59 results about "Inverse projection" patented technology

The invention discloses a steel bar spacing measurement method and system based on multi-view vision, and relates to the field of distance measurement, in the method, based on remapping parameters, a stereo correction module carries out stereo correction on left and right images to obtain corrected left and right images; based on the global features of the corrected left and right images, a reinforcement depth information calculation module calculates a feature matching constraint matrix; based on the corrected local feature points of the left and right images, a reinforcing steel bar depth information calculation module determines matched feature point pairs; based on the matched feature points, a steel bar depth information calculation module calculates a parallax value and calculates depth information of a steel bar intersection point according to the parallax value and a preset camera calibration parameter; based on the corrected left and right images, a reinforcing steel bar intersection point three-dimensional coordinate reconstruction module detects and reconstructs pixel coordinates of reinforcing steel bar intersection points; and based on the depth information and the pixel coordinates, the physical spacing calculation module calculates the spacing of the reinforcing steel bars according to an inverse projection formula. The method is used for improving the accuracy of steel bar spacing measurement.

The invention relates to a large language model long context cache compression method, system and device and a medium, and the method comprises the steps: obtaining a long text data set as verification data, inputting a large language model, calculating key value cache, executing Fourier projection and expansion operation, and calculating data reconstruction loss of each feature dimension, identifying key value dimensions insensitive to long text context information; in a text data pre-filling reasoning stage, finite-order Fourier projection compression is performed on key value cache data in a GPU memory, and marked data with insensitive dimensions are compressed from an original storage mode of linearly increasing along with the text length to a fixed-length frequency spectrum for representation; in the decoding reasoning stage, inverse projection expansion is directly carried out on the compressed Fourier projection coefficient in a GPU memory to restore the insensitive dimension by customizing a kernel operator in the GPU, and the attention weight is calculated. Compared with the prior art, the method has the advantages of reducing the storage overhead of the large language model and the like.

The invention discloses a geometric perception multi-view consistency image generation method based on a diffusion model. The geometric perception multi-view consistency image generation method comprises the following steps: S1, multi-modal condition coding: uniformly coding conditions such as texts, reference images and camera postures into fusion features and inputting the fusion features into U-Net; and S2, gating multi-path attention fusion: dynamically fusing the multi-source features in the U-Net through a gating multi-path attention mechanism, and enhancing the multi-view two-dimensional features. And S3, voxel feedback closed-loop refinement: inversely projecting the two-dimensional features to a three-dimensional voxel space, and re-projecting the two-dimensional features back to two dimensions after three-dimensional volume accumulation so as to form closed-loop geometric feedback. And S4, residual error correction autoregressive sampling: generating an image according to autoregressive of a view angle sequence, reprojecting and correcting a current sampling track by using front view angle information, and inhibiting error accumulation. And S5, composite supervision collaborative optimization: carrying out collaborative optimization on the model in combination with multiple loss functions, and freezing a backbone network so as to realize multi-view generation with high geometric consistency and high quality.

The invention relates to a vehicle visual positioning optimization method in a structured scene, and the method comprises the steps: calibrating a camera of an unmanned vehicle, obtaining a distortion-removed image, converting the distortion-removed image into a bird's-eye view through the inverse projection, updating the initial depth estimation result of a foresight camera through the bird's-eye view, and obtaining reliable depth estimation. And performing vanishing point estimation based on space line segment cosine similarity by using depth information in reliable depth estimation, estimating the attitude of the unmanned vehicle and the sub-tratta coordinate system, obtaining an attitude estimation result, updating an attitude extended Kalman filter of the unmanned vehicle by using the attitude estimation result, and determining attitude information of the unmanned vehicle. A sub-graph optimization objective function of the sliding window is further set, and a cost function is introduced to update the position information of the unmanned vehicle; and a key frame is extracted based on the attitude information and the position information, when the key frame detects that the unmanned vehicle returns to the intersection where the unmanned vehicle passes historically, a loopback frame is searched in an uncertainty ellipse, and a historical track is optimized after successful matching.

