Drilling core three-dimensional digital modeling method, system, equipment and program

Abstract

The invention discloses a drilling core three-dimensional digital modeling method, system, device and program, and relates to the technical field of three-dimensional modeling, and the method comprises the steps: carrying out the preliminary image collection of a to-be-detected drilling core sample according to preset parameters, and obtaining a preliminary image set; performing three-dimensional reconstruction on the initial image set to obtain an initial three-dimensional point cloud model of the to-be-detected drill core sample; performing inversion based on the preliminary three-dimensional point cloud model to obtain a reflection attribute parameter inversion model; optimizing preset parameters based on the reflection attribute parameter inversion model; according to the optimized image acquisition parameters, performing high-fidelity image acquisition on the to-be-detected drill core sample to obtain a high-fidelity image set; and performing three-dimensional reconstruction based on the high-fidelity image set to generate a three-dimensional digital model of the drilled core sample. Through the processing scheme disclosed by the invention, the whole acquisition system is dynamically optimized according to the physical and optical characteristics of the core surface, and higher-precision and more universal three-dimensional modeling is realized.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional modeling technology, and in particular to a method, system, device and program for three-dimensional digital modeling of borehole cores. Background Technology

[0002] Drill core samples are a core research object in fields such as geological exploration, mineral resource assessment, and geological disaster early warning. Three-dimensional digital modeling of core samples enables digital storage, visualization, and precise analysis of core information, providing reliable data support for subsequent geological research. Currently, core digital modeling methods based on 3D scanning technology are widely used, and their core processes typically include core image acquisition, image preprocessing, 3D model synthesis, and model optimization.

[0003] Drill cores of different lithologies exhibit significantly different characteristics due to variations in composition and structure. During image acquisition, camera parameters such as exposure rate, contrast, and white balance directly determine the clarity, color reproduction, and detail of the core images. However, cores with different characteristics exhibit significant differences in their ability to reflect and absorb light. If fixed camera parameters are used to photograph cores of different lithologies, it is often impossible to achieve satisfactory imaging results for all types of cores.

[0004] In existing technologies, most methods rely on manual adjustment of camera parameters to adapt to core images of different lithologies. This is not only cumbersome and inefficient, but also dependent on the operator's experience, making it difficult to guarantee the accuracy of parameter settings. This can easily lead to problems such as overexposure, underexposure, color distortion, and blurred details in some core images, thus affecting the accuracy and clarity of subsequent 3D model synthesis and failing to meet the requirements of high-precision geological research for digital core models. In addition, while some automatic modeling equipment has parameter adjustment functions, it cannot automatically optimize parameter settings according to lithology, and the modeling effect still needs improvement.

[0005] Therefore, there is an urgent need for a method that can automatically match the optimal image acquisition scheme based on the characteristics of borehole cores, and achieve efficient and high-precision three-dimensional digital modeling of cores, in order to solve the shortcomings of existing technologies. Summary of the Invention

[0006] In view of this, the present disclosure provides a method for three-dimensional digital modeling of borehole cores, which at least partially solves the problems existing in the prior art.

[0007] In a first aspect, embodiments of this disclosure provide a method for three-dimensional digital modeling of borehole core samples, the method comprising the following steps: Preliminary image acquisition is performed on the core sample of the borehole to be tested according to preset parameters to obtain a preliminary image set; The preliminary image set is reconstructed in three dimensions to obtain a preliminary three-dimensional point cloud model of the borehole core sample to be tested; Based on the preliminary three-dimensional point cloud model, an inversion model of reflection attribute parameters is obtained. The preset parameters are optimized based on the inversion model of the reflection property parameters. Based on the optimized image acquisition parameters, high-fidelity images of the borehole core samples under test are acquired to obtain a set of high-fidelity images; Based on the high-fidelity image set, a three-dimensional reconstruction is performed to generate a three-dimensional digital model of the borehole core sample.

[0008] According to a specific implementation of an embodiment of this disclosure, the preliminary image acquisition of the core sample from the borehole to be tested according to preset parameters to obtain a preliminary image set includes: The core sample from the borehole to be tested was fixed on a five-axis stage; Adjust the active light source based on preset lighting parameters; The five-axis stage is controlled to rotate one revolution. At the same time, the camera is controlled to continuously acquire a set of multi-view images of the surface of the borehole core sample to be tested based on preset camera parameters, so as to obtain a preliminary image set. Each image in the preliminary image set contains an image of the surface of the borehole core sample to be tested, the position information of the camera, and the rotation angle information of the five-axis stage.

[0009] According to a specific implementation of this disclosure, the step of performing three-dimensional reconstruction on the preliminary image set to obtain a preliminary three-dimensional point cloud model of the borehole core sample to be tested includes: Detect feature points in each image of the initial image set; Feature point matching is performed across all images to obtain the same feature point captured from different viewpoints; By performing bundle adjustment optimization on the same feature point captured from different perspectives, sparse 3D point clouds and accurate camera pose are obtained. Based on sparse 3D point cloud and accurate camera pose, dense point cloud is reconstructed to obtain a preliminary 3D point cloud model of the borehole core sample to be tested.

[0010] According to a specific implementation of this disclosure, the step of inverting the preliminary three-dimensional point cloud model to obtain a reflection attribute parameter inversion model includes: A simplified physical model of surface reflection is established based on the preliminary 3D point cloud model, the preliminary image set, and the calibrated system parameters; wherein, the calibrated system parameters include the camera's intrinsic and extrinsic parameters, as well as the known parameters of the active light source when capturing each image; The reflection attribute parameter that minimizes the difference between the predicted brightness value and the actual observed brightness value is obtained by substituting each point in the preliminary three-dimensional point cloud model into the simplified surface reflection physical model. Based on the reflection attribute parameters of each point in the preliminary 3D point cloud model, a reflection attribute parameter inversion model is generated.

