80 results about "Local matching" patented technology

The invention discloses a business process multi-dimensional logic verification method and system based on a dynamic knowledge graph, and relates to the technical field of artificial intelligence and financial risk control. The method aims at solving the technical problems that a traditional rule engine cannot process unstructured data, and a large generative model has logic illusion and privacy risks. The method comprises the following steps: constructing a rule knowledge graph containing parameterized logic constraints (such as time sequence, mutual exclusion and numerical accumulation); converting the unstructured business operation record into a feature vector by using a locally deployed semantic model; performing global sequence alignment optimization by constructing a global semantic consistency objective function, mapping operation records to a theoretical decision path derived from a graph, and eliminating local matching ambiguity; and finally, on the basis of a mapping result, calling deterministic parameters in map edge attributes to execute strict mathematical logic verification (including complex workday calculation, mutual exclusiveness check and the like). According to the method, a'nerve-symbol 'dual architecture is adopted, accurate understanding of fuzzy semantics is achieved, logic verification certainty and data privacy security are guaranteed, and the method has a hidden process discovery capability.

The invention discloses a video key frame extraction method fused with self-supervised deep learning, and the method comprises the following steps: carrying out the standardized sampling of an input video according to a fixed interval, extracting a SuperPoint local key point and a Video MAE global semantic feature, and generating a dense descriptor and a semantic vector; constructing multi-dimensional change indexes such as local matching degree and global similarity; constructing a soft distribution matrix and a matching point set based on the fusion features; dynamically judging the key frame through a self-adaptive multi-threshold rule; and outputting the key frame set. The method fuses local and global spatio-temporal information, has robust feature extraction and key frame discrimination capabilities under a weak supervision condition, and can effectively improve the efficiency and precision of video compression, abstract and event detection.

The invention relates to the technical field of intelligent recognition, in particular to a distributed edge calculation face recognition method and system for non-inductive passage, and the method comprises the steps: capturing a video stream through edge image collection equipment disposed at a passage entrance, and extracting a face image; and carrying out localized preprocessing, feature extraction and quick comparison with a local cache feature library by the edge computing node. If local matching succeeds, passing is controlled immediately; if the matching fails, the features are uploaded to a central cloud server, enhanced processing and global library comparison are performed by using the stronger computing capability of the central cloud server, and a result is issued and executed, so that the low delay of edge computing and the strong capability of cloud computing are effectively fused, and both the recognition efficiency and the system reliability are considered; and meanwhile, links such as encrypted transmission, dynamic updating, abnormal offline processing and detailed log recording are covered, the security, adaptability and maintainability of the system are remarkably improved while efficient non-inductive traffic is realized, and the method is suitable for intelligent traffic management in a large-scale and high-concurrency scene.

The invention discloses a small sample learning and out-of-distribution detection method and system based on global and local image-text alignment, and the method comprises the steps: collecting image data and corresponding class labels, obtaining a data set, processing the data set, and dividing the data set into a training set, an in-distribution test set, and an out-of-distribution test set; an out-of-distribution detection model is constructed and trained, the training comprises two stages, in the pre-training stage, text description is carried out on various image data of a training set, text description is obtained, in the training stage, global and local image-text features are extracted, and related and unrelated local image features are screened out; performing local supervised contrast learning on the fine-tuned text and related local image features, calculating global and local matching scores by using the image features and the fine-tuned text features, and completing model training; and performing classification prediction probability detection on the trained model. According to the invention, the performance of small sample image classification and small sample target detection is improved at the same time.

The invention provides an SLAM (Simultaneous Localization and Mapping) optimization method and equipment based on point cloud registration and adaptive resolution, and a medium, and relates to the technical field of AGV (Automatic Guided Vehicle) positioning and mapping, and the method comprises the following steps: (1) inputting and preprocessing sensor data; (2) front-end scanning and matching: based on the preprocessed data, carrying out point cloud registration through a GICP algorithm, updating the pose of the current frame and constructing a local subgraph; (3) global SLAM: receiving sensor data, poses and subgraph data from the front-end scanning and matching step, and carrying out loopback detection and global optimization; wherein a self-adaptive resolution switching strategy based on environment complexity is adopted in the loopback detection process; according to the GICP algorithm, the surface of the point cloud is modeled as Gaussian distribution, local features are described through a covariance matrix, and a target error function constructed based on the mahalanobis distance is optimized to estimate pose transformation. According to the method, the local matching precision and robustness are improved by replacing ICP with GICP, and the global calculation efficiency is adjusted and optimized in combination with the dynamic resolution.