The present application relates to computer vision and three-dimensional reconstruction technical field, disclose a kind of based on image recognition's building decoration component three-dimensional modeling method, method includes the following steps: receiving near-axis active illumination image sequence, solving camera pose and equivalent point light source trajectory;Inverse projection two-dimensional foreground mask generates maximum base envelope grid;Combining brightness gradient and texture high-frequency edge tensor field removes false shadow, extract effective sweep shadow mask;Tracking shadow residual optical flow displacement and combining light source trajectory analysis absolute depth, generate local three-dimensional point cloud;Surface reconstruction is executed to point cloud and opening plugging constructs negative space primitive entity grid;Extract the Laplace curvature energy of interface to generate normal shrinkage constraint, control entity grid after shrinkage and envelope grid execute Boolean difference set operation output model.The present application extracts absolute depth in featureless recessed area, and eliminates the boundary interference and distortion of grid Boolean operation.

The application provides a kind of plane panel surface coating profile size detection method, comprising the following steps: arranging positioning target in the uncoated area of the plane panel to be measured and collecting the image of the plane panel to be measured, extracting the panel outer contour and coating profile from the image;High-precision three-dimensional reconstruction is carried out on the positioning target to obtain three-dimensional point cloud data, and the spatial plane expression of the plane panel is fitted;The coating profile and the panel outer contour are calculated by inverse projection to the plane panel surface, and the three-dimensional point cloud data of the coating profile and the panel outer contour is obtained, and the two-dimensional point cloud data is obtained by dimension reduction on the three-dimensional point cloud data;Read the design profile of the plane panel, calculate the coordinate conversion relationship with the outer contour as the registration object;The two-dimensional point cloud data of the coating profile is registered with the design profile by the coordinate conversion relationship, and the detection value of the coating profile size error is obtained.The application can be suitable for surface coating size detection of plane panels with different sizes, sizes and coating shapes.

This invention relates to the field of point cloud computing technology, specifically a hatch positioning method based on multimodal lidar point clouds. First, a multi-dimensional point cloud containing three-dimensional coordinates and reflection intensity is acquired. The coordinate system of the multi-source data is unified and fused through radar extrinsic parameter calibration or point cloud fusion algorithms. Then, the deck area point cloud is segmented based on normal vector filtering and DBSCAN clustering, and converted into a two-dimensional image through orthogonal projection. Next, edges are extracted using an improved Canny operator, and the hatch planar position is determined by least-squares rectangle fitting. Finally, the three-dimensional coordinates of the hatch are restored through inverse projection mapping. This invention overcomes the detection limitations of a single radar, effectively improving the accuracy and stability of hatch positioning under complex conditions. It can provide reliable positional information for the automated control of bulk carrier unloading operations, and has significant industrial application value.

The present application relates to a kind of radar scattering cross section test methods based on deep learning and ISAR imaging inversion, the method includes using ISAR imaging test mode to obtain target and calibration body broadband radar scattering echo signal, calibration processing and applying filter-inverse projection imaging algorithm to carry out ISAR imaging, application completes the deep learning model of accurate extraction ISAR image scattering signal area, application FFT transform-interpolation integral ISAR imaging inversion algorithm high-precision inversion reconstruction RCS value, change ISAR imaging aperture center azimuth angle traversal acquisition target all azimuth angle RCS data.The radar scattering cross section test method based on deep learning and ISAR imaging inversion provided by the present application solves the accurate extraction ISAR image scattering area and ISAR image high-precision inversion reconstruction RCS and other test practical problems, expands RCS data acquisition method.

The invention relates to a method for fusing a monitoring video and a 3D virtual scene, and belongs to the technical field of image processing and computer vision. The method comprises the following steps: firstly, constructing a three-dimensional digital scene and completing space calibration of a camera; then, camera offset is detected and corrected through real-time video analysis, and meanwhile, a target in a picture is recognized and tracked; secondly, the 2D pixel coordinates of the target are converted into space coordinates in a 3D scene through inverse projection calculation, and the positioning precision is improved through the multi-view intersection technology; and finally, based on the converted 3D coordinate data, performing fusion rendering on a video picture in a rendering engine, and outputting an interactive three-dimensional scene which can be used for situation awareness and behavior analysis. According to the method, the problems of inaccurate virtual and real space registration, dynamic error accumulation and unnatural rendering in the prior art are effectively solved, and high-precision and dynamic fusion of the monitoring video and the virtual scene is realized.