[0011] According to a specific implementation of this disclosure, the optimization of preset parameters based on the reflection attribute parameter inversion model includes: Calculate the mean and standard deviation of the high light reflectance coefficient and roughness parameters of the entire borehole core sample surface; Determine the lighting mode to be used; where, when the average value of the specular reflectance coefficient is greater than the global determination threshold of the specular reflectance coefficient and the average value of the roughness parameter is greater than the global determination threshold of the roughness parameter, low-angle grazing illumination is used; otherwise, uniform diffuse illumination is used. The camera parameters are collaboratively optimized based on the parameters used in the lighting mode to obtain the optimized camera parameters.

[0012] According to a specific implementation of this disclosure, the step of generating a three-dimensional digital model of the borehole core sample by performing three-dimensional reconstruction based on the high-fidelity image set includes: Dense 3D reconstruction is performed based on the high-fidelity image set to generate a high-precision triangular mesh model; The method further includes incorporating the reflection attribute parameter inversion model as prior information for dense 3D reconstruction into the reconstruction algorithm.

[0013] Secondly, embodiments of this disclosure provide a three-dimensional digital modeling system for borehole core samples, the system comprising: The preliminary acquisition module is configured to acquire preliminary images of the core sample from the borehole under test according to preset parameters, and obtain a preliminary image set. A three-dimensional point cloud module is configured to perform three-dimensional reconstruction on the preliminary image set to obtain a preliminary three-dimensional point cloud model of the borehole core sample to be tested. The inversion module is configured to perform inversion based on the preliminary 3D point cloud model to obtain an inversion model of reflection attribute parameters; The optimization module is configured to optimize preset parameters based on the reflection property parameter inversion model; The high-fidelity acquisition module is configured to acquire high-fidelity images of the borehole core sample under test based on the optimized image acquisition parameters, and obtain a high-fidelity image set. A 3D module is configured to perform 3D reconstruction based on the high-fidelity image set to generate a 3D digital model of the borehole core sample.

[0014] Thirdly, embodiments of this disclosure also provide an electronic device, the electronic device comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to perform the three-dimensional digital modeling method for borehole cores as described in the first aspect or any implementation thereof.

[0015] Fourthly, embodiments of this disclosure also provide a non-transitory computer-readable storage medium storing computer instructions that, when executed by at least one processor, cause the at least one processor to perform the three-dimensional digital modeling method for borehole cores in the first aspect or any implementation thereof.

[0016] Fifthly, embodiments of this disclosure also provide a computer program product, the computer program product including a computing program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, which, when executed by a computer, cause the computer to execute the three-dimensional digital modeling method for borehole cores in the first aspect or any implementation thereof.

[0017] The 3D digital modeling method for borehole cores disclosed in this embodiment combines multi-view 3D scanning with surface reflection physical model solving to construct a perception-optimization closed loop, achieving proactive collaborative optimization of illumination and imaging. Based on the inverted physical properties, the optimal combination of light source and camera parameters is dynamically planned, effectively avoiding problems such as overexposure, underexposure, and detail blurring at the physical level. It proactively highlights key geological features such as microtextures, fractures, and mineral luster, obtaining high-fidelity images with maximized information content. Furthermore, through the process of preliminary scanning → attribute inversion → parameter optimization → high-fidelity scanning → fusion reconstruction, the optimal acquisition scheme can be automatically formulated and executed according to the physical characteristics of the object, significantly improving the targeting, efficiency, and consistency of data acquisition, achieving fully automated and intelligent 3D digital modeling. Attached Figure Description

[0018] Figure 1 This is a schematic flowchart of a three-dimensional digital modeling method for borehole cores provided in an embodiment of the present disclosure; Figure 2 A flowchart of a three-dimensional digital modeling method for borehole cores provided in this embodiment of the present disclosure; Figure 3 A schematic diagram of a three-dimensional digital modeling system for borehole cores provided in this embodiment of the present disclosure; Figure 4 A schematic diagram of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0019] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0020] The following specific examples illustrate the implementation of this disclosure. Those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0021] It should be noted that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. Furthermore, this device and / or this method can be implemented using other structures and / or functionalities besides one or more of the aspects set forth herein.

[0022] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0023] This invention provides a method for 3D digital modeling of borehole core samples. The method involves a preliminary scan of the core sample, using multi-view images to estimate the initial geometry and retrieving key reflection properties of the core surface. Based on these reflection properties, the optimal active light source configuration and camera parameters are dynamically calculated, and a second high-precision scan is performed using the updated parameters. Finally, in the 3D reconstruction stage, the retrieved surface reflection properties are used to enhance the model's materials, generating a digital core model that combines high-precision geometry with a realistic appearance.

[0024] Figure 1 This is a schematic diagram of the three-dimensional digital modeling method for borehole cores provided in the embodiments of this disclosure.

[0025] Figure 2 To and Figure 1 The corresponding flowchart of the three-dimensional digital modeling method for borehole cores.

[0026] like Figure 1 As shown, in step S110, preliminary image acquisition is performed on the core sample of the borehole to be tested according to preset parameters to obtain a preliminary image set.

[0027] In this embodiment of the invention, the preliminary image acquisition of the borehole core sample to be tested according to preset parameters to obtain a preliminary image set includes: fixing the borehole core sample to be tested on a five-axis stage; adjusting the active light source based on preset illumination parameters; controlling the five-axis stage to rotate one revolution, and simultaneously controlling the camera to continuously acquire a set of multi-view images of the surface of the borehole core sample to be tested based on preset camera parameters to obtain a preliminary image set; wherein, each image in the preliminary image set includes an image of the surface of the borehole core sample to be tested, camera position information, and rotation angle information of the five-axis stage.

