtain an enhanced image; aligning the enhanced image and the point cloud data to obtain color information of the point cloud points; registering the point cloud data at different angles to generate a preliminary three-dimensional geometric model; and coloring the three-dimensional geometric model according to the color information of each point cloud point to obtain a final target model.

[0066] Through the pan / tilt system's omnidirectional scanning and point cloud registration technology, point cloud data from different angles can be effectively integrated to generate a more complete and accurate 3D model. Enhancement of visible light images effectively improves color reproduction and contrast in low-light environments, making image details clearer and facilitating texture mapping and shading of 3D models. This method combines the advantages of laser point clouds and visible light images, enhancing both the geometric accuracy of the model and the visual realism of the result. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The present invention will be further described below with reference to the accompanying drawings.

[0068] Figure 1 A flowchart of a model construction method based on PTZ scanning recognition is provided for an embodiment of the present invention;

[0069] Figure 2 The present invention provides a structural diagram of a model construction device based on pan-tilt scanning and recognition. DETAILED DESCRIPTION

[0070] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0071] The embodiment of the present invention provides a model construction method based on PTZ scanning recognition. Figure 1 , Figure 1 A flowchart of a model construction method based on PTZ scanning recognition provided by an embodiment of the present invention. The method includes the following steps:

[0072] S101, perform an all-around scan of the target scene through the pan-tilt platform to obtain visible light image data and laser point cloud data.

[0073] S102: De-noising the laser point cloud data to obtain de-noised point cloud data.

[0074] S103: Enhance the visible light image data to obtain an enhanced image.

[0075] S104: Align the enhanced image and the point cloud data to obtain color information of the point cloud points.

[0076] S105 , registering the point cloud data at different angles to generate a preliminary three-dimensional geometric model.

[0077] S106: Coloring the three-dimensional geometric model according to the color information of each point cloud point to obtain the final target model.

[0078] Among them, the gimbal is equipped with a laser radar and a visible light camera; the laser point cloud data includes point clouds at different angles and the coordinate information of their point cloud points; the visible light image data includes visible light images at different angles.

[0079] An embodiment of the present invention provides a model construction method based on pan-tilt scanning and recognition. Through the pan-tilt's omnidirectional scanning and point cloud registration technology, point cloud data from different angles can be effectively integrated to generate a more complete and accurate three-dimensional model. By enhancing visible light images, color reproduction and contrast in low-light environments can be effectively improved, making image details clearer and facilitating texture mapping and coloring of the three-dimensional model. This method combines the advantages of laser point clouds and visible light images, improving the geometric accuracy of the model while enhancing the visual realism.

[0080] In one embodiment, step S103 includes:

[0081] Step 1: Convert the target visible light image to HSV space to obtain the original hue component, saturation component, and lightness component.

[0082] Step 2: According to the adaptive gamma correction algorithm, the original brightness component is enhanced to obtain the target image:

[0083]

[0084] Where V1 is the target image; (x, y) is the pixel position; V0 is the original brightness component; γ is the gamma parameter; α is the preset adjustment coefficient; V mean is the mean value of the lightness components.

[0085] Step three: fuse the target image and the original luminance component to obtain the enhanced luminance component.

[0086] Step 4: The original hue component, saturation component and enhanced lightness component are combined into a new HSV image.

[0087] Step 5: Convert the new HSV image to RGB space to obtain the enhanced image of the target visible light image.

[0088] The target visible light image is any visible light image.

[0089] In one implementation, by enhancing the luminance component, the visibility of dark areas and detail areas in the image can be effectively improved, the contrast of the image can be increased, and the details can be made more prominent, especially in areas with low light levels. This is crucial for subsequent 3D model generation, shading, and texture mapping.

[0090] In one implementation, the adjustment coefficient can be set to 0.5. Adjusting the gamma parameters based on the mean value of the luminance component avoids the over-enhancement problem that can occur with traditional gamma correction. In adaptive gamma correction, the gamma value is selected dynamically and can be flexibly adjusted based on the brightness of the specific image, making the luminance enhancement more natural and avoiding artifacts or distortion caused by over-processing.

[0091] In one embodiment, the target image and the original luminance component are fused to obtain an enhanced luminance component including:

[0092] Step 1: Get the weight coefficient based on the target visible light image:

[0093]

[0094] Where k is the desired weight coefficient; (x, y) is the pixel position; g is the brightness value obtained by the RGB channels; R, G, and B are the red, green, and blue channel values of the target visible light image, respectively; a, b, and c are constants.

[0095] Step 2: According to the weight coefficient, the target image and the original luminance component are fused to obtain the enhanced luminance component:

[0096] V2(x,y)=k(x,y)*V0(x,y)+(1-k(x,y))*V1(x,y);

[0097] Among them, V2 is the enhanced brightness component.

