1050 results about "Point cloud segmentation" patented technology

The invention discloses a multi-view panoramic point cloud splicing method, which comprises the steps of performing joint calibration on a binocular camera and a laser radar through a calibration algorithm, and aligning a coordinate system; a binocular camera is used for collecting a two-dimensional image and carrying out distortion correction, a depth map is generated based on parallax calculation, and three-dimensional point cloud reconstruction is carried out in combination with a triangulation principle; performing deep learning target detection on the two-dimensional image, and screening a line rod assembly area through non-maximum suppression; multi-view point cloud data are collected through a laser radar, point cloud segmentation processing is carried out, and a telegraph pole assembly point cloud subset is reserved; matching the two-dimensional pixel area of the telegraph pole assembly with a laser radar point cloud projection result, and screening point cloud data belonging to the telegraph pole assembly; and carrying out alignment and fusion on the multi-view point clouds through initial registration and fine registration by adopting an improved point cloud splicing algorithm to generate a panoramic point cloud image. According to the method, the point cloud splicing processing time is shortened, and the robustness, precision and integrity of point cloud splicing are improved.

A three-dimensional point cloud semantic segmentation method based on graph convolution and a grouping vector attention mechanism adopts a network structure of a U-Net style, feature extraction and propagation are performed on point cloud through multi-level feature coding and decoding, each level firstly centralizes and standardizes local neighborhood features through a geometric affine module, and then the local neighborhood features are subjected to feature extraction and propagation; the capturing capability of the model on the diversity of the local region is enhanced; secondly, constructing a local neighborhood graph, and designing an asymmetric edge feature learning function to extract local topological information; deep edge features are further extracted in combination with a residual network; and finally, selectively fusing local features and global context information under the condition of low parameter quantity by using a grouping vector attention mechanism, and performing cross-level feature interpolation up-sampling to an original scale to complete point cloud segmentation. The method can significantly improve the point cloud segmentation precision, and is suitable for the perception of a mobile robot and other devices carrying a three-dimensional sensor on a complex scene.

A three-dimensional modeling method and apparatus, which relate to the technical field of modeling, and aim to implement an automated three-dimensional scene modeling task, thereby reducing the modeling duration and the modeling cost. The embodiments of the present application involve: after three-dimensional point cloud data is reconstructed on the basis of collected images, on the basis of the three-dimensional point cloud data, segmenting a scene to be modeled, so as to obtain a point cloud feature of a target object; then obtaining a mask feature map of the target object on the basis of the images; and registering the point cloud feature and mask feature map of the target object in a three-dimensional space, so as to obtain geometric structure parameters of the target object. Instead of manually selecting points from a cloud point feature, more accurate geometric structure parameters of the target object are obtained by means of registering a point cloud feature of a target object that is obtained by means of point cloud segmentation and a mask feature map of the target object that is obtained by means of image segmentation, without the need for manual intervention, such that the automation rate of modeling operations is increased, thereby reducing the modeling duration and the labor cost consumed by modeling.

A three-dimensional point cloud segmentation method and apparatus based on a locally weighted curvature and a two-point method, which method and apparatus relate to the field of point cloud processing. The segmentation method comprises: calculating a normal vector of each sample point in a three-dimensional point cloud image (S101); calculating a locally weighted curvature of each sample point on the basis of the normal vector, and comparing the locally weighted curvature with a preset curvature threshold value, so as to obtain a comparison result (S102); when the comparison result indicates that the locally weighted curvature of the sample point is greater than or equal to the preset curvature threshold value, marking the sample point as a boundary feature point, so as to obtain a boundary feature point set (S103); fitting a point cloud planar model on the basis of the boundary feature point set, and performing iterative calculation on the point cloud planar model, so as to obtain a target inner point set (S104); and segmenting the three-dimensional point cloud image on the basis of the boundary feature point set and the target inner point set (S105).