[0028] More specifically, before image acquisition begins, the borehole core samples to be tested are preprocessed, including cleaning, cutting and trimming the acquired borehole cores to remove surface dust, impurities and damaged parts, ensuring that the core surface is flat and the features are clear.

[0029] The core sample from the borehole to be tested is then fixed on the five-axis stage of the 3D scanning and modeling equipment. This stage allows for precise control of rotation and elevation. The 3D scanning and modeling equipment also includes a high-definition industrial camera and a set of independently programmable ring-shaped LED active light sources. These light sources can adjust their intensity and color temperature and are positioned at different azimuth angles. The five-axis stage can adjust the core's orientation to ensure optimal observation from all sides, especially for irregular cores. This controllable multi-light source provides the hardware foundation for subsequent active illumination optimization. The 3D scanning and modeling equipment is started, and the stage is rotated one revolution using a set of preset, conservative general parameters. A high-definition industrial camera is then used to continuously acquire a set of multi-view images of the core surface. Each image contains core image information, camera position at the time of taking the picture, and stage rotation angle information, which serve as initial values ​​for subsequent processing.

[0030] For example, a set of fixed and safe preset operating parameters for the camera and light source: camera parameters include a medium exposure time (e.g., 1 / 60 second), a moderate ISO (200), and automatic white balance; light source parameters include using uniform neutral color temperature white light (e.g., 5000K-6500K) and moderate light intensity (adjusting the light source brightness under fixed camera parameters (e.g., aperture f / 8, ISO 200, exposure time 1 / 60 second) so that the peak of the histogram of the captured image is located in the mid-tone area (i.e., around grayscale value 128).

[0031] This set of parameters is exploratory, aiming to ensure the safe acquisition of the first set of images under completely unknown core surface characteristics, avoiding invalid data such as completely black (severely underexposed) or completely white (severely overexposed). It does not pursue optimal image quality, but only aims to obtain images with complete information that can be used for subsequent sparse reconstruction and preliminary analysis. Conversely, images that do not exhibit severe underexposure or overexposure can be used as preliminary images, and the corresponding parameters are the preset parameters.

[0032] Existing technologies typically use fixed light sources or simple diffused light sources, and the stage can only rotate. In contrast, this invention introduces a multi-degree-of-freedom stage and a controllable multi-light source system, providing the possibility for physical-based optimization at the execution level, which is the foundation for implementing subsequent algorithms.

[0033] More specifically, we now proceed to step S120.

[0034] In step S120, the preliminary image set is reconstructed in three dimensions to obtain a preliminary three-dimensional point cloud model of the borehole core sample to be tested.

[0035] In this embodiment of the invention, the step of performing three-dimensional reconstruction on the preliminary image set to obtain a preliminary three-dimensional point cloud model of the borehole core sample to be tested includes: detecting feature points in each image of the preliminary image set; performing feature point matching among all images to obtain the same feature point captured from different viewpoints; performing bundle adjustment optimization on the same feature point captured from different viewpoints to obtain a sparse three-dimensional point cloud and a precise camera pose; and performing dense point cloud reconstruction based on the sparse three-dimensional point cloud and the precise camera pose to obtain a preliminary three-dimensional point cloud model of the borehole core sample to be tested.

[0036] More specifically, using Structure of Motion (SfM) and Multi-View Stereo Vision (MVS) algorithms, based on image sets... Generate a preliminary, sparse 3D point cloud of the core. . Each point within Includes three-dimensional coordinates And from multiple (≥2) different perspectives Observed.

[0037] Furthermore, the input image set is processed using the Structure from Motion (SfM) algorithm. Specifically, it includes the following steps: 1. Feature Point Extraction and Matching: First, for each input image, feature points with saliency and invariance are extracted using a feature point detection algorithm (such as SIFT, ORB, or SURF). Then, feature point matching is performed between all image pairs to find feature point pairs that appear in different images and correspond to the same 3D point in the real world, forming a series of 2D feature point correspondences.

[0038] 2. Sparse point cloud and camera pose estimation: Through bundle adjustment optimization, two core results are solved simultaneously: Sparse 3D point cloud: This is the precise set of coordinates of successfully matched feature points in 3D space. The resulting point cloud is sparse because it only contains easily matched feature points, rather than the complete surface of the object.

[0039] Precise camera pose: Select a pair of images with a sufficient number of well-distributed matching feature points as the initial image pair. Based on the matching points of this pair of images, calculate the essential matrix using algorithms such as the five-point method or the eight-point method. Decompose the essential matrix to obtain the relative rotation and translation between the two images. Due to scale uncertainty, the translation vector at this time is a unit vector.

[0040] Using the recovered relative pose and matched point pairs, the initial points of the first batch of sparse point clouds are calculated through triangulation. This establishes an initial two-view graph with scale uncertainty. Figure 3 Dimensional scene.

[0041] The third and subsequent images are gradually added to the established reconstruction. For each new image, the PnP algorithm is used to solve for the camera pose of the new image relative to the constructed scene, utilizing its 2D-3D correspondence with existing 3D points. The pose of the new image is used to perform feature matching with the previous image, triangulation is performed to generate new 3D points, and these points are added to the point cloud. After each addition of a new image and 3D points, a small-scale bundle adjustment is performed to optimize all current parameters and reduce error accumulation.

[0042] Bundle adjustment (BA) aims to achieve global optimization by minimizing reprojection error, which is the sum of squares of the differences between the locations of all observed 2D feature points and the locations of their corresponding 3D points projected onto the image plane.

[0043] in, Number of images; Number of 3D points; For indicator variables, if 3D point In the image If it is visible in the middle, the value is 1; otherwise, it is 0. 3D points In the image 2D pixel coordinates observed in the image; For camera projection functions, it will project a 3D point Through the camera's intrinsic parameter matrix ,attitude Projected onto image On the 2D pixel plane; For the first The camera rotation matrix and translation vector of the image (i.e., the precise position to be optimized); For the first Spatial coordinates of a 3D point.