[0098] In one implementation, a, b, and c can select the weight values of the standard brightness conversion formula, namely 0.299, 0.587, and 0.114. As can be seen from the above formula, after the brightness value is calculated, it is normalized so that at the location where the brightness is 0, the weight of the original image is 0; at the location where the brightness is 255 (normalized to 1), the weight of the original image is 1. Through the fused luminance components, different brightness areas can be enhanced in a targeted manner, so that the dark details in the image are highlighted, while retaining the edge details of the high-brightness area and effectively avoiding overexposure or distortion in the highlight area. This enhancement of local adaptability can achieve a balance between the overall brightness and local details of the image, improving the overall image quality.

[0099] In one embodiment, step S104 includes:

[0100] Step 1: Temporally align the enhanced image and point cloud data to extract a pair of first image and first point cloud.

[0101] Step 2: Project the target point cloud onto the surface of the first image based on the preset camera internal and external parameters to obtain the pixel position of the target point cloud in the first image.

[0102] Step 3: According to the pixel position, bilinear interpolation is used to obtain the RGB channel value of the target point cloud point.

[0103] The target point cloud point is any point cloud point in the first point cloud.

[0104] In one implementation, temporal alignment of the enhanced image and point cloud data ensures that they were scanned or captured at the same time, avoiding errors or inconsistencies caused by time differences. This is crucial for subsequent point cloud colorization and texture mapping, ensuring a high degree of color and detail matching in the resulting 3D model.

[0105] In one implementation, bilinear interpolation is used to calculate the RGB channel values of the target point cloud point as follows:

[0106]

[0107] Among them, P i (X,Y) is the i-th channel value of the target point cloud point, where i = 1, 2, and 3 represent the R, G, and B channels, respectively; (X,Y) is the pixel position of the target point cloud point; (X1,Y1), (X2,Y1), (X1,Y2), and (X2,Y2) are the upper left pixel position, upper right pixel position, lower left pixel position, and lower right pixel position, respectively. By projecting the point cloud data onto an image and using bilinear interpolation to obtain RGB values, accurate color information can be obtained for each point in the point cloud. In this way, each point in the 3D model will have corresponding color data. The generated model is not only accurate in geometric structure, but also more realistic in visual effects. In particular, the color transitions of the model are smoother and more natural in the presentation of details and lighting effects.

[0108] In one embodiment, the platform has a horizontal rotation mechanism and a vertical rotation mechanism; step S105 includes:

[0109] Step 1: According to the pan-tilt rotation angle when acquiring the point cloud data, coordinate system conversion is performed, and the point cloud data at different angles are projected into the preset global coordinate system to obtain the spatial coordinates of each point cloud point in the global coordinate system, thereby obtaining a unified point cloud;

[0110] Step 2: Generate a triangular mesh based on the unified point cloud to obtain a three-dimensional plane as a preliminary three-dimensional geometric model.

[0111] In one implementation, the center of the gimbal can be used as the origin, and a global coordinate system can be established based on the posture when the horizontal rotation angle is 0 and the vertical pitch angle is 0. The coordinate system transformation matrix is derived from the gimbal rotation angle, allowing point cloud data from different angles to be incorporated into the same coordinate system. By projecting point cloud data from different angles into a preset global coordinate system, all data from different directions can be processed uniformly. This avoids conflicts and inconsistencies between multiple local coordinate systems, ensuring that the spatial relationships of all data are correct when generating the 3D model.

[0112] The embodiment of the present invention provides a model construction method based on PTZ scanning recognition. Figure 2 , Figure 2 This is a structural diagram of a model construction device based on pan-tilt scanning and recognition provided by an embodiment of the present invention. The device includes:

[0113] The data acquisition module is used to perform an all-round scan of the target scene through the pan-tilt platform to obtain visible light image data and laser point cloud data.

[0114] The point cloud denoising module is used to denoise the laser point cloud data to obtain denoised point cloud data.

[0115] The image enhancement module is used to enhance the visible light image data to obtain an enhanced image.

[0116] The color matching module is used to align the enhanced image and point cloud data to obtain the color information of the point cloud points.

[0117] The 3D surface generation module is used to register point cloud data from different angles and generate a preliminary 3D geometric model.

[0118] The model coloring module is used to color the three-dimensional geometric model according to the color information of each point cloud point to obtain the final target model.

[0119] Among them, the gimbal is equipped with a laser radar and a visible light camera; the laser point cloud data includes the coordinate information of the point cloud points.