An illustrative point cloud segmentation system generates an instance-wise semantic mask for a particular source image of a set of source images that has been used to construct a 3D point cloud representing a scene that includes one or more objects. The point cloud segmentation system maps a set of 3D points from the 3D point cloud to corresponding 2D points of the particular source image, then labels, based on contours defined by the instance-wise semantic mask to demarcate the one or more objects, the mapped set of 3D points in accordance with where the corresponding 2D point for each mapped 3D point is positioned with respect to the contours. Based on the labeling of the mapped 3D points, the point cloud segmentation system produces a segmented 3D point cloud including an instance-wise segmentation of the one or more objects at the scene. Corresponding methods and systems are also disclosed.

The invention relates to the technical field of bridge support damage identification, in particular to a bridge crack time sequence evolution analysis method and system based on dynamic point cloud registration, and the method comprises the steps: synchronously collecting the three-dimensional original point cloud data of a target region where a to-be-detected bridge support is located through a plurality of laser radar sensors; on the basis of the three-dimensional original point cloud data, preprocessing the point cloud data, and extracting a region of interest of the to-be-detected bridge support; constructing a plurality of local geometric features of the point cloud based on an improved multi-point geometric operator; predicting label categories of the points based on a graph attention network; obtaining a segmentation result of each part of the support by using a hierarchical segmentation strategy; according to the method, geometric analysis is carried out on each part of the support, a support part segmentation result and a support damage evaluation result are output, and a hierarchical point cloud segmentation strategy can effectively consider the overall structure and local details of the support, so that the segmentation accuracy is improved, and a more reliable basis is provided for subsequent damage identification.

The present application discloses a point cloud segmentation method and apparatus, a storage medium, and an electronic device. The method comprises: for a target sample point in a spatial point cloud, determining a curvature feature of a local plane corresponding to the target sample point in the spatial point cloud; on the basis of the curvature feature, determining a local density corresponding to the local plane; on the basis of candidate relative distance values between a plurality of other sample points and the target sample point, determining a target relative distance value corresponding to the target sample point, wherein the plurality of other sample points are sample points other than the target sample point in the spatial point cloud; and on the basis of the local density and the target relative distance value, segmenting the spatial point cloud to obtain a spatial point cloud segmentation result.

The invention discloses a point cloud classification and segmentation method based on a point cloud channel attention feature fusion mechanism, and the method builds a deep learning model, comprises a down-sampling module which carries out the re-sampling of points through a farthest point sampling algorithm, employs K neighbors for each sampling point, and carries out the segmentation of the points. Aggregation is carried out through maximum pooling, and local points are converted into normal distribution through a geometric mapping module; a double-branch input structure is constructed, one branch inputs a multi-layer perceptron layer with residual connection to perform weight learning on neighborhood features of each point of a point cloud, the other branch inputs a multi-scale channel attention fusion module, and channel attention is extracted through two branches with different scales. A multi-scale channel attention mechanism model is designed, local and global features are combined, and an attention mechanism is adopted in a feature space to fuse multi-scale information of point cloud features. According to the method, a channel attention fusion model is designed to solve the problem that the capability of capturing a remote dependency relationship by using convolution in a traditional point cloud classification and segmentation task is limited, a double-branch input network structure is adopted in a feature extraction stage, and dimension reduction operation is performed on a channel during input; the method has the advantages that the accuracy of classification and segmentation tasks can be improved, and meanwhile, the calculation amount is reduced. The method has great advantages in point cloud classification and point cloud segmentation application tasks.

The invention discloses a high-precision granary volume calculation method and system, and relates to the technical field of laser radar technology and point cloud processing, and the method comprises the following steps: data collection; laser radar equipment is used for carrying out omnibearing scanning on a target object, and a laser point cloud data set is obtained; preprocessing the data; performing point cloud segmentation; object recognition; point cloud precision compensation: performing precision compensation on the point cloud data, the precision compensation including error compensation of laser radar precision along with distance and temperature changes; volume calculation: performing volume calculation on the recognized regular object by adopting a volume calculation formula; for the recognized irregular object, a three-dimensional grid model of the object is created through a three-dimensional model reconstruction algorithm, the grid model is adjusted, and then the volume of the grid model is calculated; according to the method, optimization is carried out from the two aspects of radar distance and temperature compensation, an error model is established, in the model, primary functions are designed from the two aspects of temperature and distance, and the precision is improved from the two aspects at the same time.