The invention provides a gastrointestinal three-dimensional dynamic imaging analysis method based on conductivity distribution modeling, and the method comprises the steps: applying at least three composite electric signals with different frequencies to a gastrointestinal region, collecting voltage response signals, and constructing a dynamic conductivity parameter matrix based on frequency domain feature separation. And then, spatial discretization processing is carried out by using a nonlinear interpolation algorithm, a three-dimensional conductivity distribution map is generated, dynamic modulation and demodulation processing is carried out on the map, and a frequency band weight coefficient is adjusted. And mapping the modulated data to a dynamic imaging coordinate system through an inverse projection algorithm to generate a three-dimensional dynamic imaging result. According to the space coordinates and the change rate of the conductivity gradient mutation area, the position and the activity state of the gastrointestinal abnormal area are judged, and a self-adaptive feedback mechanism is adopted to update boundary condition parameters of the initial conductivity distribution model. The accuracy and efficiency of gastrointestinal disease diagnosis can be improved.

The invention relates to a radar cross section test method based on deep learning and ISAR imaging inversion, and the method comprises the steps: employing an ISAR imaging test mode to obtain broadband radar scattering echo signals of a target and a calibration body, carrying out the calibration processing, and carrying out the ISAR imaging through employing a filtering-inverse projection imaging algorithm, and accurately extracting an ISAR image scattering signal region by using the trained deep learning model, inverting and reconstructing an RCS value with high precision by using an FFT (Fast Fourier Transform)-interpolation integral ISAR imaging inversion algorithm, and traversing by changing an ISAR imaging aperture center azimuth angle to obtain target omni-directional angle RCS data. According to the radar cross section test method based on deep learning and ISAR imaging inversion provided by the invention, the test practice problems of accurate extraction of an ISAR image scattering region, high-precision inversion reconstruction of the RCS by the ISAR image and the like are solved, and an RCS data acquisition method is expanded.

The present application belongs to the technical field of face attribute fidelity editing, and relates to a super-resolution face attribute lossless editing method, device and readable storage medium; an original super-high resolution source image is input into a local perception network, a core topological anchor point set of a target face is output, a forward spatial affine transformation mapping operator is solved to perform affine clipping and normalized projection on a region of interest containing the target face in the original super-high resolution source image, and a local feature sub-manifold image is obtained; the local feature sub-manifold image is input into a generative adversarial network to output an evolved feature manifold image; a matrix inversion operation is performed on the forward spatial affine transformation mapping operator to obtain an inverse spatial mapping operator to perform topological inverse projection transformation on the evolved feature manifold image, and output a low-dimensional evolved image; the low-dimensional evolved image is enlarged and rotated to a physical coordinate system of the original super-high resolution source image by using a high-order resampling interpolation algorithm, and a high-dimensional registered evolved image is obtained.

The application discloses a mesh texture simplification algorithm suitable for three-dimensional reconstruction, and comprises the following steps: S1, reference three-dimensional model scene construction; using the recovered scene structure in three-dimensional reconstruction and the calibrated image, performing texture mapping on the original fine three-dimensional mesh to complete the reconstruction of the reference three-dimensional model scene; S2, reference three-dimensional model scene image acquisition; according to the three-dimensional space relative attitude relationship between the reference three-dimensional model scene and the view internal and external parameters, using the inverse projection principle to perform three-dimensional mesh to two-dimensional image rasterization calculation pixel by pixel and view by view, and completing the acquisition of the reference image set; S3, mesh and texture simplification; using the QEM algorithm to simplify the mesh, using the reference image set as a data source and using the texture reconstruction algorithm to perform texture remapping and simplification. The application has the advantages that the algorithm can support mesh and texture simplification together, under different texture simplification parameters, the texture almost has no distortion and can significantly reduce the texture data amount.

This invention belongs to the field of industrial automation control technology, specifically relating to an intelligent optimization system for process parameters in automotive steel wire production. The system includes: a data acquisition module, a deviation calculation module, a prediction module, and a control module. The data acquisition module constructs a Lagrange coordinate system with material particles as the core, collects time-series process data throughout the entire process, and maps it to the material particles. The deviation calculation module calculates the historical deviation modulus of the material particles based on the arc-length integral of the state deviation trajectory. The prediction module constructs a dissipative damping model based on the historical deviation modulus and calculates the performance loss of the material particles. The control module generates process parameter compensation quantities through inverse projection based on a preset historical regression gradient vector and performance loss, and implements feedforward control of the equipment. This invention achieves closed-loop compensation for historical accumulated damage, improving the consistency of automotive steel wire product performance.

