485 results about "Point projection" patented technology

The invention relates to the technical field of spherical display screens, and discloses a visual enhancement method and system for an LED creative spherical display screen, and the method comprises the steps: inputting an original display image of the LED spherical display screen into a multi-scale Transform layered CNN network for feature extraction, and obtaining a multi-dimensional feature image set; feature mapping is carried out, pixel points are projected into a spherical coordinate system, display deviation is calculated, and a comprehensive evaluation matrix is generated; calculating a space compensation coefficient through an N-order polynomial function to obtain a space-time compensation matrix; performing gradient calculation on the space-time compensation matrix to obtain a target vector field; inputting the target vector field into a U-Net structure generator for adversarial training to obtain compensation optimization parameters; according to the method, the real-time dynamic optimization of the display image of the spherical display screen is realized, and the display quality under different environment illumination conditions is ensured.

The invention relates to the technical field of computer vision, in particular to a point projection type three-dimensional reconstruction and segmentation method and system based on semi-Gaussian pruning. The method comprises the following steps: respectively obtaining an SFM point cloud and a consistency label mask of a cross-view label based on an obtained multi-view image; initializing the obtained SFM point cloud into an identity semi-Gaussian point cloud, and performing rendering optimization by using a differentiable renderer; densifying the initial sparse point cloud by using a localized semi-Gaussian point management method, and identifying a local error region for resetting and repairing; using the obtained consistency label mask to supervise Gaussian identity feature learning by using cross entropy loss, and using unsupervised 3D regularization loss to force spatially adjacent gauss to maintain identity consistency; according to the method, the identity coding semi-Gaussian kernel method is adopted, the inherent representation fuzziness of a single opacity formula is eliminated, and the positive influence on the identity coding precision is generated.

The invention provides a laser and inertia fusion point cloud map construction method and equipment. Mobile laser point cloud data and inertial measurement data which are subjected to hardware time synchronization are obtained through a laser radar and an inertial measurement unit sensor; performing motion distortion correction on the laser point cloud in one frame according to inertial measurement data; projecting the laser observation point after distortion correction to a map coordinate system for feature association according to the position and attitude obtained by inertial measurement data integration, and calculating the position and attitude of a carrier by considering the laser positioning degradation condition; dividing the point cloud sub-graphs according to the time span, constructing the relative pose constraint between the sub-graphs, and optimizing the poses of the sub-graphs to obtain the high-precision point cloud map. The three-dimensional sparse sampling of the environment is directly obtained through the laser radar, the discrete inertia pose is corrected by using the continuous time model to obtain more accurate point cloud distortion correction, the carrier pose is updated by considering laser degradation, and the robustness and precision of point cloud mapping in the moving process are improved.

The invention belongs to the field of three-dimensional surveying and mapping, and particularly relates to a multi-precision three-dimensional surveying and mapping data fusion method based on dynamic modeling, which comprises the following steps of: assigning low-level semantic tags to LiDAR point cloud geometric features, and assigning high-level semantic tags to optical image texture features; defining a semantic tree structure, and establishing a cross-scale semantic association initial anchor point; constructing a cross-modal graph structure, projecting LiDAR point cloud nodes to optical image neighborhood superpixel nodes, and connecting and aggregating multi-scale semantic features; inputting geometric and image residual texture features by using a U-Net generator and outputting virtual textures; geometry and texture feature fusion and semantic and geometry collaborative optimization are realized through gating weighted feature fusion. According to the method, the problem of inconsistent multi-precision data semantic expression is systematically solved, semantic consistency is improved, texture deficiency is filled, and dynamic balance between geometric fidelity and semantic enrichment is realized.

Methods and systems for performing a two-dimensional semantic segmentation and assigning a semantic data to three-dimensional (3D) points of a 3D point cloud are disclosed. The method includes projecting a set of 3D points onto a projection plane to define a set of 2D points. For each of one or more target 2D points, the method includes defining a neighborhood, determining a first and a second groups of 2D points in the neighborhood, determining a weight for each 2D point of the first and second groups, aggregating feature data of the first and second groups of 2D points to form first and second aggregated features respectively, determining a semantic confidence score based on semantic data of the 2D points included in the neighborhood and combining, based on the semantic confidence score, feature data of the target 2D point with the first aggregated feature data and the second aggregated feature data.