[0120] An embodiment of the present invention provides a model construction device based on pan-tilt scanning and recognition. Through the pan-tilt's omnidirectional scanning and point cloud registration technology, point cloud data from different angles can be effectively integrated to generate a more complete and accurate three-dimensional model. By enhancing visible light images, color reproduction and contrast in low-light environments can be effectively improved, making image details clearer and facilitating texture mapping and shading of the three-dimensional model. This method combines the advantages of laser point clouds and visible light images, improving the geometric accuracy of the model while enhancing the visual realism.

[0121] In one embodiment, the image enhancement module includes:

[0122] The first conversion module is used to convert the target visible light image into the HSV space to obtain the original hue component, saturation component and lightness component.

[0123] The gamma correction module is used to enhance the original brightness component according to the adaptive gamma correction algorithm to obtain the target image:

[0124]

[0125] Where V1 is the target image; (x, y) is the pixel position; V0 is the original brightness component; γ is the gamma parameter; α is the preset adjustment coefficient; V mean is the mean value of the lightness components.

[0126] The image fusion module is used to fuse the target image with the original luminance component to obtain an enhanced luminance component.

[0127] The replacement module is used to compose a new HSV image from the original hue component, saturation component and enhanced lightness component.

[0128] The second conversion module is used to convert the new HSV image into RGB space to obtain an enhanced image of the target visible light image.

[0129] The target visible light image is any visible light image.

[0130] In one embodiment, the image fusion module includes:

[0131] The weight calculation module is used to obtain the weight coefficient according to the target visible light image:

[0132]

[0133] Where k is the desired weight coefficient; (x, y) is the pixel position; g is the brightness value obtained by the RGB channels; R, G, and B are the red, green, and blue channel values of the target visible light image, respectively; a, b, and c are constants.

[0134] The weighted fusion module is used to fuse the target image and the original luminance component according to the weight coefficient to obtain the enhanced luminance component:

[0135] V2(x,y)=k(x,y)*V0(x,y)+(1-k(x,y))*V1(x,y);

[0136] Among them, V2 is the enhanced brightness component.

[0137] In one embodiment, the color matching module includes:

[0138] The temporal alignment module is used to temporally align the enhanced image and point cloud data and extract a pair of first images and first point clouds.

[0139] The mapping module is used to project the target point cloud point onto the surface of the first image according to the preset camera internal and external parameters to obtain the pixel position of the target point cloud point in the first image.

[0140] The color determination module is used to obtain the RGB channel value of the target point cloud point based on the pixel position using bilinear interpolation.

[0141] The target point cloud point is any point cloud point in the first point cloud.

[0142] In one embodiment, the gimbal has a horizontal rotation mechanism and a vertical rotation mechanism; the three-dimensional surface generation module includes:

[0143] The coordinate system conversion module is used to perform coordinate system conversion according to the pan-tilt rotation angle when acquiring point cloud data, project point cloud data of different angles into a preset global coordinate system, obtain the spatial coordinates of each point cloud point in the global coordinate system, and obtain a unified point cloud.

[0144] The meshing module is used to generate a triangular mesh based on the unified point cloud to obtain a three-dimensional plane as a preliminary three-dimensional geometric model.

[0145] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A model construction method based on PTZ scanning recognition, characterized in that: The gimbal is equipped with a laser radar and a visible light camera; the method includes: The target scene is scanned in all directions by the pan-tilt platform to obtain visible light image data and laser point cloud data; the laser point cloud data includes coordinate information of the point cloud points; Denoising the laser point cloud data to obtain denoised point cloud data; Enhance the visible light image data to obtain an enhanced image; Align the enhanced image and point cloud data to obtain the color information of the point cloud points; Register point cloud data from different angles to generate a preliminary 3D geometric model; Coloring the three-dimensional geometric model according to the color information of each point cloud point to obtain the final target model; The enhanced visible light image data is enhanced to obtain an enhanced image, comprising: Converting the target visible light image into HSV space to obtain the original hue component, saturation component, and lightness component; the target visible light image is any visible light image; According to the adaptive gamma correction algorithm, the original brightness component is enhanced to obtain the target image: ； in, is the target image; is the pixel position; is the original lightness component; is the gamma parameter; is the preset adjustment factor; is the mean luminance value of the luminance component; The target image and the original luminance component are fused to obtain the enhanced luminance component; The original hue component, saturation component and enhanced lightness component form a new HSV image; The new HSV image is converted to RGB space to obtain the enhanced image of the target visible light image.

2. The model construction method based on PTZ scanning recognition according to claim 1, characterized in that: The target image and the original luminance component are fused to obtain an enhanced luminance component, which includes: According to the target visible light image, the weight coefficient is obtained: ； in, is the desired weight coefficient; is the pixel position; is the brightness value obtained from the RGB channel; R, G, and B are the red, green, and blue channel values of the target visible light image, respectively; a, b, and c are constants; According to the weight coefficient, the target image and the original luminance component are fused to obtain the enhanced luminance component: ； in, is the enhanced brightness component.

