355 results about "Surface point" patented technology

The invention relates to a welding seam path planning method and device, computer equipment and a readable storage medium. The method comprises the steps that workpiece surface point clouds of a to-be-welded workpiece are obtained, the workpiece surface point clouds are clustered, a point cloud clustering cluster is obtained, weld joint end points in the point cloud clustering cluster are detected, the number of the detected weld joint end points is determined, a weld joint exists between the weld joint end points, a first weld joint type of the weld joint is detected according to the number of the end points, and a second weld joint type of the weld joint is determined according to the first weld joint type. And detecting a second welding seam type of the welding seam based on the distance between the target vector between the welding seam end points and the mass center of the preset area where the welding seam is located, and performing welding path planning on the welding seam based on the first welding seam type and the second welding seam type. By adopting the method, the welding path can be accurately planned.

The invention discloses a point cloud registration method, system and device and a medium, and relates to the technical field of three-dimensional reconstruction of scenes, and the method comprises the steps: obtaining a source point cloud and a target point cloud; semantic segmentation is carried out according to spatial structure characteristics of the point clouds, and plane points and non-plane points of the source point clouds and the target point clouds are obtained respectively; two-way distance search constraints are applied to planar points and non-planar points in the source point cloud and the target point cloud respectively, and a corresponding relation of two-way matching constraints is established; point-to-surface registration and point-to-point registration based on the maximum correlation entropy criterion are carried out on the plane points and the non-plane points after bidirectional constraint, and rotation matrixes and translation vectors of the plane points and the non-plane points are respectively obtained through multiple iterations; weighting the rotation matrix and the translation vector according to an adaptive weight, and applying the weighted rotation matrix and translation vector to the source point cloud to obtain the source point cloud after pose transformation; according to the method, the nearest neighbor corresponding relation from the source point cloud to the target point cloud and from the target point cloud to the source point cloud is considered, and the matching accuracy of the corresponding points is improved.

The invention provides a pipe thread positioning and measuring method and system based on three-dimensional vision, and belongs to the technical field of industrial automation and three-dimensional vision. According to the method, a measuring system composed of a robot measuring system and a three-dimensional visual locating system is built, and hand-eye calibration is completed; the method comprises the following steps: acquiring a three-dimensional point cloud model of the end part of a pipe thread workpiece, and extracting an end surface normal vector, a tangent vector and a circle center coordinate through preprocessing, cylinder fitting, near-end surface point cloud extraction and end surface circle fitting so as to establish a measurement reference coordinate system; and converting the reference coordinate system to a robot base coordinate system based on a hand-eye calibration result and the tail end pose of the robot, generating a robot moving trajectory, guiding the robot to be positioned to a measurement position, and completing preparation before measurement. According to the invention, automatic high-precision extraction of the pipe thread measurement reference is realized, the problems of non-uniform reference, large error and low efficiency in traditional measurement are solved, the method is suitable for various pipe thread workpieces, and a reliable basis is provided for subsequent high-precision measurement.

The invention discloses a three-dimensional scene and object reconstruction method, system and device based on SDF and a Gaussian field, belongs to three-dimensional scene reconstruction in the technical field of computer vision, and aims to solve the technical problem that the three-dimensional scene reconstruction quality is not high due to the fact that geometric precision and rendering quality cannot be achieved at the same time in the three-dimensional reconstruction process in the prior art. The method comprises the following steps: initializing and updating a TSDF voxel volume by using a depth map and an RGB image to obtain a camera external parameter and a scene three-dimensional initial model; sDF prediction is carried out on the scene three-dimensional initial model through an SDF neural network, scene surface points are generated according to the light direction, and a high-precision scene geometric model is obtained; training the SDF neural network; performing grid division on a bounding box of the scene three-dimensional initial model, outputting an SDF predicted value of each grid point by using the trained SDF neural network, and extracting a refined scene grid model; and performing Gaussian rendering by using the high-precision scene geometric model to generate a final rendered image.