The invention discloses a dual-stage CBCT and oral scanning tooth registration method and system, and relates to the field of tooth three-dimensional digital model registration, and the method comprises the following steps: extracting a tooth voxel structure in a CBCT image and a dental crown grid structure in an oral scanning image through a segmentation network; a coarse-to-fine depth registration network is introduced, geometric feature coding and feature matching are carried out on super-points obtained through multi-resolution down-sampling, local matching is carried out on a dense point set in a super-point neighborhood, a rigid transformation matrix is calculated in parallel, and an optimal registration result is screened out from the rigid transformation matrix; and dividing a registration object into a plurality of three-tooth groups containing three adjacent teeth, and performing local iteration alignment based on an initial registration result. The method still has higher robustness and accuracy under the complex conditions of large cross-modal difference, high input point cloud density and the like, is shorter in time consumption, and provides reliable data basis and automatic support for clinical scenes such as orthodontics and implantation.

The invention discloses an image staged matching and positioning method, and relates to the technical field of image analysis. Comprising the following specific steps: constructing a model architecture, and inputting an unmanned aerial vehicle image u; candidate images {u, Si} are screened through global semantic feature matching; the method comprises the following steps: extracting image key points and description words, establishing an initial matching point pair set, introducing a mismatching screening mechanism, executing homography transformation on geotagging information in a satellite image, and determining ground target positioning under the view angle of an unmanned aerial vehicle. According to the method, through a mode of combining global feature optimization and local mismatching screening, accurate positioning of a ground target under the view angle of the unmanned aerial vehicle is realized, and an optimal matching candidate is rapidly screened out; in the local matching stage, mismatching point pairs are eliminated, and the geotagging information in the satellite image is accurately mapped to the unmanned aerial vehicle image coordinate system through homography transformation, so that the image matching problem under the large view angle change is effectively solved, and the accuracy and efficiency of image matching and positioning are improved.

The invention relates to an environment sensing method based on target detection and stereo matching. According to the environment perception method based on target detection and stereo matching, targets in an environment image can be recognized through model training, and category information and position information of each target can be recognized, so that a target detection model can recognize the category information and the position information of the targets in the environment image in real time; through a local matching strategy of a target area delineated by focusing on the position information of the target, huge calculation overhead caused by full-image matching is avoided; pixel-level parallax information in the two-dimensional image is accurately converted into a physical depth distance in a three-dimensional space in combination with calibration parameters of a binocular camera in an environment sensing system; and finally outputting an environment perception image capable of displaying a detailed perception result of each detected target through information fusion. According to the environment sensing method, the efficiency and robustness of the whole sensing process are remarkably improved while the accuracy and integrity of environment sensing are ensured.

The invention relates to the technical field of three-dimensional modeling, in particular to an augmented reality-oriented three-dimensional scene reconstruction method and system, and is used for solving the technical problem that an iterative nearest point algorithm cannot meet high real-time performance and high-precision reconstruction requirements required by augmented reality at the same time. The method comprises the following steps: acquiring a pose transformation relationship between multiple frames of point cloud data and adjacent frames of point cloud data; extracting a plurality of feature points in each frame of point cloud data, and determining a feature descriptor of each feature point; according to the feature descriptors of the feature points, calculating a matching weight between feature point pairs in two adjacent frames of point cloud data, and calculating a local matching error based on the matching weight and a pose transformation relationship; performing global matching optimization based on the local matching error of the plurality of feature point pairs and the distribution density of each feature point in the respective point cloud frame, and determining an optimal matching point pair set which minimizes the global matching error; and according to the optimal matching point pair set, fusing the multi-frame point cloud data, and reconstructing a three-dimensional scene model.

The invention discloses a text matching method and device based on probability distribution, equipment and a storage medium, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining semantic feature distribution of each professional knowledge text from a professional knowledge base, obtaining a knowledge probability distribution set, abstracting the intrinsic features of the text through probability distribution, and obtaining a knowledge probability distribution set; and standardized representation of the knowledge base is realized. The text input by the user is obtained, the corresponding user text probability distribution is calculated, and the fault tolerance of text matching is remarkably improved. The similarity distance between the user text probability distribution and each distribution in the knowledge probability distribution set is calculated, and the overall semantic similarity instead of local matching is measured through the distribution distance, so that the comparison process can tolerate the distribution offset. Finally, a text matching result is determined according to the minimum value of the similarity distance, the effect of stably retrieving semantic related professional knowledge under high noise is achieved, and fault tolerance and matching precision are remarkably improved.

