14results about How to "Improve rebuild efficiency" patented technology

The invention provides a diffusion-driven channel adaptive point cloud semantic communication method, which belongs to the technical field of wireless communication, and comprises the following steps: obtaining a training point cloud data set, constructing a point cloud feature extraction network based on a hypergraph convolutional neural network as a semantic encoder, and constructing a channel adaptive enhancement module as a channel decoder, a channel adaptive recovery module is constructed at a receiving end as a channel decoder, a diffusion reconstruction network is constructed as a semantic decoder, end-to-end joint training is performed on the point cloud feature extraction network, the channel adaptive enhancement module, the channel adaptive recovery module and the diffusion reconstruction network, the diffusion reconstruction network predicts original point cloud distribution, and the point cloud feature extraction network and the channel adaptive enhancement module are subjected to end-to-end joint training. And a loss function is constructed based on the chamfering distance between the predicted point cloud distribution and the original point cloud. In the communication process, the reconstructed semantic features are input into the diffusion reconstruction network to reconstruct an original point cloud structure. The high-order semantic features of the point cloud can be effectively extracted so as to improve the adaptive capacity of the method to the incomplete point cloud.

The application relates to a fingerprint library reconstruction method and device, equipment, a readable storage medium and a program product, and relates to the technical field of data recovery backup. The method comprises the following steps: reading the head part of a data file; acquiring the head part offset of each data block in the data file according to a bit set carried in the head part of the data file; reading the head part of each data block in parallel according to the head part offset of each data block, and obtaining the data block fingerprint and metadata carried in the head part of each data block; and inserting the data block fingerprint and metadata corresponding to each data block into a fingerprint library. The method can reduce the reading of invalid data and read the head part of each data block in parallel, so as to improve the fingerprint library reconstruction efficiency.

This invention discloses a rapid reconstruction method for full-scene highway reconstruction based on multi-source parallax control and adaptive explicit radiance field, belonging to the field of 3D reconstruction technology. It includes: acquiring multi-source image data and calculating camera pose using differentiated strategies for scenes such as highways, bridges, and tunnels; representing the scene with an anisotropic set of 3D Gaussian spheres, and achieving accurate 3D-to-2D mapping through the covariance projection formula; iteratively optimizing Gaussian sphere properties using an adaptive density control strategy based on gradient threshold and volume constraints, automatically cloning / splitting in detail regions to improve accuracy; and achieving real-time rendering of over 60 FPS using tile-based parallel cardinality sorting and an alpha mixing formula. This method solves the problems of high cost, near-field blurring, disconnect between tunnel interiors and exteriors, and difficulty in real-time interaction associated with traditional methods, achieving rapid modeling and incremental updates within 24 hours, significantly improving reconstruction efficiency, detail fidelity, and user experience.

This invention discloses a three-dimensional detection method and system for steel plate edge defects based on full-matrix capture-full-focus imaging, belonging to the field of ultrasonic non-destructive testing technology. The method includes: acquiring raw waveform data and encoder position information using full-matrix capture (FMC); performing three-dimensional reconstruction calculations using the full-focus imaging (TFM) method to obtain three-dimensional volume data; binarizing the three-dimensional volume data according to set edge geometric boundary conditions and generating a three-dimensional edge mask to extract candidate edge defect regions; calculating the three-dimensional defect volume and two-dimensional projected area of ​​the candidate edge defect regions and obtaining the ratio data; triggering an upgrade operation for the evaluation level of the candidate edge defect regions when the ratio data exceeds a set threshold, and outputting the evaluation result including the edge defect. This invention effectively eliminates the detection blind spots of traditional scanning of steel plate edge regions, truly reflects the three-dimensional spatial distribution of defects in the length, width, and thickness directions of the plate, and achieves highly reliable three-dimensional detection and quantitative evaluation of steel plate edge defects.