[0044] The above equation is solved iteratively using the Levenberg-Marquardt algorithm. Through optimization, the poses of all cameras are simultaneously refined. and coordinates of all 3D points This makes them the best fit for all 2D observation data overall.

[0045] The final output is each image. Precise camera pose in three-dimensional space and preliminary 3D point cloud .

[0046] Based on the spatial geometric relationship between the camera, light source, and point, the incident angle of the light source for each observation is calculated. Geometric parameters such as observation direction.

[0047] The incident direction vector and incident angle of the light source are calculated based on the following formula. : The direction vector of the light source, i.e., from the point on the surface of the rock core. (Point cloud from sparse 3D reconstruction) Pointing to the light source The vector.

[0048]

[0049] in, For core surface points Pointing to the The light source used in this observation The direction vector; For the first During the second observation, the light source Three-dimensional spatial coordinates in the world coordinate system; For core surface points Three-dimensional coordinates in the world coordinate system; The number of observations; The sequence number of a point in a sparse 3D point cloud.

[0050] Normalize the light source direction vector:

[0051] in, For vectors The result after normalization; For vectors The model, .

[0052] The angle of incidence is the complementary angle between the direction of the ray and the surface normal vector. According to the dot product formula, we know that:

[0053] in, The incident angle of the light source; For core surface points The unit normal vector at that location (estimated by local surface fitting of the sparse point cloud).

[0054] Right now,

[0055] The range of values ​​is When light shines perpendicularly onto the surface, .

[0056] The observation direction vector is calculated based on the following formula:

[0057] in, To indicate from the core surface point Pointing to the Camera optical center during the second observation The direction vector; For the first During this observation, the three-dimensional spatial coordinates of the camera's optical center in the world coordinate system are... , Let be the rotation matrix of the camera coordinate system relative to the world coordinate system. This is the translation vector of the camera coordinate system relative to the world coordinate system.

[0058] Normalize the observation direction vector:

[0059] in, To observe the direction vector The result after normalization; For vectors The model.

[0060] Calculate the reflection direction vector of an ideal mirror and the viewing angle. (For specular models): The ideal mirror reflection direction vector is calculated based on the following formula. :

[0061] in, Let be the direction vector of ideal mirror reflection, and its unit vector be . .

[0062] Calculate the observation angle using the following formula. (Observation direction) Reflection direction of ideal mirror (the angle between them)

[0063] The smaller the value, the closer the observation direction is to the direction of specular reflection, and the stronger the observed highlight may be.

[0064] 3. Multi-view stereo vision (MVS) reconstruction: After obtaining accurate camera pose and sparse point cloud, dense reconstruction is performed using multi-view stereo vision technology.

[0065] The goal of MVS is to calculate depth information for the entire visible surface of an object based on known camera geometry, thereby generating a dense point cloud in which the intervals between points are small enough to describe the continuous surface morphology of the object.

[0066] The MVS algorithm selects a large number of pixels (not just feature points) on the image and utilizes disparity information from multiple viewpoints to calculate the 3D coordinates of these pixels through methods such as stereo matching, patch matching, or depth map fusion. Finally, it fuses the 3D points calculated from all viewpoints to form a dense 3D point cloud that completely covers the visible surface of the core sample. .

[0067] 4. Output results: The final output of this step is a preliminary three-dimensional point cloud model of the core with accurate geometric relationships (composed of sparse point cloud and dense point cloud).

[0068] This step connects the initial image acquisition with the physical property inversion, enabling the recovery of an ordered three-dimensional spatial structure from a disordered image.

[0069] Next, proceed to step S130.

[0070] In step S130, an inversion is performed based on the preliminary three-dimensional point cloud model to obtain the reflection attribute parameter inversion model.

[0071] In this embodiment of the invention, the inversion based on the preliminary three-dimensional point cloud model to obtain the reflection attribute parameter inversion model includes: establishing a simplified surface reflection physical model based on the preliminary three-dimensional point cloud model, the preliminary image set, and calibrated system parameters; wherein, the calibrated system parameters include the camera's intrinsic and extrinsic parameters, as well as the known parameters of the active light source when capturing each image; solving for the reflection attribute parameters that minimize the difference between the predicted brightness value and the actual observed brightness value after substituting each point in the preliminary three-dimensional point cloud model into the simplified surface reflection physical model; and generating the reflection attribute parameter inversion model based on the reflection attribute parameters of each point in the preliminary three-dimensional point cloud model.

[0072] More specifically, for each of the core surfaces Let's establish a simplified reflection model. Assume the surface is a linear combination of Lambertian (diffuse) and non-Lambertian (specular) reflectance, and its brightness at a certain image pixel... The calculation method is as follows:

[0073] in, The observed pixel brightness value (0-255); The diffuse reflectance coefficient represents the inherent reflectivity of a material, independent of viewing angle, and determines the basic color of an object. For rock cores, this parameter is related to mineral composition and weathering surface, and is usually a scalar or RGB vector between 0 and 1. The larger the value, the stronger the diffuse light reflected at that point, making it appear brighter or more saturated in color. The specular reflectance coefficient represents the ability of a surface to produce a specular highlight. It is related to surface smoothness or the luster of certain minerals. For example, smooth fracture surfaces or certain metallic minerals (such as pyrite) have a high specular reflectance coefficient. The value is usually a scalar between 0 and 1; the larger the value, the stronger the highlight. The intensity of the light source; Let N be the angle between the incident direction of the light source and the surface normal N. This represents Lambert's law of cosines. The angle between the observation direction and the ideal mirror reflection direction; For surface roughness parameters, The higher the value, the more concentrated the highlights and the smoother the surface; conversely, the lower the value, the more diffuse the highlights and the rougher the surface. It is a positive real number; This is the distance from the light source to the point on the surface.