The invention relates to the technical field of strain measurement, in particular to a three-dimensional full-field strain measurement method and system based on multi-view collaboration, and the method comprises the steps: constructing a sparse topology skeleton model of a measured target under a global coordinate system, and building a bidirectional projection mapping relation between the global situation and each local situation; the method comprises the following steps: synchronously acquiring a time sequence texture feature image of a measured target, calculating a luminosity geometric coupling confidence tensor of each pixel point and extracting an initial texture feature field under each view angle; utilizing a skeleton model to guide the initial texture feature field to perform limited local matching, and resolving to obtain a local independent displacement field and residual energy distribution of each view angle; performing multi-view weighted fusion based on a bidirectional projection mapping relation, and reconstructing a global three-dimensional displacement field; and performing differential calculation on the global three-dimensional displacement field to obtain a deformation gradient tensor and generate three-dimensional full-field strain distribution. According to the method, gradient mutation caused by view field splicing errors can be effectively inhibited, and physically real and full-field continuous three-dimensional strain distribution is obtained.

The invention discloses a sewer pipe network defect detection method based on multi-label zero sample learning. The method comprises the following steps: generating defect description corresponding to each pipeline defect category through a large language model; performing feature extraction and domain adaptation on the pipeline inner wall image and the defect description by using a representation guide module to obtain global and local image features and defect detailed description text features; synthesizing global and local image features of the image, respectively calculating global and local matching scores of the global and local image features and detailed description text features, and fusing to obtain a defect initial prediction score; constructing a semantic relationship adjacency matrix for displaying the relationship between different defect categories; and correcting the initial prediction score by using the semantic relationship adjacency matrix between the categories to obtain a final prediction score of each defect category. According to the method provided by the invention, knowledge migration from known defects to unknown defects is realized by constructing a guidance-fusion-correction network, and the difficulty in identifying types of unseen defects is effectively solved.

This invention proposes a cross-coordinate system road network data fusion method based on hierarchical matching and non-rigid registration, belonging to the field of road network technology, and solves the problems of computational complexity and poor versatility of existing methods. The method includes: acquiring and preprocessing benchmark and target road network data; estimating global transformation relationships by calibrating feature points, and initially aligning the benchmark road network to the target coordinate system to obtain a global region; constructing a fixed image of the target road network and a moving image of the benchmark road network within the global region; using algorithms such as B-spline free deformation that can model local nonlinear deformations to perform non-rigid registration on the fixed and moving images, optimizing the cost function to obtain pixel-level deformation fields; geometrically correcting the benchmark road network based on the deformation field, transferring the target road network attribute information, and summarizing and outputting a fused dataset. This method improves local matching accuracy and versatility through a hierarchical strategy and pixel-level registration, overcoming the influence of differences in road segmentation.

The invention discloses a fire-fighting monitoring room personnel on-duty monitoring method and system, a medium and a product, and relates to the field of image recognition. The method comprises the following steps: in response to detection that a to-be-monitored object completely enters a preset entrance area, extracting a visual feature vector in an unshielded image frame as an initial reference feature; when the to-be-monitored object moves to the on-duty monitoring area, deducting a shielding part based on an overlapping relationship between the real-time detection frame and the environment shielding mask, and extracting an exposed area; and calculating the local matching degree of the exposed area and the initial reference feature to determine the on-duty state of the personnel, and generating an updated reference feature when the conditions of the spatial overlapping degree and the texture similarity are met. According to the method, the complete features obtained at the entrance are used as the reference, and local comparison is carried out in combination with the exposed area, so that the problem of misjudgment alarm caused by the fact that an existing general target detection model cannot recognize an incomplete human body target in a static facility shielding environment is effectively solved, and the accuracy of on-duty monitoring is improved.

This invention discloses a heterogeneous robot collaborative scheduling method for multiple modules of a space station. It employs a hierarchical distributed collaborative architecture based on a semi-Markov decision process. Physical constraints are transformed into filtering conditions through a pre-defined physical constraint sub-mask that includes area access, resource capacity, deadline reachability, and skill matching. A task residual update mechanism mitigates the waiting and overhead issues caused by explicit synchronization negotiation under communication constraints. A task commitment mechanism is introduced, dynamically updating the task residual based on the confidence level of the preceding robot's commitment to the target task. Subsequent robots then fill in, replace, or re-match based on the updated task residual. When local matching enters an oscillating state or local matching convergence stalls, a skill gap tension vector reflecting the gap status of each skill dimension is generated and fed back to the upper layer. The upper layer determines the cause based on the gap type and degree of different skill dimensions and takes appropriate measures for collaborative scheduling.