This invention provides a calibration-free path reconstruction method for buried cables based on Topo-NeRF and active sensing. Addressing the issues of traditional magnetic field inversion relying on calibration and being prone to mismatch in environments with strong interference such as reinforced concrete, the method preprocesses raw data to obtain a spatiotemporal feature matrix. This matrix undergoes feature extraction, spatial alignment, and drift compensation to eliminate extrinsic parameter calibration, resulting in multi-source fused data. This multi-source fused data is combined to characterize the cable's magnetic field using a continuous three-dimensional topological manifold. An implicit neural radiation field is constructed and coupled with differentiable electromagnetic rendering. The cable body and branch nodes are separated within a persistently cohomologically constrained topological bottleneck layer. The initial reconstruction results are combined with conditional diffusion and ground-penetrating radar dielectric priors to enhance low signal-to-noise ratio weak fields, ensuring consistency between the reconstruction results and the underground physical structure. Based on reconstruction uncertainties, topological entropy is calculated, and online acquisition trajectories are planned to form a closed-loop active sensing system. Finally, a three-dimensional path model is output, enabling rapid detection of underground cables.

This disclosure provides a method, apparatus, electronic device, storage medium, and product for soft body asset reconstruction. The method includes: acquiring multi-view videos of a target soft body; extracting features from the first frame of each video using an aggregator in a feedforward neural network to obtain first image features of the first frame; predicting point cloud data of the target soft body using a first prediction network in the feedforward neural network based on the first image features of each first frame; obtaining a digital model of the target soft body using a second prediction network in the feedforward neural network based on the first image features and point cloud data of each first frame; extracting features from each video using the aggregator to obtain motion state features corresponding to each video; and predicting the physical parameters of the target soft body using a third prediction network in the feedforward neural network based on the motion state features corresponding to each video. This improves the efficiency and accuracy of soft body asset reconstruction.

The invention discloses a road full-scene rapid reconstruction method based on multi-source parallax control and an adaptive explicit radiation field, and relates to the technical field of three-dimensional reconstruction. Comprising the following steps: collecting multi-source image data by adopting a differentiation strategy aiming at scenes such as roads, bridges and tunnels, and resolving camera poses; the scene is represented by an anisotropic three-dimensional Gaussian ball set, and accurate mapping from three dimensions to two dimensions is realized through a covariance projection formula; gaussian ball attributes are iteratively optimized based on an adaptive density control strategy of a gradient threshold and volume constraint, and automatic cloning / splitting is carried out in a detail area to improve precision; and real-time rendering of more than 60FPS is realized by adopting parallel cardinal number sorting based on image blocks and an Alpha mixed formula. The problems that a traditional method is high in cost, the close shot is fuzzy, the interior and exterior of the tunnel are disjointed, and real-time interaction is difficult are solved, rapid modeling and incremental updating within 24 hours are achieved, and the reconstruction efficiency, the detail fidelity and the user experience are remarkably improved.

The application relates to a crowd-sourced unordered image auxiliary-based urban scene reconstruction method and device and a storage medium, wherein the method comprises the following steps: acquiring laser radar data to form basic point cloud data; acquiring crowd-sourced unordered images, estimating a depth map by using a depth estimation network, and obtaining auxiliary point cloud data; fusing the basic point cloud data and the auxiliary point cloud data to obtain geometric information; acquiring multispectral images and panchromatic images obtained by satellite remote sensing, determining ground object color information based on the multispectral images, and determining a ground surface texture model and a ground object model based on the panchromatic images; performing data preprocessing on the crowd-sourced unordered images to obtain a texture image, combining the ground object model and the ground object color information to obtain a ground object texture model; determining texture information based on the ground object texture model and the ground surface texture model; and realizing urban scene reconstruction by using an automatic reconstruction solution based on the geometric information and the texture information. Compared with the prior art, the application has the advantages of high restoration degree and high reconstruction precision.

The application discloses a kind of object three-dimensional reconstruction method and device based on deep learning, it is related to three-dimensional reconstruction technical field, wherein, the reconstruction method includes: using camera imaging equipment to collect target object video of target object, and based on target object video, determine target image set, based on target image set, determine target camera parameter and initial camera pose, and correct initial camera pose, obtain target camera pose, input target image set, target camera parameter and target camera pose to preset neural radiance field model, output target radiance field information, based on target radiance field information, construct the object three-dimensional model of target object.The application solves the technical problem that the accuracy of three-dimensional reconstruction of object in related art is low.