The invention relates to a method for automatic segmentation of a dental arch that comprises acquiring a three-dimensional surface of the dental arch, in order to obtain a three-dimensional representation comprising a set of vertices, generating virtual views from the three-dimensional representation, projecting the three-dimensional representation onto each two-dimensional virtual view, in order to obtain an image representing each vertex on the virtual view, processing each image by means of a deep learning network, carrying out inverse projection of each image in order to assign, to each vertex of the three-dimensional representation, one or more pixels of the images in which the vertex appears and to which it corresponds, and assigning one or more probability vectors to each vertex, determining the class of dental tissue to which each vertex most probably belongs based on the probability vector or vectors.

Disclosed are a method, device, and system for X-ray CT contrast imaging using magnetic nanoparticles. The method includes: rotating a target object containing magnetic nanoparticles as contrast agent for one circle relative to X-rays, recording multiple consecutive transmission projection images of the target object at all angles; for multiple transmission projection images at each angle, after conducting image post-processing operations, performing inverse projection transform on magnetic nanoparticle imaging projection images at all angles to obtain a tomographic imaging result. The image post-processing operation includes: time-frequency transform: converting the third dimension of the three-dimensional time domain matrix to frequency domain to obtain a three-dimensional frequency domain matrix; feature extraction: selecting a two-dimensional matrix with frequency of kf along the third dimension of the three-dimensional frequency domain image matrix; k is a positive integer; f is the frequency of the excitation magnetic field.

The invention provides a multi-line LiDAR orchard trunk instance segmentation method based on image deep learning. The method comprises a point cloud acquisition and preprocessing step, a point cloud spherical projection step, a trunk image semantic segmentation model training step, a trunk image semantic segmentation step, an image spherical inverse projection step and a trunk point cloud instance segmentation step. According to the method, sparse and disordered multi-line LiDAR point cloud is converted into dense and regular image data through a spherical projection technology, and the data processing complexity is remarkably reduced while multi-dimensional information such as three-dimensional coordinates, distance and strength is reserved; a trunk semantic feature is mined from the annotated data in combination with a U-Net architecture network, and a high-precision trunk semantic segmentation model is constructed; a traditional point cloud processing algorithm is introduced for optimization, and the progress from semantic segmentation to instance segmentation is realized. According to the scheme, while the calculation efficiency is considered, the complex interference problems of trunk form diversity, low vertical crowns, ground facilities and the like in the orchard scene can be effectively solved, and accurate real-time segmentation of the orchard trunk instance is realized.

The application discloses a kind of laser radar point cloud prediction method and system based on Transformer, it is related to three-dimensional environment perception technical field.The method first converts distance image by spherical coordinate projection conversion for historical point cloud sequence with semantic label, and utilizes semantic embedding module to map discrete semantic label into high-dimensional continuous feature;Subsequently, the geometric feature and semantic feature are spliced in channel dimension, to form the integrated composite feature tensor;The tensor is input into the improved Transformer network for training, the network adopts encoder-double-branch attention-decoder architecture, and is supervised using the composite loss function combining geometric loss and semantic loss;Finally, the network synchronously outputs future distance image and semantic probability distribution, and generates future three-dimensional point cloud with semantic attributes through inverse projection and color mapping.The application significantly improves the modeling ability of the motion law of different categories of objects and the prediction accuracy in complex dynamic scenarios by explicitly integrating semantic information.

The present application relates to the technical field of computer vision and data processing, and particularly relates to a cascade image reconstruction method based on a physical residual feedback mechanism, equipment and medium, comprising inputting observation data into a pre-trained first-level reconstruction network to output an initial reconstruction image; subsequently, a physical forward projection calculation data domain residual is used, and inverse projection is performed to generate a spatial attention mask; finally, the initial reconstruction image is combined with the spatial attention mask to obtain a reconstruction image embedded with the spatial attention mask, which is input into a second-level refining network; and thus a final reconstruction image is output. The method effectively suppresses artifacts that violate physical facts, guarantees the physical authenticity of the reconstruction result, overcomes the edge oversmoothing problem caused by the traditional global unified updating strategy, and significantly improves the edge sharpness and detail integrity of key structures in the reconstruction image.