The application discloses a kind of based on ecological environmental protection supervision big language model address resolution and geographic coding acquisition method and system.For the fuzzy, non-standard and positioning difficult problems of environmental supervision letter and visit complaint address description, the method first uses big language model to parse the original text into structured address, and cooperates multi-level fault tolerance mechanism to ensure the reliability of parsing result.In the matching stage, the system adopts local priority strategy, and preferentially searches the environmental special position database containing key polluting units and other information;If the initial matching is not hit, the self-defined part-of-speech tag segmentation technology is used to accurately extract the core environmental entities such as pollution outlet and water source protection area for secondary matching.To ensure accurate positioning, the system verifies by semantic similarity and geographical distance, to prevent errors caused by the same name in different places.When local matching fails, the system automatically accesses the network query, and uses big language model to simplify the redundant description and retry, forming a closed-loop error correction mechanism.The application significantly improves the accuracy and efficiency of pollution source positioning, protects data privacy, and provides strong technical support for environmental law enforcement personnel to achieve accurate positioning, rapid verification and scientific division of responsibility area.

The application relates to a multi-sensor-based repositioning method, device, equipment and storage medium. The method comprises the following steps: acquiring environment images collected by respective sensors; performing global matching on the respective environment images and images in a prior environment image library to obtain respective robot poses before updating corresponding to the respective sensors; performing local matching on the respective environment images and respective surrounding environment images corresponding to the respective robot poses before updating in the prior environment image library to obtain score values of respective robot poses after updating; and determining a repositioning result of a current robot pose according to the score values of the respective robot poses after updating. By performing global matching and local matching on the environment images collected by different types of sensors and the images in the prior environment image library, the repositioning result of the current robot pose can be ensured to have accuracy and reliability.

The embodiment of the invention relates to the technical field of federated learning and information security, in particular to a federated multi-target scheduling method and system for industrial control crowdsourcing testing, which is characterized in that sensitive data such as ability, experience and the like of a tester are only reserved at a local terminal by constructing a framework combining local feature coding and federated model training, so that the test efficiency is improved, and the test efficiency is improved. Outward transmission is not carried out; only the model update quantity after the privacy enhancement processing such as gradient cutting and differential privacy noise injection is uploaded to the federated matching learning server, and only the model update quantity after the privacy enhancement processing is uploaded to the federated learning server, so that the leakage risk of concentrated data storage is avoided from the source, the requirements of privacy regulations such as GDPR and the like are completely met, and the reliability of the system is improved. Compared with traditional privacy technologies such as homomorphic encryption, the method has the advantages that the calculation and communication overhead is greatly reduced, the design of local matching degree calculation is matched, the task matching response speed is greatly increased, and the real-time requirement of industrial control system testing is met.

The invention relates to a scene photographing recognition intelligent glasses system based on Bluetooth, and solves the problems of large response delay, low positioning precision and heavy equipment of traditional navigation equipment. The system comprises an intelligent glasses terminal, a local edge server and a cloud knowledge base. The glasses terminal is integrated with a miniature camera and a bone conduction loudspeaker, and a 128-bit visual feature vector is extracted by optimizing an ORB algorithm; a centimeter-level 3D scene map is pre-stored in the edge server, and a lightweight MobileNetV3 model is adopted to realize millisecond-level local matching; and the cloud generates personalized explanation based on the BERT model and the knowledge graph. The method has the technical effects that the indoor scene recognition accuracy is greater than 92%, the end-to-end response is less than 0.8 second, and the equipment cost is reduced to 1 / 5 of an AR scheme. The extended function supports barrier-free interaction and dialect protection, is suitable for scenes such as museums and scenic spots, and realizes integrated experience of cultural cognition and convenience service.