The application discloses a self-adaptive 4D Gaussian splashing high-precision three-dimensional reconstruction system and method, and belongs to the technical field of computer graphics processing. In order to solve the problems of realizing high-precision automatic registration of multi-source heterogeneous data and improving the calculation efficiency, the application comprises the following steps: collecting multi-source heterogeneous data; constructing a multi-modal fusion registration method, including realizing spatial distribution geometric rough registration by combining satellite image data and low-altitude oblique photography data, realizing photometric fine registration by fusing low-altitude laser radar point cloud data and ground acquisition vehicle laser radar point cloud data, and establishing semantic feature auxiliary registration to obtain registered multi-source data; initializing a 4D Gaussian primitive and performing self-adaptive splashing reconstruction to obtain an optimized 4D Gaussian splashing model; designing a cloud-edge collaborative computing architecture for the 4D Gaussian splashing reconstruction, performing distributed parallel processing on the obtained optimized 4D Gaussian splashing model; performing quality evaluation and self-adaptive optimization, and outputting a final self-adaptive 4D Gaussian splashing model.

The invention belongs to the technical field of infrared thermal wave imaging, and particularly relates to a blind structure polarization modulation infrared thermal wave super-resolution imaging detection system and method, and the system comprises an infrared camera, a processor, a signal generator, a power amplifier, a semiconductor laser, and a structured light modulator. A super-resolution detection image is reconstructed through instantaneous phase difference images of thermal wave signals and linear frequency modulation signals under different spatial polarization phases, and the spatial resolution of infrared thermal wave imaging for detecting tiny defects and complex edge defects is improved.

This invention provides a method and storage medium for 3D reconstruction of petrochemical pipelines based on multi-view optimization, relating to the fields of petrochemical pipeline inspection and 3D reconstruction. The method involves acquiring multiple pipeline images around the target petrochemical pipeline to construct a multi-view image set; preprocessing the multi-view image set; extracting features from each image to obtain a feature point set and feature descriptors; performing initial matching to obtain initial feature matching pairs; optimizing the process to obtain the optimal feature matching pairs; calibrating camera parameters to eliminate lens distortion and accumulated errors; generating a sparse point cloud using an improved motion recovery structure algorithm; removing redundant and noise points to obtain an optimized sparse point cloud; generating a dense point cloud using a dense reconstruction algorithm; optimizing the process to obtain an optimized dense point cloud; constructing an initial 3D model; and post-processing to obtain the final 3D model of the target petrochemical pipeline; and storing the data on a storage medium. This invention provides reliable 3D model support for the digital management, fault detection, maintenance, and repair of petrochemical pipelines.

The application discloses a kind of based on quadruped robot and machine vision's building model reconstruction method and system, based on the quadruped robot of laser scanner, utilize simultaneous localization and map construction technology to generate the global map of the region to be reconstructed.Combining three-dimensional ray tracing and A* path search algorithm determines target scanning point and carries out global path planning.Based on the high-precision sensor of quadruped robot, through adaptive monte carlo positioning and improved dynamic window method algorithm, to realize the accurate positioning and obstacle avoidance of quadruped robot in complex environment.After moving to target scanning point, robot stops and starts laser scanner, carries out high-precision point cloud data acquisition, until completing all sampling.After the preprocessing of the collected point cloud data, through point cloud registration and poisson surface reconstruction technology, generate three-dimensional building model.The application can realize comprehensive, efficient and accurate three-dimensional reconstruction in complex building environment.

This application provides a method for recovering from connection-state synchronization loss in a satellite network and a user terminal. The method includes: after detecting downlink synchronization loss, the user terminal acquires a first received signal strength before synchronization loss and a second received signal strength at the time of synchronization loss; the user terminal determines the signal change direction and signal change rate based on the first and second received signal strengths; the user terminal adjusts the runtime of a T310 timer based on the combination of the signal change direction and signal change rate; if the user terminal still fails to restore synchronization after the T310 timer expires, the user terminal adjusts the runtime of a T311 timer based on the combination of the signal change direction and signal change rate, and performs beam search and RRC connection reconstruction during the adjusted T311 timer runtime. This application improves the recovery efficiency and reconstruction success rate after connection-state synchronization loss by adjusting the T310 and T311 timers according to different scenarios.