The invention discloses a product model detection method and system based on welding spots. The method comprises the steps that a welding spot identification mark is obtained, a welding spot file matched with the welding spot identification mark is identified from a product model, and the welding spot file is a data file comprising a welding spot model; identifying a part model having an interference relationship with the welding spot model based on the position relationship of the welding spot file in the product model; taking an intersection surface obtained by intersecting the part models as a welding surface of the part models, projecting the welding spot model onto the welding surface to obtain a welding spot projection position, and detecting a shortest spot edge distance from the welding spot projection position to a welding surface boundary; based on the shortest point edge distance, the width of the welding face is determined through an auxiliary face intersection method; and detecting whether the product model is qualified or not based on the shortest point edge distance and / or the width of the welding surface. The technical problems that in the prior art, welding spot detection mainly depends on manual estimation, so that subjectivity is high, efficiency is low, and missing detection is prone to occurring are solved.

The invention relates to the technical field of multi-sensor fusion, in particular to an automatic calibration method and calibration device for laser radar-camera external parameters, and the method comprises the steps: synchronously collecting the point cloud data and image data of a road scene based on a vehicle-mounted laser radar and a camera; performing semantic segmentation on the image data to generate a semantic mask set of each image; based on the point cloud data, geometric attributes of a road scene are extracted from the vectorized map, a laser radar coordinate system of the vehicle-mounted laser radar and points in the vectorized map are projected into the image in combination with the semantic mask set, and a target consistency function is constructed; and adjusting the external parameter matrix based on the target consistency function until the target consistency function reaches a maximum value, and completing automatic calibration. Therefore, the problems that the calibration precision is insufficient and the high-precision perception requirement of automatic driving is difficult to meet due to the fact that related technologies depend on a large amount of labeled data or a specific environment, the generalization ability is poor, and a high-precision map and road prior information cannot be combined are solved.

The invention discloses a live pig body size and weight measuring method based on RGB-D. The method comprises the following steps: S1, obtaining a left vision rotation translation matrix and a right vision rotation translation matrix; s2, acquiring a three-view RGB image and a depth image of a single pig; s3, acquiring a body scale measurement key point; s4, acquiring a left viewpoint cloud, a right viewpoint cloud and an overlook point cloud, and performing point cloud registration to obtain a complete pig point cloud C1; projecting the body size measurement key points to the complete pig point cloud C1; s5, obtaining a final complete pig point cloud C2; s6, calculating the body length, the body width, the body height and the abdominal girth of the pig by using the final complete pig point cloud C2, and forming body size data; and S7, substituting the body size data of the pig into the multiple linear regression prediction model to predict the weight. According to the method for measuring the body size and the weight of the live pig, the accuracy and the stability of extracting the position of the body size measuring point are improved, and the accuracy of the body size and the weight measuring result of the pig is improved.

The embodiment of the invention provides an indoor vehicle positioning method and device and a storage medium, and the method comprises the steps: obtaining a scene image corresponding to a target vehicle at a current moment when it is detected that the target vehicle drives into a positioning region of a target scene; edge point detection is carried out on the scene image to determine a target image area corresponding to a target calibration lattice, and the target calibration lattice is a calibration lattice assembled on the target vehicle. And extracting pixel coordinates corresponding to each pixel point in the target image area from the scene image. And according to a pre-calibrated internal reference matrix of the camera, the pixel coordinate corresponding to each pixel and the height from the target calibration grid to the ground, determining the camera coordinate of each pixel point projected to the camera coordinate system. And according to the pre-calibrated external parameter matrix of the camera, converting the camera coordinate corresponding to each pixel point into the world coordinate in the world coordinate system to determine the position information of the target vehicle in the positioning area, so that the vehicle position can be accurately positioned in the indoor scene.

The invention discloses a multi-sensor fusion AGV dynamic target elimination method and device based on target detection and semantic segmentation technology, and the method comprises the steps: carrying out the joint calibration of a monocular camera and a two-dimensional laser radar, and obtaining an internal reference matrix of the camera and a conversion matrix TLC from a laser radar coordinate system to a monocular camera coordinate system; enabling the laser SLAM to enter a laser tracking thread, and carrying out the dynamic threshold detection of the minimum adjacent point pair distance of the front and rear groups of dynamic point clouds; performing timestamp matching on the image acquired by the monocular camera and the laser point cloud, and performing interpolation calculation to obtain a visual key frame; projecting a laser dynamic point to an image coordinate system of a tc moment visual key frame FrameL, and inputting the laser radar dynamic point into a target detection thread; performing target detection on the visual key frame at the tc moment, and extracting feature points outside a target detection frame for updating the subgraph; semantic segmentation, feature point filtering and static feature point screening are carried out in a target detection frame region, and in a map construction stage, a grid probability is updated.