A system and method for generating synthetic high-frequency seismic data for subsurface imaging comprises injecting a first seismic shot having a low frequency into a surface region and receiving reflected waves at a plurality of seismic receivers. Seismic traces are recorded and processed to generate a low-frequency seismic shot gather. A second seismic shot having a high frequency is injected, and a sparse number of high-frequency traces are recorded. A computing device resamples the low-frequency traces to match the sparse high-frequency dataset and applies a one-dimensional (1D) local matching filter to transform the low-frequency traces into simulated high-frequency traces. The transformed dataset is used to generate a high-resolution subsurface image of geological interfaces. The system enables computationally efficient high-frequency seismic data synthesis, optimizing seismic inversion accuracy while reducing acquisition costs and computational overhead.

The present disclosure provides a kind of light node-oriented transaction verification method based on blockchain.The method does not depend on super node, through the process of transaction graph publishing-transaction propagation-transaction verification-transaction packaging chain, light node synchronizes DAG transaction graph, when light node carries out transaction verification judgment, first find the transaction that reaches verification condition, form the block to be verified with transaction digest, and publish to network;Light node received to the block to be verified carries out the judgment whether to pass according to the number of local matching consistency verification.The present application first filters the transaction that reaches verification condition by each light node local, then publishes the abstract to each light node for verification judgment, avoids the problem of large amount of calculation and network information interaction caused by each light node according to transaction content for verification judgment, both meets the demand of mobile device light weight storage, reduces the computing cost, and realizes data verification.

The invention relates to a laser equipment work order automatic identification method and system based on computer vision, and relates to the technical field of laser equipment work order visual identification, and the method comprises the steps: carrying out the model matching of a workpiece image based on a standard workpiece image library, and obtaining a workpiece calibration model; positioning identifier optimization is carried out based on the workpiece calibration model, and a recommended positioning image is obtained; the recommended positioning image is locally matched with the standard workpiece image of the calibration model, and the relative coordinates of the marking position and the recommended positioning image are obtained; performing local matching on the recommended positioning image and the workpiece image to obtain a selected marking position; and according to the recommended positioning image, the relative coordinates, the selected marking position and the workpiece image, laser equipment work order configuration is executed. The problems that when traditional laser equipment is used for marking, marking positions and marking sizes need to be manually set in a user-defined mode according to different workpieces, so that manual intervention is much, the intelligent degree is low, and the processing efficiency is low are solved.

The invention discloses a construction method and device for a mobile robot map and a storage medium, and belongs to the technical field of positioning and mapping. The construction method comprises the following steps: acquiring a current measurement point cloud and a current prediction pose of the mobile robot at a current moment; performing normal distribution transformation matching on the current measurement point cloud and a point cloud corresponding to the current prediction pose on a pre-constructed global voxel map to obtain a point cloud global matching residual error; performing iterative nearest point matching on the current measurement point cloud and a point cloud corresponding to a current prediction pose on a pre-constructed local dense map to obtain a current observation pose and a point cloud local matching residual error; determining a current actual pose according to the current observation pose, the current prediction pose and the point cloud local matching residual error, and obtaining a current actual point cloud matched with the current actual pose; and constructing a mobile robot map according to the current actual pose and the current actual point cloud. The method is used for improving the construction precision of the mobile robot map.

The invention relates to the technical field of semiconductor packaging, and provides a high-precision positioning method for multilayer leads. The method comprises the following steps: based on three-dimensional point cloud data and a two-dimensional image of a chip assembly after wire bonding is completed, carrying out noise reduction processing on the three-dimensional point cloud data; dividing the ROI by taking a connecting line between each pair of welding spots as a central axis, and dividing sub-ROIs in the ROI along the central axis direction; clustering the point clouds in the sub-ROIs according to a preset neighborhood size, and performing inter-class and intra-class noise reduction processing on clustering fragments according to lead features; a local matching score is constructed based on the height feature and the angle feature between the last lead segment of the lead combination and the lead segment to be matched, matching with too large difference is eliminated, and a strategy for searching the matched lead segment under the shielding condition is designed; obtaining an optimal combination by combining the local matching score and the global score; and fitting a target lead by using a Bezier curve. According to the invention, the multi-layer lead can be quickly and accurately positioned, and the robustness to noise is improved.

The invention relates to the technical field of computer vision and geographic information science, and discloses a remote sensing image surveying and mapping feature extraction system based on deep learning, during online operation, a query coding module in the system performs deep deconstruction and vector synthesis on a natural language instruction input by a user; the image coding module performs multi-scale coding on the target remote sensing image to generate a fine and macroscopic feature map; the preliminary matching module executes cross-modal semantic matching; the verification and fusion module is used for comparing the macroscopic environment characteristics of the candidate region with a core concept derived from query so as to effectively filter out misinformation of context inconformity; the module fuses local matching and macroscopic verification results, and drives the feature extraction module to generate high-confidence vectorized ground feature features. According to the system, accurate extraction of any complex semantics is realized, and the automation level and flexibility of remote sensing image feature extraction are remarkably improved.