3. The model construction method based on PTZ scanning recognition according to claim 1, characterized in that: The enhanced image and point cloud data are aligned to obtain the color information of the point cloud points, including: Perform temporal alignment on the enhanced image and point cloud data, and extract the paired first image and first point cloud; Projecting a target point cloud point onto the surface of the first image according to preset camera internal and external parameters to obtain a pixel position of the target point cloud point in the first image; the target point cloud point is any point cloud point in the first point cloud; According to the pixel position, bilinear interpolation is used to obtain the RGB channel value of the target point cloud point.

4. The model construction method based on PTZ scanning recognition according to claim 1, characterized in that: The platform is equipped with a horizontal rotation mechanism and a vertical rotation mechanism; The step of registering point cloud data at different angles to generate a preliminary three-dimensional geometric model includes: According to the pan-tilt rotation angle when acquiring point cloud data, coordinate system conversion is performed, and point cloud data of different angles are projected into the preset global coordinate system to obtain the spatial coordinates of each point cloud point in the global coordinate system, thus obtaining a unified point cloud; A triangular mesh is generated based on the unified point cloud to obtain a three-dimensional plane as a preliminary three-dimensional geometric model.

5. A model construction device based on pan-tilt scanning recognition, characterized in that: The gimbal is equipped with a laser radar and a visible light camera; the device includes: A data acquisition module is used to perform an omnidirectional scan of the target scene through a pan-tilt platform to obtain visible light image data and laser point cloud data; the laser point cloud data includes coordinate information of the point cloud points; Point cloud denoising module, used to denoise laser point cloud data to obtain denoised point cloud data; An image enhancement module is used to enhance visible light image data to obtain an enhanced image; The color matching module is used to align the enhanced image and point cloud data to obtain the color information of the point cloud points; The 3D surface generation module is used to register point cloud data from different angles and generate a preliminary 3D geometric model; A model coloring module is used to color the three-dimensional geometric model according to the color information of each point cloud point to obtain the final target model; The image enhancement module includes: A first conversion module is configured to convert a target visible light image into an HSV space to obtain an original hue component, a saturation component, and a lightness component; the target visible light image is any visible light image; The gamma correction module is used to enhance the original brightness component according to the adaptive gamma correction algorithm to obtain the target image: ； in, is the target image; is the pixel position; is the original lightness component; is the gamma parameter; is the preset adjustment factor; is the mean luminance value of the luminance component; An image fusion module is used to fuse the target image with the original luminance component to obtain an enhanced luminance component; A replacement module is used to compose a new HSV image from the original hue component, saturation component and enhanced lightness component; The second conversion module is used to convert the new HSV image into RGB space to obtain an enhanced image of the target visible light image.

6. The model construction device based on PTZ scanning and recognition according to claim 5, characterized in that: The image fusion module includes: The weight calculation module is used to obtain a weight coefficient according to the target visible light image: ； in, is the desired weight coefficient; is the pixel position; is the brightness value obtained from the RGB channel; R, G, and B are the red, green, and blue channel values of the target visible light image, respectively; a, b, and c are constants; The weighted fusion module is used to fuse the target image and the original luminance component according to the weight coefficient to obtain an enhanced luminance component: ； in, is the enhanced brightness component.

7. The model construction device based on PTZ scanning and recognition according to claim 5, characterized in that: The color matching module includes: A temporal alignment module, configured to temporally align the enhanced image and point cloud data, and extract a pair of first images and first point clouds; a mapping module, configured to project a target point cloud point onto the surface of the first image based on preset camera intrinsic and extrinsic parameters to obtain a pixel position of the target point cloud point in the first image; the target point cloud point is any point cloud point in the first point cloud; The color determination module is used to obtain the RGB channel value of the target point cloud point by using bilinear interpolation according to the pixel position.

8. The model construction device based on PTZ scanning and recognition according to claim 5, characterized in that: The platform is equipped with a horizontal rotation mechanism and a vertical rotation mechanism; The three-dimensional surface generation module includes: The coordinate system conversion module is used to perform coordinate system conversion according to the pan / tilt rotation angle when acquiring point cloud data, project point cloud data of different angles into the preset global coordinate system, obtain the spatial coordinates of each point cloud point in the global coordinate system, and obtain a unified point cloud; The meshing module is used to generate a triangular mesh based on the unified point cloud to obtain a three-dimensional plane as a preliminary three-dimensional geometric model.

Images