[0074] For each surface point It has images from different perspectives Multiple brightness values ​​observed in and the known light source parameters at the corresponding time. and geometric relations By minimizing the error between all observed brightness values ​​and model predictions, it is possible to obtain the desired brightness for each point. Solve for an optimal set of reflection property parameters number.

[0075]

[0076] in, To observe the brightness value; This represents the brightness value predicted by the model.

[0077] This step transforms the visual appearance of the core surface into physically interpretable parameters. These parameters are independent of specific imaging conditions and represent the inherent optical properties of the core surface, providing a basis for subsequent active optimization. Regardless of whether the rock is granite, sandstone, or an unknown lithology, the system can obtain its surface properties through mathematical inversion. The parameter diagram has greater universality.

[0078] Furthermore, the specific steps for constructing the reflection attribute parameter inversion model are as follows: Step 1: Input data preparation and association.

[0079] Input preliminary 3D point cloud Image collection The calibrated system parameters include the camera's intrinsic parameters (focal length, principal point, distortion coefficients) and extrinsic parameters (precise position and orientation at the time of each image capture, calculated by SfM), as well as the known parameters of the active light source at the time of each image capture (intensity). Color temperature and spatial location).

[0080] The calibrated system parameters form the physical basis for all subsequent quantitative calculations. Their function is to correlate the pixel coordinates in the image with the three-dimensional spatial coordinates in the real world, as well as the geometric relationships of illumination (such as the angle of incidence). They are precisely linked together.

[0081] Step 2: Establish a simplified physical model of surface reflection.

[0082] The following formula describes the reflection behavior of an object's surface on light, linking the observed pixel brightness E with unknown, desired intrinsic surface properties, and known, controllable shooting conditions:

[0083] This formula indicates that the total brightness received by the camera sensor from a point on the surface is... , Represents the diffuse component, which is independent of the viewing direction and depends only on the light intensity and the angle of incidence, presenting a uniform, soft color. Representing the specular (reflective) component, it is highly correlated with the viewing direction and appears as a bright, concentrated spot of light. Its distribution is determined by the roughness parameter. control.

[0084] Step 3: Construct the inversion problem.

[0085] For each point in the sparse point cloud It has observed brightness values ​​from n different viewpoints. and what is known at each observation Conditional parameters, etc.

[0086] We need to find a set of unknown reflection property parameters. This allows the calculated predicted brightness values ​​to be obtained by substituting them into the reflection model. The difference between the actual observed brightness value and the actual brightness value is minimal.

[0087] The solution is based on the following formula:

[0088] Numerical optimization algorithms (such as the Levenberg-Marquardt algorithm, Gauss-Newton method, etc.) are used to solve the above objective function, and the parameters are adjusted iteratively. The value of is gradually reduced to decrease the prediction error until it converges to a local optimum.

[0089] The output is for each 3D surface point Solving for the optimal set of reflection property parameters yields a set of optimal reflection property parameters. Ultimately, three two-dimensional parametric fields are formed on the visible surface of the entire core: a diffuse reflectance map, a specular reflectance map, and a roughness map.

[0090] This step constructs an inverse problem that infers surface physical properties from multi-view observation data. Through rigorous physical-optical models and numerical optimization, key optical material properties of the core surface are directly and quantitatively measured, providing a basis for subsequent active optimization of imaging conditions and generation of realistic digital models.

[0091] Next, proceed to step S140.

[0092] In step S140, the preset parameters are optimized based on the reflection property parameter inversion model.

[0093] In this embodiment of the invention, the optimization of preset parameters based on the reflection attribute parameter inversion model includes: illumination optimization, which includes: calculating the mean and standard deviation of the specular reflectance coefficient and roughness parameter of the entire borehole core sample surface; determining the illumination mode to be used; wherein, when the mean specular reflectance coefficient is greater than the global determination threshold of the specular reflectance coefficient and the mean roughness parameter is greater than the global determination threshold of the roughness parameter, low-angle grazing illumination is used; otherwise, uniform diffuse illumination is used; and the camera parameters are collaboratively optimized based on the parameters used in the illumination mode to obtain the optimized camera parameters.

[0094] More specifically, the active illumination-imaging parameter co-optimization based on reflectance properties includes the following steps: I. Overall Analysis and Basic Strategy Formulation 1. Calculate global statistical characteristics: Calculate the entire core surface and mean , and standard deviation , .

[0095] 2. Basic lighting mode settings: The mean and standard deviation of the core surface were determined when... and At that time, it was determined that the core was generally smooth and had obvious highlights, and the basic strategy adopted was low-angle grazing illumination. near This enhances the visual contrast of surface undulations globally.

[0096] Otherwise, if the core is determined to be generally coarse and dominated by diffuse reflection, the basic strategy is to use uniform diffuse illumination (such as ring-shaped soft light) to accurately reconstruct the core. The color information it represents.

[0097] in, Based on the statistical values ​​set in a common rock sample library, the samples in the rock sample library are preliminarily scanned and their reflectance properties are inverted using the methods in steps S110-S130, and the surface area of ​​each sample is calculated. average value( ) and distribution. For all standard samples Statistical analysis is performed, and frequency distribution histograms or cumulative distribution plots are drawn. The samples are then divided into two classes by selecting the upper quartile or by using cluster analysis (such as K-means). In this embodiment of the invention, The value range is 0.10-0.20. If a system is to be more sensitive to specular features (i.e., employing a grazing light strategy more often), the lower limit of the range can be used; if a more robust strategy is desired to avoid misjudging slight gloss, the upper limit of the range can be used. Unless otherwise specified, The preferred value range is 0.12-0.15.

[0098] Used to measure highlight concentration and The calculations are performed using the same rock sample library and the same method, and will not be elaborated upon here. In the embodiments of the present invention, The value range is 20-30, preferably 25.