An apparatus for generation of a 3D representation of a scene includes a memory for storing a plurality of images depicting a scene; and processing circuitry in communication with the memory. The processing circuitry is configured to generate a depth map for the plurality of input images depicting a scene and unproject the depth map to a three-dimensional (3D) point cloud. The processing circuitry is also configured to project a plurality of 3D points from the 3D point cloud to a plurality of two dimensional (2D) planes and extract a plurality of triplane features from the plurality of 2D planes. The processing circuitry is further configured to generate a 3D representation of the scene based on combining the plurality of triplane features and a plurality of image features extracted from the plurality of input images.

A panoramic image formed by projecting a target place to a virtual environment can be generated based on a plurality of environment images obtained by observing the target place from different angles of view. A machine can automatically recognize and intelligently extract layout information of the target place based on the panoramic image, and build, by using the layout information, a target virtual environment for simulating the target place.

A system including a drone having a projector to project an image from a projection origin. The drone also has a navigation unit to determine location information for the drone. A processor coupled to the drone includes a memory. Execution of programming by the processor configures the system to obtain a projection surface architecture for a projection surface. The projection surface architecture includes reference points that correspond to physical locations on the projection surface. Each reference point is associated with relationship data with respect to an architecture origin. The system also receives location information for the drone, adapts the relationship data responsive to change in the location information, adjusts the image using the adapted relationship data, and projects the adjusted image onto the projection surface.

The invention discloses an asymmetric lens distortion compensation method for linear reconstruction guided by feature point projection transformation. The method comprises the following steps: traversing and calibrating a coordinate matrix of world coordinates of image feature points after projection transformation through minimum reference grids, calculating a projection transformation comprehensive error of all the minimum reference grids, and obtaining the projection transformation comprehensive error through weighted calculation of straight line constraint, cross ratio constraint and parallel line constraint; and carrying out secondary screening to obtain a reference grid region with the minimum projection transformation comprehensive error. Thirdly, optimizing the optimal reference grid by taking the minimum comprehensive error as a target; and finally, solving the optimized feature point coordinates and the corresponding world coordinates to obtain a homography transformation matrix, performing linear reconstruction on the coordinates of projection transformation of the feature points of the whole calibration image, and performing parameter optimization of the lens asymmetric distortion model with the coordinates before reconstruction, thereby obtaining a high-precision lens asymmetric distortion model. And the asymmetric distortion of the lens is compensated and corrected.

The invention discloses a virtual viewpoint video sequence generation method and device, equipment and a medium, and relates to the technical field of three-dimensional display, and the method comprises the steps: obtaining a single viewpoint video sequence, dividing adjacent frame images in the single viewpoint video sequence, and obtaining a current key frame image and a current associated frame image; determining a current key frame depth map and a current associated frame depth map from the current key frame image and the current associated frame image respectively, performing optimization processing on the current key frame depth map and the current associated frame depth map, and determining the current associated frame depth map based on the optimized current key frame depth map and the optimized current associated frame depth map. And performing three-dimensional coordinate system pixel point projection and virtual viewpoint projection on the current key frame image and the current associated frame image to obtain a key frame virtual viewpoint image and an associated frame virtual viewpoint image, and generating a virtual viewpoint video sequence. The quality of the generated virtual viewpoint video sequence can be improved, and high-efficiency, high-quality and low-cost conversion from a single-viewpoint video to a multi-viewpoint video is realized.

The invention discloses a weld ROI segmentation method based on active and passive vision fusion. The method comprises the following steps: synchronously acquiring a two-dimensional image and a three-dimensional point cloud of the surface of a welded workpiece; inputting the trained deep learning network, and outputting a weld joint candidate region and a pixel-level mask graph thereof; establishing a cross-modal mapping relation through geometric calibration, and projecting the three-dimensional point cloud to a two-dimensional image to complete registration and fusion; cutting from the three-dimensional point cloud by using a cross-modal mapping relation to obtain a candidate point cloud region; generating a gray matrix for ray odd-even discrimination according to the pixel-level mask pattern; and each point of the candidate point cloud is projected to a pixel coordinate point by point, ray odd-even discrimination is carried out on the projection points according to the gray matrix, the points conforming to an odd-even rule are determined as welding line points and are aggregated into a welding line ROI point cloud, and the welding line ROI point cloud is output as a segmentation result. According to the method, the adaptive capacity to the unstructured welding seam is effectively improved, the point cloud processing complexity is reduced, and good real-time performance and robustness are achieved.