The present application relates to the field of pipeline data processing, and particularly relates to a pipeline multi-attribute fusion screening method for a pipeline database. The present application acquires a target pipeline and a set of to-be-matched pipelines, determines a set of candidate pipelines associated with the spatial position of the target pipeline based on spatial indexing; normalizes the target pipeline and the candidate pipelines, constructs a pipeline trend development sequence based on pipeline connectivity, and divides the pipeline trend development sequence into a plurality of along-line segmentation units; analyzes the basic matching features of the along-line segmentation units, determines local matching differences, and sets matching state labels for the candidate pipelines based on the local matching differences; determines suspected deviation pipe sections according to the matching state labels, and performs adjacent and continuous re-checking on the suspected deviation pipe sections, or directly determines whether the candidate pipelines are the same pipelines corresponding to the target pipeline according to the overall matching result. The present application can reduce unnecessary overall matching calculation, and improve the pipeline screening efficiency and accuracy.

The application discloses a SM9 identification algorithm key generation method based on a physically unclonable function, and comprises the following steps: S1, based on a multipath effect, channel state information data acquisition, preprocessing and quantization coding are completed to obtain an encoding result containing a bit sequence; S2, a local sensitive hash algorithm is used to perform dimension reduction processing on the bit sequence in the encoding result, and a local matching pair is found and split hash is performed on the dimension-reduced bit sequence, so that a terminal device unique identifier is generated; and S3, wireless terminal public and private keys are generated according to the terminal device unique identifier and a national secret SM9 identification algorithm, which are used to realize digital signature and verification between a wireless terminal device and an access point. The application can effectively prevent an attacker from forging a user identity identifier to steal a key, and improves the reliability and security of a wireless communication system.

The application discloses a sewer network defect detection method based on multi-label zero-shot learning, and the method comprises the following steps: generating a defect description corresponding to each pipeline defect category through a large language model; using a representation guide module to perform feature extraction and field adaptation on a pipeline inner wall image and the defect description, so as to obtain global and local image features and defect detailed description text features; comprehensively considering the global and local image features of the image, respectively calculating global and local matching scores of the image and the detailed description text features, and fusing the global and local matching scores to obtain an initial defect prediction score; constructing a semantic relation adjacency matrix for displaying the relation between different defect categories; and using the semantic relation adjacency matrix between the categories to correct the initial prediction score, so as to obtain a final prediction score of each defect category. The method disclosed by the application realizes knowledge transfer from known defects to unknown defects by constructing a guide-fusion-correction network, and effectively solves the problem of recognizing unknown defect types.

The invention discloses a scene identification method and system based on vision-point cloud multi-modal fusion, and the method comprises the steps: collecting a visual image and laser radar point cloud data in a target scene, carrying out the preprocessing, and extracting corresponding visual image features and point cloud features; generating a global feature, a visual modal local feature and a point cloud modal local feature in the target scene; performing global retrieval in a preset scene database by utilizing the global features to obtain a plurality of candidate positions and corresponding global matching scores; performing local matching and geometric verification on the candidate positions based on the visual modal local features and the point cloud modal local features to obtain a local matching score of each candidate position; and reordering the candidate positions according to the global matching score and the local matching score, and outputting an optimal scene recognition result. The method solves the problems of poor feature alignment, insufficient modal fusion, weak complex scene adaptability and data redundancy in the existing multi-modal method.

The invention discloses a non-rigid shape registration method based on neural feature guidance. The non-rigid shape registration method comprises the steps of direction normalization, feature extraction, iterative registration optimization and spatial truncation spectrum embedding. The method comprises the following steps of: aligning input point cloud to a standard coordinate system through a pre-trained direction regression device, extracting high-dimensional features by utilizing a teacher-student normal form point feature extractor, realizing accurate alignment through dual-stage optimization in a feature space and a coordinate space, and processing a non-connected or local missing shape by inheriting a complete shape eigenbasis through spatial truncation spectrum embedding. According to the method, corresponding labeling is not needed, the problems of complex non-rigid deformation and incomplete shapes can be efficiently and accurately solved, the limitation of a traditional method on labeling data, large deformation and local matching is broken through, and the method has a wide application prospect.