The invention relates to a workpiece polishing track generation method and equipment, and the method comprises the steps: collecting a current workpiece surface point cloud, carrying out the plane fitting, obtaining an upper surface equation and an upper surface point cloud, projecting the upper surface point cloud to the upper surface equation, obtaining a two-dimensional projection drawing, obtaining the template workpiece contour of each type of template workpiece, and carrying out the polishing of the workpiece. And calculating the polishing track points of each type of template workpiece, matching the two-dimensional projection drawing with the contour of the template workpiece to obtain the corresponding polishing track points, and back-projecting the polishing track points to the three-dimensional space to obtain the three-dimensional polishing track of the current workpiece. The problem that the workpiece surface polishing track cannot be quickly and effectively recognized in the prior art is solved.

The invention belongs to the technical field of plate shape monitoring and visual analysis, and discloses a stainless steel medium-thickness plate double-face contour measurement method and system, and the method comprises the steps: S1, completing the calibration of a double-line-scan digital camera, and obtaining the upper and lower surface images of a medium-thickness plate; s2, carrying out binocular stereoscopic vision three-dimensional reconstruction to generate a point cloud picture, and converting the point cloud picture into a point cloud picture under the same coordinate system for display; s3, eliminating transverse rotation and deviation according to point cloud center line fitting and plane fitting of the upper surface and the lower surface of the medium-thickness plate; s4, comparing the geometrical relationship between the point clouds of the upper and lower surfaces to quantify local vibration and thickness change, correcting vibration errors, and obtaining the thickness of the medium-thickness plate; and S5, extracting an edge point set from the point cloud image to obtain the overall contour of the medium-thickness plate, and obtaining the position of a shear line based on the width and edge linearity detection of the medium-thickness plate. By the adoption of the technical scheme, the overall contour, overall thickness distribution and accurate shearing position of the medium-thickness plate can be obtained.

The invention relates to the technical field of reservoir storage capacity measurement, in particular to a reservoir storage capacity measurement method and system based on remote sensing and point cloud data, and the method comprises the following steps: extracting a terrain surface point set based on input remote sensing image data and point cloud data, and calculating a three-dimensional coordinate value, a curvature value and a point density value of each point; and comparing and classifying curvature and density data in combination with the change of the reservoir boundary. According to the method, through comprehensive application of remote sensing image data and point cloud data, three-dimensional coordinates, curvature values and point density values are adopted to accurately extract topographic features, scientific division and feature description of sub-regions are achieved, refined three-dimensional grid parameters are generated through fitting and interpolation calculation, and the accuracy of the three-dimensional grid parameters is improved. The terrain modeling precision and the space geometry description capability of a complex area are improved, boundary node coordinates and normal vectors are dynamically adjusted, the grid continuity is optimized, the integrity of a boundary transition area is ensured, and the boundary complex volume is accurately described by adopting unit volume subdivision and boundary correction.

A camera is provided for reconstructing the surface of the object. The camera includes an optical module configured to spatially separate photons reflected from an object in a scene to form at least two focused and distinctly polarized images on a sensor, wherein the sensor is configured to receive the at least two polarized images on pixels the sensor and generate intensity values of the pixels, and a computing module including a processor and a memory having instructions stored thereon. According to the instructions, the processor computes depth values at points of a surface of the object based on a ratio of the intensity values of the pixels with respect to the at least two polarized images and reconstructs the surface of the object from the computed depth values.