The invention discloses a point cloud segmentation method and system based on two channels, and relates to the technical field of computer vision and artificial intelligence, and the method comprises the steps: collecting point cloud data; creating a feature channel according to the three-dimensional point cloud data; and performing feature fusion and classification according to the feature channel. According to the point cloud segmentation method based on the two channels, different types of features are processed in different channels, high calculation efficiency is kept while global feature dependence is captured, and the method is suitable for processing and segmentation tasks of large-scale point cloud data. Two different network architectures are used, the structure and parameters of the model are flexibly adjusted to adapt to different application requirements, and a more efficient and reliable solution is provided for a point cloud segmentation task in a complex three-dimensional scene. The method has better effects in the aspects of segmentation precision, segmentation result accuracy and universality.

The invention relates to the technical field of behavior recognition, in particular to a machine learning-based surgical personnel video behavior recognition method and system. The method comprises the following steps: acquiring a video frame sequence and a point cloud frame sequence from a hand washing area of an operator through an RGB camera and laser ranging equipment to obtain the video frame sequence and the point cloud frame sequence; performing hand point cloud segmentation on the point cloud frame sequence to obtain hand point cloud data; performing action original data generation according to the video frame sequence and the hand point cloud data to obtain action original data; and extracting position and attitude information of hand key points from the original motion data to obtain video key point feature data. Visual information is captured through the RGB camera, three-dimensional depth information is obtained through the laser ranging device, fine analysis and evaluation are carried out on actions in combination with the deep learning network, and finally higher recognition precision, robustness and automation level are achieved.

The invention relates to the technical field of power detection, and discloses a vegetation risk hidden danger detection method and system based on sparse point cloud segmentation, and the method comprises the steps: collecting image data of a monitoring region at different angles, generating sparse point cloud data, and carrying out the preprocessing of the sparse point cloud data; and performing point cloud segmentation on the sparse point cloud data through a neural network, dividing the monitoring region according to region types, analyzing a spatial relationship between vegetation and power equipment in a target region, performing risk assessment, and generating an early warning signal in combination with multi-modal data. According to the method, the sparse point cloud data is precisely segmented, the three-dimensional space characteristics of the tree are extracted, and the potential risk of the tree and the power transmission facility can be dynamically monitored in real time by precisely calculating the space relation between the tree and the power transmission facility; according to the multi-modal fusion data, multi-level early warning information is generated, so that the efficiency and precision of power transmission line inspection are greatly improved, and the pre-judgment and timely treatment of hidden dangers are realized.

The invention relates to the technical field of image processing, in particular to a feature point automatic labeling method and system based on point cloud data. The method comprises the following steps: acquiring regional multi-source point cloud data, and performing multi-modal data fusion to obtain point cloud fusion data; performing shoreline structure ground point cloud segmentation based on the point cloud fusion data to obtain a ground point cloud segmentation data set; performing edge detection on the ground point cloud segmentation data set, and identifying geometric salient points according to an edge detection result so as to obtain a feature point candidate set; performing ground feature type classification based on the feature point candidate set to obtain a target classification data set; distributing a symbolic pattern for the target classification data set and binding a point cloud feature attribute to obtain a symbolic data set; and performing multi-level symbol dynamic adjustment and interactive labeling based on the symbolized data set to obtain a symbol dynamic view. The method is helpful for improving the accuracy and efficiency of symbolization expression of the shore feature points, and has strong interactivity.

Modern retrofit construction practices use 3D point cloud data of the building envelope to obtain the as-built dimensions. However, manual segmentation by a trained professional is required to identify and measure window openings, door openings, and other architectural features, making the use of 3D point clouds labor-intensive. Automatic point Cloud Building Envelope Segmentation (Auto-CuBES) algorithms can significantly reduce the time spent during point cloud segmentation. The Auto-CuBES algorithm inputs a 3D point cloud generated by commonly available surveying equipment and outputs a dimensioned wire-frame model of the building envelope. By leveraging unsupervised machine learning methods in the Auto-CuBES methods facades, windows, and doors can be identified while keeping the number of calibration parameters low. Additionally, some embodiments of Auto-CuBES can generate a heat map of each facade indicating nonplanar characteristics that are valuable for optimization of connections used in overclad envelope retrofits.