The invention discloses a pixel-level accurate texture generation method based on a multi-view artistic work. The method comprises the following steps: inputting an image file; performing multi-view projection on the 3d model for a plurality of views corresponding to the plane image to construct a texture space; aiming at coexisting points on the surface of the 3d model in the multiple visual angles, calculating a dot product of each visual angle direction and a normal of the surface of the 3d model so as to construct an initial texture map corresponding to the 3d model; projecting all contour points of the 3d model into a texture space to obtain corresponding texture information, and comparing the texture information with contour points contained in the initial texture map to construct a mask area; generating a shielding texture map corresponding to the mask area through a diffusion model; and carrying out rendering processing on the 3d model based on the initial texture map and the sheltered texture map so as to output a high-quality model with texture information. The invention further provides a pixel-level accurate texture generation device. According to the method provided by the invention, the high-quality texture can be generated for the grid, and meanwhile, the pixel-level consistency with the reference art is ensured.

The invention discloses a coating salient point arrangement which comprises an isolating membrane, a plurality of coating salient points are arranged on the coating surface of the isolating membrane, and the coating salient points are in irregular salient point shapes or linear shapes. The coating salient points are irregular salient point-shaped or linear, round points can be changed into polygons such as triangles, quadrangles, polygons and plum blossom-shaped polygons, the peripheral area of the salient points is increased under the same salient point projection area, the contact area of electrolyte is increased, the compactness is reduced, and the polygonal structure is more stable under the same salient point projection area. And the risk of being scratched off during winding can be reduced. The linear shape can be made into dotted lines, solid lines, parallel lines, crossed lines or circular rings, and parts of PVDF on the front and back surfaces of the diaphragm are overlapped, so that the problem of insufficient electrolyte storage solution space after lamination of the positive and negative electrodes and the diaphragm can be solved after lamination. For a point-shaped plate roller, the probability of single-point abrasion or falling is large, but a linear plate roller can well avoid the problem.

An electronic device for performing calibration is provided. The electronic device includes a communication interface, memory storing one or more computer programs, and one or more processors communicatively coupled to the communication interface and the memory, wherein the one or more computer programs include computer-executable instructions that, when executed by the one or more processors individually or collectively, cause the electronic device to obtain, via the communication interface, a first image of a user captured by a first camera, and a second image of the user captured by a second camera, extract first joint feature points, which are two-dimensional (2D) position coordinate values of joints of the user, from the first image, and second joint feature points, which are 2D position coordinate values of the joints, from the second image, obtain three-dimensional (3D) joint feature points of the joints by lifting the extracted first joint feature points to 3D position coordinate values, obtain a projection relationship for projecting the 3D joint feature points onto the 2D position coordinate values of the second joint feature points, and perform camera calibration by predicting a positional relationship between the first camera and the second camera based on the obtained projection relationship.

The invention discloses a dual-path non-cooperative spacecraft pose estimation method based on uncertainty perception, and belongs to the technical field of spacecraft pose estimation. According to the method, a dual-path prediction framework based on a shared backbone network is constructed and comprises a geometric reasoning path and a global context sensing path. In the training stage, prediction results of the two paths are aligned through a geometric consistency loss function, and joint optimization is carried out by combining multi-task losses such as key point regression, classification, rotation and translation regression and the like. In the inference stage, the pose estimation result is adaptively fused according to the predicted variance output by each path, when the variance is low, the output of the global context sensing path is directly adopted, otherwise, the key point projections of the two paths are combined, and the final pose is recovered. The method has higher estimation precision, robustness and interpretability in complex space environments such as shielding, illumination variation and large-scale variation, and provides reliable visual navigation support for on-orbit autonomous tasks.

Non-invasive method for monitoring wear on a turbomachine blade. The method comprises the steps of: a) acquiring, using an optical sensor (54), a three-dimensional point cloud of a reference surface (56), b) defining a projection direction (Z) from the point cloud of the reference surface (56), c) acquiring a three-dimensional point cloud of a control portion (32) of the blade (10), d) calculating a three-dimensional skin area of ​​the control portion (32) and scanning it to determine a lowest point (72), e) projecting onto said projection direction (Z) a first point (70) belonging to the control portion (32), and the point (72), f) determining a height (H') between the first point (70) and the second point (72), g) applying a comparison criterion of said height (H') to a predetermined value (C, C') quantifying an acceptable level of wear of said control portion (32).Figure to be published with the abbreviation: [Fig. 6].

