45results about How to "Improve rebuild efficiency" patented technology

The invention provides a fast magnetic resonance reconstruction method and system based on a multistage wavelet convolution network, and particularly relates to the technical field of magnetic resonance imaging and image reconstruction, and the scheme comprises the steps: collecting multi-channel under-sampling K space data; inverse Fourier transform is carried out on the multi-channel under-sampling K space data to obtain an under-sampling magnetic resonance image; and inputting the undersampled magnetic resonance image into a trained multi-stage wavelet convolutional network for reconstruction to obtain a reconstructed magnetic resonance image, the multi-stage wavelet convolutional network being constructed based on multi-stage wavelet transform and a convolutional neural network. According to the scheme, the magnetic resonance K space data is collected in an undersampling mode, the scanning time can be shortened, the multi-stage wavelet convolution network can effectively reserve high-frequency details of the image in the reconstruction process, and therefore the reconstruction quality and efficiency of the multi-channel magnetic resonance image can be remarkably improved on the whole.

The invention discloses a three-dimensional scene reconstruction method and device, and belongs to the field of computer vision and image processing. The method comprises: determining spatial geometric information of a shooting scene according to M two-dimensional images, the M two-dimensional images being images of the shooting scene at different shooting angles, the spatial geometric information at least comprising: at least one target spatial point and at least one spatial line segment, M being an integer greater than 1; and according to the spatial geometric information, a three-dimensional Gaussian ellipsoid set is generated, and the three-dimensional Gaussian ellipsoid set comprises at least one three-dimensional Gaussian ellipsoid used for representing three-dimensional scene information of the shooting scene.

The invention discloses an object three-dimensional reconstruction method and device based on deep learning, and relates to the technical field of three-dimensional reconstruction, and the reconstruction method comprises the steps: collecting a target object video of a target object through a camera imaging device, determining a target image set based on the target object video, and obtaining a target image set based on the target image set; the method comprises the steps of obtaining a target image set, determining target camera parameters and an initial camera pose, correcting the initial camera pose to obtain a target camera pose, inputting the target image set, the target camera parameters and the target camera pose to a preset neural radiation field model, outputting target radiation field information, and constructing an object three-dimensional model of a target object based on the target radiation field information. According to the invention, the technical problem of low accuracy of three-dimensional reconstruction of an object in the prior art is solved.

The embodiment of the invention discloses an ultrasonic harmonic image generation method and device, electronic equipment and a medium. A specific embodiment of the method comprises the following steps: controlling an ultrasonic transmitting probe to transmit a low-frequency ultra-harmonic signal; controlling an ultrasonic receiving probe to receive the echo signal set to be detected; carrying out compressed sampling processing on the echo signal set to be detected and then carrying out signal reconstruction to obtain a reconstructed echo signal set; carrying out delay superposition processing on the reconstructed echo signal set to obtain a superposed echo signal; performing signal filtering decomposition processing on the superposed echo signals to obtain a super-harmonic signal set; performing envelope detection on the super-harmonic signal set to obtain an echo amplitude information set; and generating a target area ultrasonic harmonic image set, and sending the target area ultrasonic harmonic image set to a target terminal for display. According to the embodiment, by transmitting the low-frequency signal and receiving the ultra-harmonic signal, the attenuation of the received signal can be reduced, the signal penetration depth can be improved, the purity of the received signal can be improved, and the quality and the resolution of the generated ultrasonic harmonic image can be improved.

The invention relates to the technical field of video processing, in particular to a video key frame extraction method and system based on shot segmentation and a VGG neural network, and the method comprises the steps: firstly, extracting LBP-HF features and Canny features of continuous video frames, and obtaining shot boundary frames; then calculating the inter-frame similarity of boundary frames of adjacent shots, and dividing each shot into an active area or an inactive area by setting a threshold value; selecting a shot boundary frame as a coarse extraction key frame set for an inactive area; for the active area, sequentially inputting the video frames in the shot into a VGG19 convolutional neural network for feature vector description, extracting image depth features, calculating the similarity between the video frames, setting a threshold value to determine key frames, and adding the obtained key frames of the active area into a coarse extraction key frame set; and finally, removing key frames with high similarity in the roughly extracted key frame set to obtain a final video key frame set. According to the method, the key frame extraction efficiency and accuracy are effectively improved.