The invention discloses a multi-scale attention aggregation-based target point cloud segmentation method for a battery replacement robot, and the method comprises the steps: 1, collecting an RGB image, a depth image and three-dimensional point cloud data of a target fastener through a binocular structured light depth camera, and carrying out the fusion to generate a FastSeg3D data set; 2, a two-stage preprocessing method is provided, noise points are removed through radius filtering, and background point clusters far away from a target are removed through DBSCAN density clustering; 3, the network encoder uses a local feature aggregation module to extract geometric features, and the calculation complexity is reduced in combination with a random sampling strategy; 4, embedding a multi-scale attention aggregation module into the jump connection of the encoder and the decoder, fusing the features through a channel and a space attention unit, and achieving the self-adaptive weight weighting of the features of each layer of the encoder; and 5, recovering the resolution of the original point cloud by adopting nearest neighbor interpolation up-sampling, outputting a segmentation semantic tag, and obtaining a high-quality point cloud target. According to the method, the operation time of the battery replacement robot is shortened, and the balance problem of large-scale target point cloud segmentation speed and precision is solved.

The invention discloses a building point cloud data high-precision three-dimensional model generation method, and belongs to the technical field of point cloud deep learning modeling, and the method comprises the following steps: S1, collecting multi-source building point cloud data; s2, carrying out deep learning point cloud registration; s3, performing deep learning point cloud completion; s4, carrying out deep learning point cloud segmentation; s5, slicing the point cloud and extracting building parameters; and S6, building a BIM model according to the extracted parameters. According to the method, the steps of point cloud registration, completion, segmentation and parameter extraction are optimized through a deep learning method, an efficient method from point cloud data to BIM model automatic generation is provided, and the method has the advantages of high precision, high robustness and adaptation to multi-source data.

The invention relates to the technical field of synchronous positioning and mapping, in particular to a three-dimensional mapping method and system. Extracting feature points of the obtained target map image by using an ORB-SLAM system, and performing three-dimensional visual reconstruction based on the feature points to generate an initial sparse point cloud map; fusing the sparse point cloud data of the target map image acquired by the plurality of sensors to obtain dense point cloud data, and processing the obtained dense point cloud data by using a point cloud segmentation algorithm and a target detection algorithm to obtain target information in the dense point cloud data; and performing spatial segmentation and organization on the dense point cloud data according to the target information in the dense point cloud data by using a tree data structure algorithm to obtain a point cloud map with a hierarchical structure. According to the method, the map data is accurate, and the management of the map data can be efficiently realized.

The application relates to a dynamic visualization method and device for earthquake disasters. The method comprises: acquiring initial point cloud data of buildings and scenes in a target area; acquiring shake displacement data varying with time of the buildings by simulating an earthquake excitation on the buildings and the scenes; performing voxel downsampling and point cloud segmentation and extraction on the initial point cloud data to determine a ground point cloud and a point cloud of each building; updating in real time coordinates of each point of the point cloud of each building according to the shake displacement data; and rendering in real time the ground point cloud and the coordinates of each point of the point cloud of each building to generate a dynamic visual image for displaying. Accordingly, seismic scenes of regional buildings are presented more truly and visually, thus improving the visualization effect.

The invention discloses a multi-mode SLAM method, system and device, a medium and a program product, and the method comprises the steps: segmenting a current image frame through employing a lightweight semantic segmentation network, obtaining a semantic mask of the current image frame, carrying out the correction, and projecting the corrected semantic mask to a three-dimensional point cloud, and obtaining a point cloud image of the current image frame under a radar coordinate system; performing point cloud segmentation to obtain a plurality of point cloud sets with different semantic classification tags; predicting a saliency mask of the current image frame, extracting visual feature points of the current image frame, and obtaining depth values of the visual feature points; and finally, tracking the visual feature points and constructing a local map. And meanwhile, a Scan-Context descriptor and a similar distance are utilized to carry out loopback detection and global BA on the point cloud set, and a global map is constructed. According to the invention, various sensor data are comprehensively utilized, the perception capability of environment characteristics is improved, and the problems of insufficient perception and reduced positioning precision in a complex environment are effectively solved.