The invention innovatively provides a laser cladding processing device control system which comprises the following steps: a target extraction step: collecting to-be-processed workpiece image data acquired by a visual sensor to obtain a total data set, and performing similarity analysis on the total data set in a rough classification strategy to obtain a target detection data set; an image analysis step: training the target detection data set to obtain a plurality of to-be-processed workpiece target detection models, obtaining a dimension reduction plane through point projection according to the to-be-processed workpiece target detection models, and constructing the dimension reduction plane into a plurality of grid matrixes; a defect extraction step: according to the grid matrix, a change grid is obtained through change ratio analysis in a fine analysis strategy, and the change grid reflects a change point of the workpiece to be machined; and a defect judgment step: obtaining the change grid and judging whether the workpiece to be machined has defects or not through a state analysis strategy.

The invention discloses a building detail extraction method and device based on oblique photography and composite modal features, and the method comprises the steps: calculating a tilt parameter of a midpoint of a point cloud model based on the point cloud model of oblique photography data of a target building, and converting the tilt parameter into a color parameter; projecting points on the point cloud model to a target building view area of a two-dimensional plane to obtain a plurality of superpixel blocks, inputting features of the superpixel blocks into a variational auto-encoder, and performing optimization to obtain encoder optimization weight data; and inputting the features of the super-pixel blocks into an encoder to obtain corresponding optimized latent variables, clustering the super-pixel blocks based on the optimized latent variables, and mapping a clustering result to the three-dimensional model of the target building to obtain details of the target building. By adopting the technical scheme, the three-dimensional features and the color features are compounded, and the structure details and the distribution characteristics of the complex building are captured by utilizing the relevance between the features; an encoder of a pre-trained variational auto-encoder is used for aggregation, so that the accuracy of an aggregation result is improved.

The invention relates to the technical field of robots, and discloses an obstacle sensing method and a storage medium, and the method comprises the steps: obtaining image data of an environment in front of a mobile robot and three-dimensional point cloud data generated through laser radar scanning; pixel-level semantic segmentation is carried out on the image data, and a semantic segmentation map with each pixel having a semantic tag is generated; projecting each three-dimensional point in the three-dimensional point cloud data to a semantic segmentation map based on a calibration relationship between a camera and a laser radar, and generating a semantic point cloud with a semantic tag; for a detection blind area of the laser radar in front of the robot, generating a virtual three-dimensional point cloud in the detection blind area by using obstacle semantic information corresponding to an image area of the detection blind area in the semantic segmentation map; and determining the obstacle point cloud based on the three-dimensional point cloud of which the semantic label is the obstacle category in the semantic point cloud and the virtual three-dimensional point cloud. According to the invention, the obstacle detection precision in a complex environment can be improved.

The invention relates to the technical field of sports auxiliary equipment, and discloses a table tennis auxiliary training method, a storage medium, a device and terminal equipment, and the method comprises the steps: obtaining table position information through a depth camera, and constructing a three-dimensional coordinate system according to the calibration information of the depth camera and the table position information; acquiring a first table tennis motion image through a depth camera; obtaining table tennis coordinate values based on the three-dimensional coordinate system and the first table tennis motion image; according to the table tennis coordinate values, obtaining table tennis motion trails and drop points; the motion track and the drop point of the table tennis ball are projected to the table tennis table through the projector. The table tennis auxiliary training system integrates intelligence, datamation and informatization, and can capture table tennis motion trails and motion information of athletes at the same time. The motion trail of the table tennis ball can be projected to the table tennis table, and meanwhile the motions of athletes can be scored and guided through projection and an AI model. And the efficiency and quality of table tennis training are effectively improved.

The invention discloses an external parameter calibration method of a vehicle-mounted around-view camera and a storage medium, and the method comprises the steps: projecting an image point on an imaging plane of a camera to the ground through employing a reverse projection process, and calculating a reverse projection error between two adjacent cameras through employing a reverse projection point obtained in the reverse projection process; an objective function is constructed based on the inverse projection error, and finally, the objective function is utilized to adjust the external parameters of the vehicle-mounted around-view camera to complete calibration. The world coordinate of each image point does not need to be calculated in the constructed target function, so that the calculation amount in the calibration process is greatly reduced, and the camera calibration efficiency is improved; and when the inverse projection process is utilized, because the image points are uniformly projected to the ground, the influence of the camera height arrangement can be avoided, the problem that the calibration effect is poor due to the fact that the calculation of the re-projection error in the classical BA algorithm is influenced by the camera arrangement height is solved, and the calibration effect of the vehicle-mounted around-view camera can be effectively improved.