[0099] II. Dynamic Optimization of Local Illumination This step involves segmentation and light optimization based on the specific characteristics of different regions on the core surface.

[0100] 1. Region segmentation and feature extraction: The core surface is meshed or locally clustered using point cloud computing. For each region A, its average reflectance property is calculated. ,in, The average diffuse reflectance coefficient of region A; The average specular reflectance of region A; Let be the average roughness parameter of region A.

[0101] 2. Local lighting mode decision: when and At that time, among them , The region is determined to be a highlight / smooth area (such as a mineral cleavage surface). This is the local threshold for determining the specular reflectance. This is the local threshold for roughness parameters.

[0102] Analysis of all samples and all regions in the rock sample library and The joint distribution of values ​​determines whether a region is considered a salient feature requiring local optimization only when its attribute value exceeds that of most regions (80%–95%) in the library. The value range is 0.20-0.35, preferably 0.25. The value range is 30-50, preferably 35.

[0103] At this point, select to enable 1-2 side point light sources. By adjusting the position of the light source, the angle of incidence can be... Set to the grazing angle, preferably .

[0104] To prevent overexposure, the light source intensity It can be deduced from the inversion model.

[0105] Desired high gloss The saturation level should be lower than the camera's saturation value, calculated based on the following formula:

[0106] Right now,

[0107] in, The target highlight value is (preferably 200 / 255). It can be calculated based on the current perspective; This is the distance from the light source to the point on the surface of the rock core.

[0108] when When this occurs, the area is determined to be a diffuse / textured area (such as coarse sandstone).

[0109] At this point, the ring-shaped diffused light source is activated, and the intensity... According to Make adjustments. , The constant is used to ensure that the expected brightness is moderate, where The value is obtained through system calibration.

[0110] III. Collaborative Optimization of Camera Parameters Camera parameters are matched in real time with dynamically changing lighting to ensure image quality.

[0111] 1. Predict scene brightness: For the next frame to be captured, based on the light source parameters from step two... , area reflection properties In addition to geometric relationships, the average brightness of the image is predicted using a complete reflection model. and the brightest highlight value .

[0112] Calculate points based on the following formula The predicted radiance value Epred,j under the assumption of a uniform neutral gray response camera. This value is the sum of contributions from all enabled light sources:

[0113] in, To ensure that the core surface is under the planned lighting conditions, The predicted radiance at a point is a relative physical quantity that represents the radiance level that point should present on a hypothetical neutral-response camera. It forms the basis for subsequent calculations of average and maximum radiance. This is the serial number of the light source; For the surface of the core The diffuse reflectance coefficient of a point is obtained through inversion and represents the physical property of the point's inherent color. For the first The light intensity of each light source; For the first The incident ray from the light source and the point The angle between the surface normals (angle of incidence); For the surface of the core The specular reflectance coefficient of a point, obtained based on inversion, represents the ability of that point to produce specular highlights; For the direction of observation and the first A light source at a point Adjust the angle between the direction of the mirror reflection; For the surface of the core The surface roughness parameters at each point, obtained through inversion, control the concentration of specular highlights when the viewing direction deviates from the reflection direction ( (As it decreases), the value of this item decreases sharply; For the first The surface point to the first The square of the distance to each light source.

[0114] The average luminance prediction value is calculated based on the following formula. :

[0115] in, The predicted average brightness of the entire frame image is used to set the camera exposure parameters to ensure that the overall brightness of the image is moderate. This represents the total number of surface points visible in the camera's field of view on the core surface at the current stage pose.

[0116] The brightest highlight value is calculated using the following formula. :

[0117] in, This is the predicted brightness value of the brightest highlight area in the entire frame of the image.

[0118] 2. Exposure parameters Calculation: make sure The average brightness should not exceed the saturation level of the camera sensor (e.g., 250 / 255). Within the optimal range (preferably 120-180 / 255).

[0119] Exposure parameters are based on the following formula. Perform the calculation:

[0120] in, The target grayscale value is (preferably 118-138). These are system constants, obtained through system calibration.

[0121] An automatic exposure algorithm is used, with the constraint of preventing highlight clipping being a hard constraint, and the solution is iteratively obtained.

[0122] 3. White Balance Optimization: Based on global and key area settings. By adjusting the ratio of the mean across all RGB channels, the camera's white balance gain is finely tuned to ensure that the captured gray-white standard board area appears as neutral gray in the image.

[0123] IV. Generating and Executing Optimized Instruction Sequences 1. Sequence Generation: Bind the stage rotation plan, light source configuration, and camera parameter presets to generate a time-base control sequence. The sequence can include conditional statements, such as: when rotating to a certain angle... If the brightness variance of a certain area in the real-time view exceeds the threshold, a fine adjustment is made, corresponding to a sidelight intensity of -10%.

[0124] 2. Execution and Fine-tuning: The system performs high-fidelity scanning sequentially. Simultaneously, the algorithm monitors the acquired images in real time. If the actual imaging effect (such as sharpness and contrast) deviates from the prediction beyond the tolerance, a small-scale parameter fine-tuning loop can be triggered based on the value range of each parameter to ensure optimal image quality for each frame.

[0125] This step translates the physical parameters obtained from the inversion into executable hardware control commands. Different reflection properties correspond to different optimal observation lighting conditions, and the system performs customized lighting and imaging accordingly, thereby creating the best imaging environment for each core sample, or even different areas of the core sample.

[0126] This step fundamentally solves problems such as overexposure, underexposure, and blurred details, and in particular, it can significantly improve the imaging quality of surface micro-textures (dependent on grazing light) and glossy areas (requiring control of highlights).

[0127] Next, proceed to step S150.

[0128] In step S150, high-fidelity images of the borehole core sample to be tested are acquired according to the optimized image acquisition parameters to obtain a high-fidelity image set.