The invention relates to a rubber part finished product forming degree detection method, system and equipment and a medium, and belongs to the technical field of rubber part forming degree detection. The molding degree detection method comprises the following steps: receiving surface point cloud data and surface geometric position information of a to-be-detected rubber part; constructing a three-dimensional surface contour model of the rubber part to be detected; mapping the surface pressure distribution data to the three-dimensional surface contour model, and detecting a pressure abnormal distribution area; extracting light scattering characteristic data according to the optical image data; comparing the light scattering characteristic data with the pressure abnormal distribution area to obtain a defect area analysis result; dynamic deformation characteristics of the rubber part to be tested under the dynamic environment simulation test are obtained; marking the three-dimensional surface contour model, and constructing a comprehensive feature model of the rubber part to be detected; and carrying out difference analysis on the comprehensive feature model to obtain a feature difference result of the to-be-detected rubber part, and generating a forming degree scoring report of the to-be-detected rubber part. According to the invention, the molding degree of the rubber part can be accurately detected.

The invention discloses an integrated side slope monitoring system and method based on a laser radar, and relates to the technical field of side slope monitoring, and the method comprises the following steps: setting the laser radar and a high-definition camera for a side slope under the jurisdiction of a current region, carrying out the three-dimensional scanning of the side slope through the laser radar, and obtaining the point cloud data of the surface of the side slope, based on the soil characteristics of the current region, grouping and dividing the jurisdiction slopes, and recording the collected slope data; a slope abnormal site is determined through abnormal point cloud density and elevation values in point cloud data in the slope, coordinates of the abnormal site are obtained through position coordinate conversion, and main side view image acquisition is carried out on the coordinate position based on a high-definition camera; and performing line extraction on the main view and the side view of the abnormal position, and performing parameter analysis on line curvature and distance in the main view and the side view. According to the method, the advantages of laser radar and image analysis are combined, the data processing amount in the slope monitoring process is reduced, and the anomaly recognition accuracy and the monitoring efficiency are improved.

The invention discloses a robot variable stiffness surface path planning method based on three-dimensional perception and application. The method comprises the following steps: processing workpiece surface point cloud data; a surface defect detection model is established, and deformation deviation generated by workpiece surface defects in the point cloud data is compensated; and performing path planning analysis on the point cloud model to generate a self-adaptive tool path. In the process that the robot executes the planned path, passive analysis is conducted through a variable stiffness impedance control law under energy constraint, force control surface treatment is achieved by collecting a force feedback track as a reference and combining a quadratic programming optimization controller, and task execution of the robot planned path is completed. According to the method, a planned path model is combined, an AI auxiliary programming framework is designed, and a user parameterization task and tool are guided through a natural language interface and a knowledge representation engine. According to the method, the adaptive capacity of the robot to the complex variable stiffness surface is improved, the machining consistency and efficiency can be remarkably improved, and the wide application prospect is achieved.

The invention relates to a complex terrain surveying and mapping method, device and equipment and a medium. According to the complex terrain surveying and mapping method, device and equipment and the medium, laser beams with alternating polarization states are synchronously emitted through a dual-wavelength laser radar, an original point cloud with a space-time mark is captured, and earth surface points and noise points are analyzed based on time difference and polarization degradation characteristics; constructing a noise cluster function according to the noise points to identify vegetation artifacts; performing earth surface confidence evaluation on the earth surface points, filtering noise by using an elevation layering strategy, and positioning a fracture area; and finally, based on the denoised cloud layer, the fracture area and the earth surface point confidence coefficient, reconstructing a terrain curved surface, and generating a continuous terrain model. Therefore, precise reconstruction of the terrain sudden change area under vegetation coverage is realized, and the dual technical effects of resisting complex scattering interference and keeping the integrity of the terrain structure are achieved.