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 invention provides a reference marking system, a three-dimensional reconstruction method and electronic equipment, and relates to the technical field of three-dimensional reconstruction. The marking assembly comprises at least four non-coplanar markers with different target features, and the target features of the markers have projection stability; the marking assembly has full-view projection specificity, and two-dimensional projection images of the marking assembly are in one-to-one correspondence with three-dimensional space postures of the marking assembly. The reference marking system is suitable for X-ray imaging and natural light imaging, and can realize accurate identification of projection positions of all markers, so that accurate spatial position and attitude information can be provided for three-dimensional reconstruction, high-precision three-dimensional reconstruction can be performed from a single or sparse angle, and the spatial positioning precision and the reconstruction efficiency are improved.

The invention relates to a dot matrix auxiliary focusing digital holographic microscope system and a holographic reconstruction method, the system comprises a digital holographic microscopy light path part and a dot matrix auxiliary focusing light path part, the digital holographic microscopy light path part adopts polarization modulated narrow-band laser to irradiate an object sample to be measured to become object light; the reference light which is orthogonal with the polarization reflected by the reference mirror is subjected to interference imaging in the polarization camera, so that different phase shift acquisition of a single image is realized; the dot matrix auxiliary focusing light path part calculates the focus position of the object sample to be measured by using the definition of the image, and the optimal inverse diffraction distance required in the digital holographic reconstruction process can be directly obtained. Compared with the prior art, the method has the advantages that the automatic focusing efficiency of the digital holographic microscope is improved, the digital holographic three-dimensional surface reconstruction speed is greatly increased, and the reconstruction efficiency and real-time performance are improved on the premise that the reconstruction measurement precision is guaranteed.

The invention discloses an outdoor scene-oriented efficient multi-view three-dimensional reconstruction method and system, and belongs to the field of multi-view three-dimensional reconstruction, and the method specifically comprises the steps: preparing a multi-view three-dimensional reconstruction data set for model training; constructing a multi-view three-dimensional reconstruction network comprising an adaptive feature extraction converter module based on big kernel attention, a cost regularization module and a depth estimation module; performing training and fine tuning on the network model to obtain a final model; building a multi-view three-dimensional reconstruction system and equipment; according to the method, the problems of poor accuracy and low integrity of a reconstruction result when an existing multi-view three-dimensional reconstruction method faces an outdoor large-scale scene can be solved, and the training reasoning time and the calculation cost of multi-view three-dimensional reconstruction can be reduced; and an important foundation is laid for application of outdoor large-scale scene image data in the field of multi-view three-dimensional reconstruction and development of a three-dimensional reconstruction technology.

The invention belongs to the technical field of data processing, and discloses a transformer polymerization degree distribution visualization method, system and device and a medium, and the method comprises the steps: generating an internal temperature field distribution simulation result of a transformer model through simulation software, and outputting transformer polymerization degree distribution data under different temperature changes; performing mesh generation on the transformer simulation model to obtain a triangular mesh model, and mapping the triangular mesh model to a plane parameter domain; selecting parameter points in the plane parameter domain, and generating corresponding data sampling points of the parameter points in the triangular mesh model by using an inverse mapping technology; and performing curved surface interpolation fitting on the data sampling points to obtain a curved surface fitting result, and performing visualization processing on the curved surface fitting result. According to the method, the sensor simulation model is subdivided to obtain the triangular mesh model, a complex model can be converted into a model with a small number of vertexes and a single boundary, the operation complexity of the fitting process is simplified, and the curved surface fitting efficiency is improved.

The invention provides a three-dimensional image reconstruction method and device, and belongs to the technical field of image processing, and the method comprises the steps: obtaining first point cloud data and second point cloud data; wherein the sources of the first point cloud data and the second point cloud data are different; determining a target registration demand based on the first point cloud data and the second point cloud data; a target registration strategy is determined from a plurality of point cloud registration strategies based on the target registration requirement, and the point cloud registration precisions of the plurality of point cloud registration strategies are different; registering the first point cloud data and the second point cloud data based on a target registration strategy to obtain point cloud registration data; and performing rendering processing on the point cloud registration data to obtain a three-dimensional image model. According to the three-dimensional image reconstruction method and device provided by the invention, the efficiency and accuracy of three-dimensional image reconstruction can be improved.