[0129] More specifically, the optimal acquisition strategy (a series of light source and camera parameter combinations and corresponding stage poses) is loaded. The stage and light sources are controlled to perform automated acquisition according to the strategy, obtaining a high-fidelity image set. During this process, key areas can be photographed in multiple modes (such as different lighting angles). Furthermore, the number of viewing angles for high-fidelity image acquisition is 1.5-2 times that of the initial acquisition, ensuring comprehensive coverage of surface details.

[0130] Next, proceed to step S160.

[0131] In step S160, a three-dimensional reconstruction is performed based on the high-fidelity image set to generate a three-dimensional digital model of the borehole core sample.

[0132] In this embodiment of the invention, the step of performing three-dimensional reconstruction based on the high-fidelity image set to generate a three-dimensional digital model of the borehole core sample includes: performing dense three-dimensional reconstruction based on the high-fidelity image set to generate a high-precision triangular mesh model; the method further includes: incorporating the reflection attribute parameter inversion model as prior information for dense three-dimensional reconstruction into the reconstruction algorithm.

[0133] More specifically, using high-fidelity image sets Perform intensive 3D reconstruction to generate a high-precision triangular mesh model. During this process, the surface reflection property parameters obtained through inversion can be... It is incorporated into the reconstruction algorithm as an additional constraint or prior information.

[0134] For example, in stereo matching, This helps to perform more robust color consistency checks from different viewing angles and reduces the impact of highlights. This resulted in a mismatch.

[0135] Feeding optical properties back to the geometric reconstruction process can improve matching accuracy, especially in areas with repetitive or weak textures.

[0136] This operation breaks down the barriers between imaging and reconstruction. By utilizing the intermediate layer of information—reflection properties—it enables 3D reconstruction to depend not only on image grayscale / color but also on the physical properties of the surface, thereby generating models with higher geometric accuracy and, in particular, richer details.

[0137] Furthermore, the inverted reflection attribute parameter field Mapping to high-precision mesh model On each vertex or texture map, generate a value containing geometry and diffuse color (by...). Derivative), highlights and roughness (from) A complete PBR (physically based rendering) material model derived from it.

[0138] The final model can be exported to a universal format (such as gITF, USD) that includes color texture maps and material maps (such as roughness maps and normal maps), or a compatible OBJ / PLY format.

[0139] The invention generates digital rock cores with realistic material properties, which are not only more visually realistic, but their material parameters (such as roughness) can also serve as a new data dimension for quantitative analysis of geological features (such as surface weathering degree and mineral luster), greatly enhancing the scientific analysis value of digital models.

[0140] The proposed method for 3D digital modeling of borehole core samples involves initial image acquisition of the sample with preset parameters, followed by 3D reconstruction and inversion of the preliminary image set to optimize the preset parameters. Resampling is then performed based on the optimized parameters, and 3D reconstruction is conducted again based on the resampling results to generate a 3D digital model of the borehole core sample. This method solves the problems of poor imaging quality, low modeling accuracy, and low operational efficiency caused by the inability of camera parameters to adaptively adjust according to lithology in existing 3D digital modeling processes for borehole cores, thus improving the efficiency and quality of core digital modeling.

[0141] Figure 3 The present invention illustrates a three-dimensional digital modeling system 300 for borehole cores, comprising a preliminary acquisition module 310, a three-dimensional point cloud module 320, an inversion module 330, an optimization module 340, a high-fidelity acquisition module 350, and a three-dimensional module 360.

[0142] The preliminary acquisition module 310 is used to acquire preliminary images of the core sample from the borehole under test according to preset parameters, and obtain a preliminary image set; The three-dimensional point cloud module 320 is used to perform three-dimensional reconstruction on the preliminary image set to obtain a preliminary three-dimensional point cloud model of the borehole core sample to be tested. Inversion module 330 is used to perform inversion based on the preliminary three-dimensional point cloud model to obtain an inversion model of reflection attribute parameters; The optimization module 340 is used to optimize the preset parameters based on the reflection attribute parameter inversion model; The high-fidelity acquisition module 350 is used to acquire high-fidelity images of the borehole core sample under test according to the optimized image acquisition parameters, and obtain a high-fidelity image set; The 3D module 360 ​​is used to perform 3D reconstruction based on the high-fidelity image set to generate a 3D digital model of the borehole core sample.

[0143] See Figure 4 This disclosure also provides an electronic device 40, which includes: At least one processor; and, The memory is communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the three-dimensional digital modeling method for borehole cores in the foregoing method embodiments.

[0144] This disclosure also provides a non-transitory computer-readable storage medium that stores computer instructions for causing the computer to execute the three-dimensional digital modeling method for borehole cores in the foregoing method embodiments.

[0145] This disclosure also provides a computer program product, which includes a computing program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, cause the computer to perform the three-dimensional digital modeling method for borehole cores in the foregoing method embodiments.

[0146] The following is for reference. Figure 4 The diagram illustrates a structural schematic of an electronic device 40 suitable for implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0147] like Figure 4 As shown, electronic device 40 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from storage device 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of electronic device 40. The processing unit 401, ROM 402, and RAM 403 are interconnected via bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0148] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic device 40 to communicate wirelessly or wiredly with other devices to exchange data. Although electronic device 40 with various devices is shown in the figure, it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0149] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a storage device 408, or installed from a ROM 402. When the computer program is executed by the processing device 401, it performs the functions defined in the methods of embodiments of this disclosure.

[0150] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0151] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0152] The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquire at least two Internet Protocol (IP) addresses; send a node evaluation request including the at least two IP addresses to a node evaluation device, wherein the node evaluation device selects an IP address from the at least two IP addresses and returns it; and receive the IP address returned by the node evaluation device; wherein the acquired IP address indicates an edge node in a content delivery network.