The invention relates to the technical field of exhibition lamps, in particular to an exhibition lamp and an exhibition method, and the method comprises the following steps: inputting preset exhibit database information, extracting exhibit numbers, material types, dominant tone ranges and size information, carrying out point-by-point scanning on exhibit surfaces, collecting reflection spectrum data, converting the reflection spectrum data into CIELAB values, and integrating the extracted information and the collected data. And establishing an exhibit basic information set. The comprehensive exhibit basic information set is established by combining the preset database information and the real-time scanning and acquisition of the surface reflection spectrum or the CIELAB value of the exhibit, so that the subsequent illumination design can be carried out based on the unique optical characteristics of each exhibit, the accuracy and pertinence of personalized illumination are improved, and the material details and color levels can be deeply revealed. Secondly, processing the optical data of the exhibit by using a preset weight set associated with a specific display target, and generating an ideal display illumination parameter including spectral distribution and a brightness value;

The invention belongs to the technical field of point cloud data reconstruction, and particularly relates to a robust three-dimensional reconstruction method based on weighted local curved surface approximation. The method comprises the following steps: firstly, preprocessing input three-dimensional point cloud data, constructing a neighborhood relationship, and fitting a local curved surface in a local neighborhood of each point through a least square method; secondly, calculating the influence weight based on the neighborhood information of the point, and calculating the position of a final curved surface point by adopting a weighted average method so as to optimize the geometric accuracy of a local curved surface; then, screening and removing abnormal points by using an abnormal weight elimination strategy based on a mean square error, reducing noise interference, and improving stability and robustness of curved surface reconstruction; and finally, through weighted average, the precision of overall curved surface fitting is improved, so that the reconstructed curved surface can more accurately fit the original point cloud data, and the reconstruction precision of the point cloud data is effectively improved.

The invention provides a 6D attitude rapid estimation method and device for a target object, and relates to the field of electrical digital data processing in the technical field of machine vision and perception, and the method comprises the steps: obtaining an RGB image and a depth image of the target object, inputting the RGB image into a segmentation model, and obtaining a binary mask of the target object; acquiring a camera internal reference matrix, and constructing a curved surface point cloud matrix of the target object according to the depth image, the binary mask and the camera internal reference matrix; constructing a cubic package of the target object, and constructing a fitting penalty function according to the curved surface point cloud matrix and the cubic package; according to the curved surface point cloud matrix, the cubic package and the fitting penalty function, gradient vectors of the center point and the Euler angle of the target object are obtained through calculation; and performing iterative solution on the gradient vectors of the central point and the Euler angle through a gradient descent algorithm to obtain optimal solutions of the central point and the Euler angle, and taking the optimal solutions of the central point and the Euler angle as the 6D attitude of the target object.

The invention relates to the technical field of defect detection, in particular to a method and a system for rapidly detecting defects after defoaming of a flexible screen. The method comprises the following steps: acquiring corresponding flexible screen defoaming images under different spectrum and angle irradiation, and performing image complementary registration fusion and structure phase morphology reconstruction to generate flexible screen surface three-dimensional morphology data; marking points are arranged on the edge of the flexible screen, and dynamic deformation compensation and curvature analysis of all points on the surface are carried out, so that principal curvatures and Gaussian curvatures corresponding to all the points on the surface of the flexible screen after bubble removal are obtained; performing abnormal curvature area identification and defect geometric feature analysis on the corresponding flexible screen after bubble removal to obtain defect geometric features of the flexible screen after bubble removal; and constructing a corresponding deep learning model and carrying out defect classification detection so as to detect and obtain corresponding bubble residue, indentation, scratch and degumming type defect results after the flexible screen is defoamed. According to the invention, the rapid and accurate detection process of the defects after defoaming of the flexible screen can be realized.

The invention provides a part virtual assembly method based on a three-dimensional point cloud. Comprising the following steps: 1, acquiring surface point cloud data of a measured piece; 2, registering the point cloud to a design model of the part; 3, selecting a section according to the position of the design model, extracting contour points of the point cloud on the section, and calculating part manufacturing deviation according to contour information; 4, planning an optimal assembly sequence with the minimum overall deviation according to the manufacturing deviation of each part; and 5, for the part point cloud under the optimal assembly sequence, extracting a section contour vertical to an assembly surface, calculating the distance between part boundary points, and outputting the assembly clearance of the part. According to the method, the overall and local features of the part surface point cloud are mainly utilized to realize part virtual assembly and deviation detection, the automation degree is high, the application range is wide, good stability and repeatability are achieved, the part assembly result can be predicted, assembly can be guided, and the assembly quality of actual parts is effectively improved.