The invention provides a laser radar point cloud analysis method based on transform. The method relates to the technical field of laser radar point cloud segmentation. The method comprises the following steps: synchronously acquiring a scene laser radar point cloud and an RGB image through a laser radar and a camera, and carrying out filtering processing on the laser radar point cloud; constructing a sparse depth map of the RGB image by using the laser radar point cloud; the pre-trained depth estimation model converts the RGB image of the scene into a dense depth map under the conditions of the internal reference of the camera, the relative attitude of the camera collecting the RGB image and the sparse depth map, and ensures that the generated dense depth map is aligned with the laser radar point cloud; the dense depth map and the laser radar point cloud which are matched with each other are provided for a Unet architecture point cloud segmentation model based on a Transform block, and the Unet architecture point cloud segmentation model supplements dense depth map features into laser radar point cloud features to realize laser radar point cloud segmentation.

The invention provides a sheep body size measuring method and device based on three-dimensional reconstruction and point cloud segmentation, a medium and a program product, and relates to the technical field of computer vision. The method comprises the following steps: acquiring a sheep image of a to-be-detected sheep, and preprocessing the sheep image to obtain a multi-view sheep target point cloud; performing point cloud registration processing on the multi-view sheep target point cloud to obtain a sheep three-dimensional model; performing point cloud segmentation processing based on the body size measurement key points and the sheep three-dimensional model to obtain key point region point clouds; based on the key point region point cloud, determining a region point cloud projection space, performing attitude normalization on the interval point cloud, and determining an attitude normalized key point region point cloud; and calculating the body size data of the to-be-measured sheep based on the point cloud of the key point region subjected to attitude normalization. According to the embodiment of the invention, through three-dimensional reconstruction of the body surface point cloud of the sheep, non-contact measurement of the body size data of the sheep is realized, and the measurement efficiency and accuracy are improved.

The invention discloses an instance segmentation and model reconstruction method and system based on deep learning and synthetic point clouds, and the method comprises the steps: firstly, carrying out the sampling of a BIM model, and generating an instance-level synthetic point cloud; comprising the following steps: uniformly sampling to generate a non-occlusion point cloud, generating an occlusion point cloud by applying a hidden point removal algorithm, and selecting candidate viewpoints to simulate the occlusion point cloud in a real scene. And then, the generated synthetic point cloud is used for training a deep learning instance segmentation model, and the trained model is used for segmenting an actual point cloud scene, so that component-level accurate identification is realized. And finally, parametric modeling is carried out on the segmented point cloud through Dynamo software, a building component is reconstructed according to an instance segmentation result, and three-dimensional model reconstruction in a complex scene is completed. The method can effectively improve the precision and modeling efficiency of point cloud segmentation in a complex environment, and is widely applied to the fields of building intelligent operation and maintenance, electromechanical equipment management and the like.

The invention discloses a lead strain clamp three-dimensional pose identification method and system based on structured light, and the method comprises the steps: obtaining a multi-view structured light image, and carrying out the preprocessing of the structured light image, and obtaining a target structured light image; carrying out point cloud reconstruction according to the target structured light image, then carrying out denoising and abnormal value removal on the reconstructed three-dimensional point cloud, carrying out point cloud segmentation according to geometric features and spatial distribution of the point cloud, and obtaining a point cloud cluster after segmentation; selecting one point cloud in the point cloud cluster as a reference point cloud, and performing point cloud registration on other point clouds and the reference point cloud to obtain a registered point cloud cluster; and for the registered point cloud clusters, fitting a pre-established strain clamp three-dimensional model with the point cloud data by adopting a model-based pose estimation method, and determining a three-dimensional pose estimation result of each strain clamp. According to the invention, the problem of three-dimensional pose measurement of the strain clamp in a complex environment is effectively solved, and high-precision and high-robustness measurement effects are realized.