[0153] Alternatively, the aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: receive a node evaluation request including at least two Internet Protocol (IP) addresses; select an IP address from the at least two IP addresses; and return the selected IP address; wherein the received IP address indicates an edge node in the content delivery network.

[0154] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0155] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0156] The units described in the embodiments of this disclosure can be implemented in software or in hardware. The name of a unit does not necessarily limit the unit itself; for example, the first acquisition unit can also be described as "a unit that acquires at least two Internet Protocol addresses".

[0157] It should be understood that the various parts of this disclosure can be implemented in hardware, software, firmware, or a combination thereof.

[0158] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for three-dimensional digital modeling of borehole cores, characterized in that, The method includes the following steps: Preliminary image acquisition is performed on the core sample of the borehole to be tested according to preset parameters to obtain a preliminary image set; The preliminary image set is reconstructed in three dimensions to obtain a preliminary three-dimensional point cloud model of the borehole core sample to be tested; Based on the preliminary three-dimensional point cloud model, an inversion model of reflection attribute parameters is obtained. The preset parameters are optimized based on the inversion model of the reflection property parameters; Based on the optimized image acquisition parameters, high-fidelity images of the borehole core samples under test are acquired to obtain a set of high-fidelity images; Based on the high-fidelity image set, a three-dimensional reconstruction is performed to generate a three-dimensional digital model of the borehole core sample.

2. The three-dimensional digital modeling method for borehole cores according to claim 1, characterized in that, The preliminary image acquisition of the core sample from the borehole to be tested is performed according to preset parameters to obtain a preliminary image set, including: The core sample from the borehole to be tested was fixed on a five-axis stage; Adjust the active light source based on preset lighting parameters; The five-axis stage is controlled to rotate one revolution. At the same time, the camera is controlled to continuously acquire a set of multi-view images of the surface of the borehole core sample to be tested based on preset camera parameters, so as to obtain a preliminary image set. Each image in the preliminary image set contains an image of the surface of the borehole core sample to be tested, the position information of the camera, and the rotation angle information of the five-axis stage.

3. The three-dimensional digital modeling method for borehole cores according to claim 1, characterized in that, The step of performing three-dimensional reconstruction on the preliminary image set to obtain a preliminary three-dimensional point cloud model of the borehole core sample to be tested includes: Detect feature points in each image of the initial image set; Feature point matching is performed across all images to obtain the same feature point captured from different viewpoints; By performing bundle adjustment optimization on the same feature point captured from different perspectives, sparse 3D point clouds and accurate camera pose are obtained. Based on sparse 3D point cloud and accurate camera pose, dense point cloud is reconstructed to obtain a preliminary 3D point cloud model of the borehole core sample to be tested.

4. The three-dimensional digital modeling method for borehole cores according to claim 1, characterized in that, The inversion based on the preliminary 3D point cloud model to obtain the reflection attribute parameter inversion model includes: A simplified physical model of surface reflection is established based on the preliminary 3D point cloud model, the preliminary image set, and the calibrated system parameters; wherein, the calibrated system parameters include the camera's intrinsic and extrinsic parameters, as well as the known parameters of the active light source when capturing each image; The reflection attribute parameter that minimizes the difference between the predicted brightness value and the actual observed brightness value is obtained by substituting each point in the preliminary three-dimensional point cloud model into the simplified surface reflection physical model. Based on the reflection attribute parameters of each point in the preliminary 3D point cloud model, a reflection attribute parameter inversion model is generated.

5. The three-dimensional digital modeling method for borehole cores according to claim 1, characterized in that, The optimization of preset parameters based on the reflection property parameter inversion model includes: Calculate the mean and standard deviation of the high light reflectance coefficient and roughness parameters of the entire borehole core sample surface; Determine the lighting mode to be used; where, when the average value of the specular reflectance coefficient is greater than the global determination threshold of the specular reflectance coefficient and the average value of the roughness parameter is greater than the global determination threshold of the roughness parameter, low-angle grazing illumination is used; otherwise, uniform diffuse illumination is used. The camera parameters are collaboratively optimized based on the parameters used in the lighting mode to obtain the optimized camera parameters.

6. The three-dimensional digital modeling method for borehole cores according to claim 1, characterized in that, The step of performing three-dimensional reconstruction based on the high-fidelity image set to generate a three-dimensional digital model of the borehole core sample includes: Dense 3D reconstruction is performed based on the high-fidelity image set to generate a high-precision triangular mesh model; The method further includes incorporating the reflection attribute parameter inversion model as prior information for dense 3D reconstruction into the reconstruction algorithm.

7. A three-dimensional digital modeling system for borehole cores, characterized in that, The system includes: The preliminary acquisition module is configured to acquire preliminary images of the core sample from the borehole under test according to preset parameters, and obtain a preliminary image set. A three-dimensional point cloud module is configured to perform three-dimensional reconstruction on the preliminary image set to obtain a preliminary three-dimensional point cloud model of the borehole core sample to be tested. The inversion module is configured to perform inversion based on the preliminary 3D point cloud model to obtain an inversion model of reflection attribute parameters; The optimization module is configured to optimize preset parameters based on the reflection property parameter inversion model; The high-fidelity acquisition module is configured to acquire high-fidelity images of the borehole core sample under test based on the optimized image acquisition parameters, and obtain a high-fidelity image set. A 3D module is configured to perform 3D reconstruction based on the high-fidelity image set to generate a 3D digital model of the borehole core sample.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, cause the at least one processor to perform the three-dimensional digital modeling method for borehole cores as described in any one of claims 1 to 6.

9. A computer program product, characterized in that, The computer program product includes a computing program stored on a non-transitory computer-readable storage medium, the computer program including program instructions that, when executed by a computer, cause the computer to perform the three-dimensional digital modeling method for borehole cores as described in any one of claims 1 to 6.

Images