This application relates to the field of data processing technology, and in particular to a method, apparatus, device, and storage medium for model training and 3D mesh reconstruction, used to rapidly generate high-quality 3D results. The model training method includes: obtaining an initial repair model by distilling a student model to be trained; the initial repair model is used to make the input initial sample set, after M iterative repairs, present the shape of a 3D point cloud corresponding to the initial sample set; obtaining an intermediate repair model by distilling the initial repair model; the intermediate repair model is used to make the input intermediate sample set, after N iterative repairs, present the shape of a 3D point cloud corresponding to the intermediate sample set; and obtaining a target repair model by adversarial training of the intermediate repair model based on the initial repair model, so that the error between the results obtained by repairing the same target sample set using the target repair model and the initial repair model conforms to a preset error range.

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.

The application discloses a method and system for selecting a first reference image of MVS dense reconstruction, and belongs to the field of three-dimensional reconstruction, which comprises the following steps: inputting source image data; calculating the incidence angle of the source image; in each loop, calculating the incidence angle of other source images relative to the current source image, and obtaining the number of other source images within a certain threshold relative to the incidence angle of each source image; normalizing the number of other source images corresponding to each source image to obtain a first parameter for measuring the index; performing feature detection on all source images; performing feature matching and recording the total sum of feature point matching pairs of each source image; normalizing the total sum of feature point matching pairs of each source image to obtain a second parameter for measuring the index; calculating the product of the first parameter and the second parameter of each source image, and selecting the source image with the largest product result as the first reference image. The application can obtain a first reference image that can reconstruct a larger number of three-dimensional points, thereby obtaining a more accurate reconstruction in MVS and improving the efficiency of reconstruction.

The present application provides a method for reconstructing a three-dimensional model of a remanufactured blank based on deep learning, the method comprising: collecting images of the remanufactured blank according to pre-set acquisition parameters; generating sparse point cloud data of the remanufactured blank based on the images; extracting features of the remanufactured blank for each image to obtain feature images of the remanufactured blank of different sizes; generating feature-enhanced point cloud data based on the sparse point cloud data and feature images, and optimizing the feature-enhanced point cloud data using a deep learning network to obtain the optimal feature-enhanced point cloud data; generating a three-dimensional model of the remanufactured blank based on the optimal feature-enhanced point cloud data. The method achieves the improvement of the three-dimensional reconstruction accuracy and reconstruction efficiency of the remanufactured blank by pre-setting acquisition parameters and optimizing the feature-enhanced point cloud data using a deep learning network.

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.

The present application relates to a magnetic resonance (MR) imaging method and system, the method comprising: acquiring imaging data of an object collected by an MR imaging device within a scanning time; determining multiple reference images corresponding to multiple times within the scanning time based on at least part of the imaging data; determining time information of a dynamic image corresponding to the object within the scanning time based on the multiple reference images; determining spatial information of the dynamic image based on the time information and the imaging data; and reconstructing the dynamic image based on the time information and the spatial information.

The present application provides a 3D scene reconstruction method, device, electronic device and computer-readable medium, which relates to the field of computer vision technology, including: acquiring image data of a vehicle traveling in a target scene, performing sparse reconstruction using the image data to obtain a sparse point cloud of the target scene, and densely reconstructing the sparse point cloud through a pre-trained 3D Gaussian distribution representation model to obtain a first 3D scene reconstruction result, wherein the first 3D scene reconstruction result includes multiple first Gaussian distribution point clouds, the 3D Gaussian distribution representation model is used for dense reconstruction based on a 3D Gaussian splash algorithm, and the training samples of the 3D Gaussian distribution representation model are multiple real ground images and the sparse point clouds corresponding to each of the multiple real ground images. While improving the reconstruction efficiency, the weak texture area contained in the sparse point cloud obtained by sparse reconstruction is densely reconstructed to improve the accuracy of 3D scene reconstruction.

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.

The present invention discloses a method, device and readable storage medium for reconstructing a dense three-dimensional model. The method for reconstructing a dense three-dimensional model includes the following steps: S110, obtaining an input image, and extracting a multi-scale feature map in the input image; S120, performing a differentiable homography transformation operation on the multi-scale feature map to deform the multi-scale feature map into a corresponding feature body; S130, performing multi-level depth estimation on the feature body to obtain a target depth map of the same size as the input image; and S140, fusing the target depth map into a point cloud to reconstruct a dense three-dimensional model of the input image. The technical solution of the present invention can reconstruct a dense three-dimensional model of an input image, and at the same time has the advantages of reducing the complexity of image calculation, requiring less GPU resources, saving costs, having a simple structure, and having high reconstruction efficiency.