The invention provides a power equipment three-dimensional modeling and route planning method based on laser point cloud and image fusion. The method comprises the steps that an original point cloud sequence and an original image sequence collected by an unmanned aerial vehicle and pose information of the unmanned aerial vehicle are acquired; preprocessing the original point cloud sequence to generate a clean point cloud sequence; preprocessing the original image sequence to generate an enhanced image sequence; performing rough matching on the clean point cloud sequence according to the pose information to generate a panoramic scene point cloud; projecting points of the panoramic scene point cloud to the enhanced image sequence; mapping the pixel color to the panoramic scene point cloud by applying an L-M algorithm to generate a color point cloud; inputting the enhanced image sequence into a pre-trained SegFormer semantic segmentation model to obtain semantic probability distribution of each image; mapping the color point cloud to a corresponding image according to the semantic probability distribution to generate a semantic point cloud, the semantic point cloud including color information and semantic information; and generating a three-dimensional scene model according to the semantic point cloud to output an inspection route.

The invention relates to a 3D target detection method and device for adaptively optimizing BEV features, computer equipment and a storable medium. The 3D target detection method for adaptively optimizing the BEV features comprises the following steps: acquiring all-round view image data from a Nuscenes data set; performing feature extraction on the look-around image by using a feature extraction backbone network to obtain image features; raising the dimension of the image features to a BEV space, and compressing a z-axis to obtain BEV features; n 3D query boxes to be queried are uniformly arranged in a BEV space, candidate boxes are projected to a BEV plane to obtain a 2D rotating frame, BEV features in the 2D rotating frame are extracted as prior features, the center points of the query boxes are used as query points, camera parameters are utilized to project the query points to an image feature plane for interpolation to obtain corresponding query features, and the prior features and the query features are fused on a channel level; the method comprises the following steps of: predicting a perd box and a label score by using fusion features, manually generating optimization points at the centers of six surfaces of k pred boxes before the score, adding offset, optimizing and updating BEV features by using the corresponding fusion features, sending the optimized BEV features into a positioning detection head and a classification detection head, and respectively predicting a 3D object bounding box and an object category. The method has the advantage that the accuracy of 3D target detection of the look-around image is greatly improved.

The present invention discloses a multi-point projection device for improving the accuracy of laser ranging and its measurement method, belonging to the technical field of laser ranging; it first emits at least three laser beams in sequence through a laser emission unit, and the laser irradiates the surface of the object to be measured at different angles in sequence through an optical scanning unit, and after being reflected by the surface of the object to be measured, it is received by a laser receiving unit through an optical receiving unit; the data processing unit obtains the ranging value of each laser beam according to the received data, and calculates the spatial coordinate data of each measurement point by combining the emission angle of each beam; constructs a Hermite matrix from the obtained spatial coordinate data to obtain the minimum eigenvalue and eigenvector, and obtains the coefficients of the plane equation according to the minimum eigenvalue and eigenvector; substitutes the obtained coefficients of the plane equation into the projection distance formula from a point to a plane to obtain the actual distance between the laser ranging device and the object to be measured; maximally eliminates external errors and improves the ranging accuracy.

The invention discloses a lunar surface target three-dimensional point cloud detection and positioning method and system based on semantic information, relates to the technical field of three-dimensional object detection, and receives three-dimensional point cloud data and a two-dimensional image detection result, the two-dimensional image detection result is obtained by detecting acquired image data based on a pre-established two-dimensional target detection model, performing ground segmentation on the three-dimensional point cloud data to obtain non-ground point cloud data, mapping the non-ground point cloud data into a two-dimensional grid with a preset size to obtain a two-dimensional clustering result, and performing clustering on the non-ground point cloud data to obtain a clustering result; mapping the two-dimensional clustering result to the three-dimensional point cloud data to obtain a plurality of point cloud clusters; the points of each point cloud cluster are projected into an image coordinate system, dynamic weighted scores are calculated based on the number and confidence of the projection points of each point cloud cluster falling into the detection frame and the geometrical characteristics of the point cloud clusters, the point cloud cluster with the highest dynamic weighted score is selected for distance calculation, and the distance of the point cloud cluster with the highest dynamic weighted score is calculated; and obtaining a lunar surface target three-dimensional point cloud detection and positioning result.