The invention discloses a heliostat back surface type detection method and system, belongs to the technical field of heliostat surface type detection, and can solve the problems that an existing detection mode is low in detection efficiency, unstable in detection result and high in detection cost. The method comprises the following steps: S1, acquiring position information of a plurality of points on the back surface of a heliostat to be measured to form back surface point cloud data; the position information comprises an acquisition distance, a pitch angle and an azimuth angle; s2, performing coordinate transformation and rotation translation on the back point cloud data in sequence to obtain rotated point cloud data; the normal vector of the rotated point cloud data is parallel to the coordinate axis, and the central point of the rotated point cloud data coincides with the original point of the coordinate axis; and S3, fitting the back surface type of the heliostat to be measured according to the rotated point cloud data to obtain a back fitting surface type, and determining a back surface type detection result of the heliostat to be measured according to the back fitting surface type. The method is used for detecting the back surface type of the heliostat.

The invention relates to the technical field of three-dimensional modeling, and discloses a texture mapping method and device based on geometric morphology, and the method comprises the steps: carrying out the data analysis of the geometric morphology of an initial three-dimensional model, and obtaining a plurality of geometric features of each surface point; dividing texture grades based on the plurality of geometric features of each surface point to obtain a plurality of texture grade areas; generating a target texture based on the plurality of geometric features of each surface point; based on the texture grade area to which each surface point belongs, mapping the target texture to the initial three-dimensional model to obtain a hybrid three-dimensional model; and based on the plurality of geometric features of each surface point, adjusting the texture of the hybrid three-dimensional model to obtain a target three-dimensional model. Texture mapping is carried out based on the geometric features of the model surface, compared with manual mapping, it is ensured that the texture of each area is highly matched with the geometric features, texture distortion and distortion are avoided, the detail expressive force and the visual effect are improved, and meanwhile the automation degree and the working efficiency of the texture mapping process are improved.

A method and system for eliminating 2D features from planar surfaces in 2D images. Three digital images are taken, from a single camera at a fixed position, of a subject such as a pallet of boxes. One image (IA) is taken with ambient lighting, one image (I1) has ambient lighting plus a first added light source, and one image (I2) has ambient lighting plus a second added light source. An output image Q is then computed by Q=(I1−IA) / (I2−IA). Subtracting the ambient image removes ambient diffuse and specular reflections. Division eliminates all variations in the output image caused by color. The only variations that remain are those due to the angle between each surface point's normal direction and the direction from the light to that point. The output image Q, devoid of all colors and 2D features, is well suited for computing a robot grasp of an object in the image.

The invention discloses a method for generating a workpiece model based on point cloud data, and aims to automatically generate a high-precision workpiece model through reverse modeling. The method comprises the following steps: dynamically adjusting a data acquisition amount through a line structure light sensor, and obtaining workpiece surface point cloud data; preprocessing the point cloud, including three-dimensional effective area cutting, uniform downsampling and radius filtering to remove interference data and reduce data volume; carrying out clustering segmentation on the preprocessed data by utilizing a point cloud region growing method, separating a bottom surface part from a non-bottom surface part, extracting contour points of each plane region, calculating normal lines, generating vertexes by fitting contour edges and solving intersection points, and forcibly closing unclosed contours to construct complete geometric features; and finally, generating a workpiece model by combining the point, line and surface information packaged by the open source library. According to the method, the low-error and high-precision model can be directly generated for the model-free non-standard workpiece, limitation of manual modeling or model presetting is avoided, and programming time consumption is remarkably reduced.