This invention discloses a multimodal intelligent safety early warning system for oil and gas field stations, relating to the field of industrial information security technology. It includes the following modules: a multimodal temporal alignment module for outputting initial multimodal data frame pairs; an acoustic spectral fingerprint extraction module for outputting acoustic spectral fingerprint vectors; a visual micro-perturbation perception module for outputting visual perturbation tensors; a physical constraint cross-modal coupling module for introducing a strain energy gradient-driven crack tip field energy diffusion constraint mechanism to improve the ViLBERT model; a temporal correlation dynamic verification module for outputting verification confidence levels; an early warning decision module for outputting safety early warning signals; and a three-dimensional inverse projection tracing instruction module for generating safety early warning instructions. This invention overcomes the limitations of traditional methods, such as difficulty in cross-modal alignment and susceptibility to background noise interference, providing an efficient solution for intelligent safety early warning in oil and gas field stations.

The invention provides an instrument reading method, system and device based on OCR combined with three-dimensional geometric constraint and a medium, belongs to the technical field of instrument information reading, and is realized through an end-edge-cloud collaborative architecture. The method comprises the steps that an inspection robot collects and preprocesses an image; a lightweight convolution-Transform mixed backbone is used for extracting features; regression of a two-dimensional point set and uncertainty is carried out by a key point detector; a weighted PnP algorithm is combined with the three-dimensional template to solve the pose of the camera, and a front view is generated through inverse projection correction; the correction features are input into an OCR decoder to recognize a character sequence, Aleoric and Episteric uncertainty is output in parallel, and the Aleoric and Episteric uncertainty is combined into comprehensive uncertainty; according to a threshold grading decision, low-risk direct output, middle-risk upload correction image cloud review and high-risk upload original image manual review are carried out; and returning a rechecking result and a process data log. According to the method, the instrument recognition robustness in a complex scene is improved, resource allocation is optimized, and traceability and sustainable evolution of the whole process are realized.

The application discloses a method and device for correcting image hardening artifacts, which comprises the following steps: obtaining an original image, segmenting the original image to obtain a high-density tissue image, performing orthographic projection, data fusion and inverse projection on the image to obtain first fusion data and second fusion data, and finally solving a correction coefficient array according to the original image, the high-density tissue image, the first fusion image and the second fusion image, and obtaining a corrected image by combining the correction coefficient array. The correction method is based on the original image, and does not involve the steps of obtaining and processing of the ray energy in the correction process, so that additional prior knowledge such as energy-dependent attenuation coefficient, ray source energy and detector related information is not required. The corrected image can be obtained only by simple calculation based on the original image, the speed is relatively faster than that of the iterative algorithm, the efficiency of artifact correction is improved, and the method is more suitable for engineering application.

Methods and apparatus for encoding and decoding a volumetric scene are disclosed. A set of attribute and geometry patches is obtained by projecting samples of the volumetric scene onto the patches according to projection parameters. If the geometry patch is comparable to a planar layer located at a constant depth according to the projection parameters, only the attribute patch is packed in an attribute atlas image and the depth value is encoded in metadata. Otherwise, both attribute and geometry patches are packed in an atlas. At the decoding, if metadata for an attribute patch indicates that its geometry may be determined from the projection parameters and a constant depth, the attributes are inverse projected on a planar layer. Otherwise, attributes are inverse projected according to the associated geometry patch.

This invention relates to the field of marine geological 3D visualization technology, specifically disclosing a marine geological VR 3D visualization simulation system that integrates panoramic real-world footage. The system includes a visualization simulation platform, which is communicatively connected to the following modules: a coordinate mapping and alignment module, used to precisely align the spherical UV coordinates of the panoramic real-world footage with the terrain world coordinate system in Unity3D through inverse projection transformation using a unified virtual-real coordinate mapping algorithm, and to smooth the parallax jumps during head rotation using quaternion interpolation. This invention precisely aligns the spherical UV coordinates of the panoramic real-world image with the 3D terrain world coordinate system using a unified virtual-real coordinate mapping algorithm, and uses quaternion spherical linear interpolation to smoothly transition the parallax changes during head rotation, eliminating the visual disconnect between the panoramic background and the 3D terrain, and ensuring the continuity and stability of spatial perception when the user's perspective changes.