The present invention discloses a sparse viewing angle X-ray image three-dimensional reconstruction method based on hybrid features. The method uses gradient values ​​as weights to guide sampling rays so that the sampling rays are distributed in areas with more obvious changes as much as possible. Three-plane decomposition features and hash coding features are mixed, and the hybrid features are sent to an attenuation field prediction network to predict attenuation values. An attenuation weight prediction network (W‑NAF) is used to predict the attenuation weight of each sampling pixel point on the X-ray path. The attenuation value and attenuation weight value at the sampling pixel point are obtained through the two networks. The predicted projection pixel value is generated by accumulating the attenuation values ​​along the sampling rays, and then the network is optimized by minimizing the difference between the predicted projection and the real X-ray image.

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.

The present invention provides a storage device and a control method thereof, wherein the storage device comprises: a backup module, which uses the copy data of the host data as the backup data; a flash memory chip, wherein some storage blocks in the flash memory chip are divided into a table block, a backup block and a register block, wherein the backup data is stored in the backup block, the address mapping information of the host data and the backup information of the backup data are stored in the table block, wherein the register block stores the reconstruction identification data; a normal power-off reconstruction module, wherein when the storage device receives the power-off instruction from the host computer, the normal power-off reconstruction module scans the backup block according to the backup information and rebuilds the backup information; and an abnormal power-off reconstruction module, wherein when the storage device abnormally powers off, the abnormal power-off reconstruction module scans the backup block according to the reconstruction identification data and rebuilds the backup information. The storage device of the present invention has high storage reliability.

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 invention discloses a flame three-dimensional shape reconstruction method and system based on physical constraint and electronic equipment, and relates to the technical field of flame shape reconstruction, and the method comprises the steps: calculating a visual boundary of a target flame in a three-dimensional space according to a plurality of flame images of the target flame at a target moment, and calculating the visual boundary of the target flame; constructing a radiation transfer equation set facing all pixels in each flame image of the target flame at the target moment, and performing adaptive adjustment iteration solution on the radiation transfer equation set to obtain three-dimensional distribution data of the radiation intensity at the target moment until three-dimensional distribution data of the radiation intensity at each preset moment is obtained; and constructing a three-dimensional shape of the target flame according to the three-dimensional distribution data of the radiation intensity at each preset moment. According to the method, the number of unknown quantities can be reduced, the calculation amount can be reduced, the convergence speed of iterative calculation can be increased, and the calculation efficiency and accuracy of solution can be improved through visual boundary demarcation, adaptive adjustment of iterative solution and time sequence information transmission.

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 present disclosure provides a method and device for wireless resource control (RRC) connection reconstruction, a storage medium and an electronic device; and relates to the field of communication technology. The method includes: receiving an RRC reconstruction request message sent by a user equipment (UE); searching for corresponding UE context information based on the RRC reconstruction request message; when the UE context information is not found, sending a recovery UE context request to the corresponding source base station based on the physical cell identifier and the UE context identifier, so that the source base station obtains the UE context information negotiated in the initial RRC establishment phase based on the UE context identifier and responds to the recovery UE context request; receiving the UE context information, and responding to the RRC reconstruction message to the UE, so that the UE completes the RRC connection reconstruction. The present disclosure can solve the problems of high failure rate of the RRC reconstruction process across base stations and VoNR service disconnection in the prior art.

According to the method, a multi-cost body aggregation module is combined with a D-RFP network structure, a novel MHM model of deep separator convolution is added into the D-RFP network structure, and the D-RFP performs feature extraction and fusion by using a feature extraction network of an FPN structure for multiple times, so that the expression ability of the extracted features is enhanced; in addition, depth separable convolution is added on the basis of a conventional RFP network, and the operation can effectively reduce the parameter quantity of convolution operation, so that the memory requirement of a running model is reduced. According to the multi-cost aggregation module, the feature bodies are aggregated firstly, then the cost bodies are constructed according to the view angles one by one, then the final cost body is formed through aggregation, the similarity calculation mode of the related features provides effective cost body representation, excessive information loss is avoided, and therefore the precision of the whole network is improved. According to the model process, firstly, a feature body is obtained through a D-RFP structure, then, a cost body is constructed by using a multi-cost body aggregation module, then, an initial depth map is obtained through 3D convolution regularization, and finally, a network is refined through the depth map, and deep fusion is carried out to generate a final three-dimensional point cloud.