The invention discloses a reinforcing steel bar surface binding point identification and positioning method based on point cloud and pixel mapping. The method comprises the following steps: obtaining original three-dimensional point cloud data to carry out outlier noise point elimination; performing plane fitting on the denoised point clouds, identifying template surface point clouds and removing the template surface point clouds; mapping the spatial physical coordinate attribute of the steel bar surface point cloud into a three-channel coding image; separating the three-channel coded image into three single-channel images: a length image, a width image and a depth image; processing the depth image to extract an initial reinforcing steel bar straight line set; screening out a parallel steel bar straight line set from the initial steel bar straight line set according to a set reference direction and a parallel angle tolerance threshold value; and calculating straight line intersection points in the parallel steel bar straight line set to obtain candidate binding point pixel coordinates, determining representative binding point pixel positions through clustering, querying coordinate information of the three single-channel images, and outputting three-dimensional space coordinates of the steel bar binding points. According to the invention, robust and accurate steel bar binding point identification and positioning are realized.

The invention discloses a robot bolt tightening method and system based on 3D visual guidance, and relates to the technical field of intelligent manufacturing, and the method comprises the following steps: S1, constructing a minimum photographing frequency model according to known camera and frame data, and solving an optimal photographing path; s2, acquiring an image and point cloud information of the frame; s3, inputting the image into an intelligent bolt detection model to obtain size and position data of the bolt; s4, bolt surface point cloud information is cut out according to the data of the bolt, and the posture and the specification size of the bolt are calculated through fitting of the bolt surface point cloud information; s5, calculating the transformation relation of the pose of the tightening gun under the image coordinate system and the frame coordinate system; and S6, the robot is guided to conduct tightening operation according to the transformation relation and the posture, specification and size of the bolt.

The application relates to the technical field of image processing, in particular to a camera-based progressive feature distribution sampling 6D pose estimation method and system, which comprises the following steps: obtaining dense pixel points by using a multilayer progressive feature sampling network of Unet++; analyzing three-dimensional model surface point features by using graph convolution to form a corresponding distribution relationship between image pixel points and surface points; calculating the probability distribution of the corresponding key of the image pixel coordinate point set features by querying the model to obtain the mask probability of the corresponding three-dimensional surface of the image pixel coordinates; calculating the best probability distribution value; scoring and voting each generated hypothesis; refining the obtained best voting score attitude hypothesis according to the surface distribution to obtain the target final attitude estimation pose. The application solves the problems that the stability and accuracy of pose estimation of a target object are not high when only RGB data is used, and the accuracy is not high when irregular features exist in three-dimensional model surface feature sampling.

The invention provides a dimple quality online detection method and system based on 3D scanning and virtual assembly, and the method comprises the steps: S1, obtaining dimple hole point cloud data, segmenting the point cloud data, and obtaining conical surface point cloud data; s2, performing conical surface fitting on the conical surface point cloud data through a fitting algorithm to obtain a local point set and a cone parameter; and S3, according to the local point set and the cone parameters, pit diameter contour plane fitting is carried out, the pit diameter contour plane and the nail head are assembled in a simulated mode, and a dimple depth error value is obtained. On the basis of 3D scanning and virtual assembly, pit hole surface point cloud analysis, nail rod interference analysis and compensation functions are achieved, pit depth measurement is achieved, and compared with a monocular or binocular vision scheme, the measurement precision is higher.

The invention discloses a virtual pre-assembly method for a bridge closure section. The method comprises the following steps: acquiring three-dimensional point cloud data of the closure section and a closure gap; carrying out closure section connection section area data extraction and closure gap connection section area data extraction based on a bisection method, and obtaining connection end face area data; extracting connection end face point cloud data from the connection end face region; extracting outer boundaries of the connecting end faces and fitting feature angular points to obtain all key angular points on each connecting end face, and finally obtaining a feature angular point data set of the closure section and the closure gap; and feature point three-dimensional model construction and virtual pre-assembly: establishing a feature point three-dimensional model based on the feature angular points of the closure section and the closure gap, simulating the final state of hoisting butt joint by using a virtual pre-assembly method, and calculating the matching and cutting amount of the closure section. According to the method, the cutting amount of each feature point position can be automatically calculated, and the precision and efficiency of closure construction are greatly improved.