The invention discloses a voice coding and decoding method based on principal component analysis and multi-scale depth attention, and relates to the technical field of voice signal processing, and the method comprises the steps: carrying out the multi-scale depth attention convolution operation of a voice signal for many times, and obtaining a first feature; wherein after feature extraction and feature fusion are respectively carried out through a plurality of parallel depth separable convolutions with different depth convolution kernels, channel-by-channel multiplication is carried out on fused features according to channel attention weights; performing projection processing on the first feature according to the projection matrix and the feature mean value to obtain a second feature; wherein principal component analysis is carried out on first features of a training set, a feature mean value is determined, and a previous feature vector with the highest cumulative variance contribution rate is selected to form a projection matrix; performing multi-stage vector quantization on the second feature in sequence to obtain a reconstructed vector; and carrying out inverse projection processing on the reconstructed vector, and then decoding and outputting reconstructed voice. And ultra-low-bit-rate and high-quality voice communication is realized.

The application relates to the technical field of intelligent driving, in particular to a sparse-labeled obstacle label enhancement method and device, equipment and a storage medium, which comprises the following steps: based on the 3D box parameter of a sparse-labeled key frame target, a 3D box parameter change curve of the target is fitted and obtained, and interpolation is carried out based on the 3D box parameter change curve to obtain a 3D interpolation box of the target; a true value 2D box of a sparse-labeled adjacent key frame target is inversely projected to a 3D space to obtain a key frame 3D inverse projection box, interpolation is carried out to obtain a 3D inverse projection interpolation box, and a 2D optimized interpolation box is obtained by projection with the width-height ratio of the 3D inverse projection interpolation box as a constraint; the 3D interpolation box is corrected based on the 2D optimized interpolation box to obtain a 3D optimized interpolation box. The technical problem that the prior art completely depends on a 3D point cloud detection model, and the detection and interpolation precision sharply decreases due to insufficient point clouds in a point cloud sparse scene such as a long-distance, low obstacle and serious occlusion can be solved.

The application discloses a kind of based on 3D vision's packaging chip substrate face warping detection method and system, method includes: the real three-dimensional view of chip is obtained by the structured light projection technology in optical triangulation;Based on template matching algorithm, complete chip area is extracted in depth map;Depth map is converted into three-dimensional point cloud data using inverse projection conversion;The point cloud data obtained is filtered, and noise point not belonging to substrate surface is removed;Multiple regions of interest are set on substrate surface, and ROI meeting conditions are screened and retained;RANSAC random sampling consistency algorithm is used to plane fitting reserved ROI to generate reference surface;The maximum and minimum perpendicular distance of each point on substrate surface to reference surface is calculated, and combined with set threshold value, warping is quantitatively judged, to determine whether chip exists warping deformation.The application can effectively extract the warping parameter of single chip, realize the automatic determination of chip warping state.

The invention relates to the technical field of robot perception and automatic driving, and discloses a laser radar and camera target-level semantic fusion method and system based on inverse projection, and the method comprises the steps: constructing a laser radar-camera calibration model based on environment arc features, extracting and optimizing the coordinates of the intersection point of the image arc features and the laser radar, and obtaining a target-level semantic fusion model; solving a projection matrix between the sensors; a multi-dimensional semantic segmentation algorithm based on inverse projection is designed, a segmentation region is obtained through inverse projection of a target bounding box, and unified segmentation of 2D / 3D laser point clouds is realized in combination with ground point cloud filtering, distance adaptive clustering, regional point cloud purification and regional growth; constructing a target-level semantic fusion framework, and integrating laser radar depth information and camera texture information; the method can improve the semantic segmentation precision (mIoU on a SemanticKITTI data set reaches 77.1% and exceeds 1.4% of the prior art) under the scene of lack of dense mark data, sparse point cloud or distance change, reduces background interference and calculation load, and is suitable for scenes of mobile robots, automatic driving and the like.

This invention belongs to the field of artificial intelligence technology and discloses a method and system for semantic assignment of two-dimensional LiDAR point clouds based on a camera. The method includes: obtaining the projection transformation matrix from the camera image plane to the laser scanning plane; obtaining the pixel set within the target bounding box of the target object image based on the target object image captured by the camera; extracting image features from the target object image to obtain the contour geometric line equation of the target object; inversely projecting the contour geometric line equation of the target object onto the LiDAR coordinate system using the projection transformation matrix to obtain the inverse projection curve; determining the range of laser points hitting the target object based on the inverse projection curve, and assigning semantic information to the laser points within the range. This invention abandons complex clustering algorithms, data fusion algorithms, and a large amount of laser point reprojection calculations. It only requires inverse projection of the contour to filter out the range of laser points hitting the target object, improving the accuracy of point cloud search and the speed of semantic assignment.