The invention provides a casting joint line polishing method based on vision, and belongs to the crossing field of mechanical manufacturing automation and intelligent manufacturing, and the casting joint line polishing method comprises the following steps: obtaining template surface point cloud of a standard casting, carrying out reverse modeling on the template surface point cloud to obtain first template point cloud, and obtaining first measurement point cloud of a casting to be polished; performing rough matching and fine matching on the first template point cloud in sequence, and converting the original joint line model into a new joint line model; points, closest to track points of the template track, in the new joint line model are obtained, for each closest point, the point, having the largest vertical distance from the plane where the new joint line model is located, in the neighborhood of the closest point is obtained, and all the points having the largest distances form a path planning point cloud; and traversing all planning points in the path planning point cloud according to the machining direction, selecting a machining mode according to the machining allowance corresponding to each planning point, and generating a final machining track. The grinding precision, the grinding quality and the grinding efficiency of the joint line can be improved, and the grinding device is also applicable to complex curved surfaces.

The invention discloses a method and system for processing a circular weld joint based on point cloud data, and belongs to the technical field of machine identification of weld joints, and the method comprises the following steps: collecting the point cloud data of the surface of a workpiece through a visual sensor; preprocessing the collected point cloud data; calculating a normal vector of each point in the point cloud data; segmenting the workpiece surface point cloud and the reflective interference points based on a normal vector, and filtering the interference points deviating from a preset range in the normal direction to obtain a target point cloud and a reference point cloud; projecting the target point cloud to a reference plane obtained by fitting the reference point cloud, generating a target projection point cloud, detecting boundary points of the target projection point cloud, screening candidate weld joints based on distance distribution from the boundary points to the center of mass, and extracting weld information. According to the method, the workpiece made of the material with high light reflection is treated, the finally obtained result can still keep good precision, and the quality of the workpiece is improved.

The invention provides a cement conveyor volume measurement method based on deep learning point cloud segmentation. The method comprises the following steps: obtaining 3D point cloud data by using a laser line scanning 3D camera, and determining RGB values of pixels corresponding to the 3D point cloud data to obtain 3D point cloud with color features; inputting the 3D point cloud with the color features into an improved Point-Net + + neural network for 3D point cloud segmentation, and obtaining 3D point cloud data only containing the cement surface in the conveyor after segmentation; constructing a triangulation network based on the cement irregular surface in the segmented 3D point cloud data by adopting a triangulation method, and performing volume calculation according to the constructed triangulation network; wherein the improved Point-Net + + neural network is constructed on the basis of a framework of an original Point-Net + + point cloud segmentation network, and comprises a core point convolution, a context sensing module and a Focal Loss loss function. Therefore, the performance of a point cloud segmentation algorithm is improved, and even under complex conditions, the surface point cloud data of the cement material in the conveyor can still be accurately segmented for volume measurement.

The invention provides an ancient building damaged component AR matching repair method and system, and the method comprises the steps: obtaining the surface point cloud data of a damaged component through an augmented reality technology, generating a high-precision three-dimensional point cloud model through a laser scanning and multi-view stereoscopic vision fusion method, and obtaining the digital expression of the complex geometric shape of the component; for the point cloud model, adopting voxel filtering and a normal estimation algorithm to carry out denoising and surface smoothing processing, and extracting component edge features and curved surface geometric features to obtain a smooth three-dimensional geometric model; and carrying out space mapping on the optimized mortise and tenon joint structure model and an actual component position through an augmented reality technology, generating a virtual joint guide line and stress distribution visual image, guiding a component installation process, and obtaining a high-precision component installation result.