The invention discloses an automatic stacking method and device, a terminal and a storage medium, and the method comprises the steps that package information of a to-be-stacked package is obtained according to an acquisition device, and an optimal stack type is matched for the to-be-stacked package in combination with historical stack type data of an automatic stacking system; carrying out point cloud segmentation on the tray with the stacked parcels through a preset segmentation model to obtain a segmentation result, calculating a tray gap in combination with the optimal stack type and a preset path planning algorithm, and generating a stacking path; the flexible clamping jaw device is controlled to grab the parcels to be stacked, the grabbing posture is corrected in real time in combination with feedback of the torque sensor, and the mechanical arm is controlled to execute stacking according to the stacking path; and when the segmentation result of the tray is matched with the optimal stack type, a stacking three-dimensional model is generated, the stacking three-dimensional model is bound with the AGV below the tray, and the AGV is controlled to be automatically put in storage. According to the method, stacking and warehousing are completed through stack type matching and path planning, and the adaptability and the space utilization rate of a stacking system are improved.

A point cloud cylinder segmentation processing method and apparatus, and an electronic device. The present invention relates to the technical field of computers, and in particular, to the technical field of three-dimensional point cloud segmentation. The method comprises: acquiring a plurality of sample points comprised in a point cloud corresponding to a target object; determining neighbor points of the plurality of sample points within respective preset neighborhood ranges; on the basis of the neighbor points of the plurality of sample points within respective preset neighborhood ranges, obtaining normal vectors respectively corresponding to the plurality of sample points; performing clustering processing on the plurality of sample points on the basis of the normal vectors respectively corresponding to the plurality of sample points to obtain a clustering result; and performing cylinder segmentation on the target object on the basis of the clustering result to obtain a cylinder segmentation result of the target object.

The invention relates to a progress monitoring method based on digital twinning and a BIM (Building Information Modeling) information management system, which can realize automation of a progress monitoring process and can provide more accurate and more objective building activity progress information. By using a three-dimensional information front view and a deep learning-based semantic segmentation technique, the completion percentage of the construction activity can be detected and reported. According to the invention, construction management personnel can be helped to track the progress of main structures such as beams, columns, floor plates and the like, and potential delay or problems can be found as early as possible in the construction process. Through BIM color coding and a point cloud segmentation technology, the progress of each activity can be visualized, so that a project manager is helped to make a more intelligent decision, and resource allocation is optimized.

The invention discloses a wheat organ three-dimensional point cloud segmentation method based on dynamic voxel and category perception. Three-dimensional reconstruction is carried out on a wheat sample based on a three-dimensional Gaussian splash method, and dense point cloud data are obtained; performing data amplification on the dense point cloud data by adopting a dynamic voxel rasterization random sampling method combining dynamic voxel division and a random farthest point sampling strategy, and performing standardized point cloud quantity processing to obtain a standard point cloud data set; and based on the standard point cloud data set, carrying out key organ segmentation by adopting a category perception segmentation network. According to the data enhancement method, key geometric features are maintained while data diversity is expanded, a feature extraction module of the category perception segmentation network solves the problem of ignoring important points during sampling by fusing DCA and a weighted farthest point sampling strategy, and an improved loss function combining a point cloud space structure and information is adopted to improve the accuracy of data enhancement. And the segmentation precision of the wheat complex point cloud data is effectively improved.

The invention provides a shield muck three-dimensional point cloud segmentation and volume calculation method based on deep learning, and belongs to the technical field of deep learning and shield, and the method comprises the steps: S1, point cloud data collection, preprocessing and sample labeling; s2, constructing and training a deep learning segmentation model; s3, carrying out muck point cloud segmentation; and S4, calculating the volume of the muck. According to the method, efficient interaction of point cloud attention is realized by adopting four serialization modes, and the influence of point cloud disorder on calculation is avoided.

Three-dimensional point cloud segmentation is realized based on the Rebar-YOLOv8-seg network and depth data, so that the steel bar spacing is measured, and the method has the advantages of low cost and high real-time performance. The Rebar-YOLOv8-seg network improves the accuracy of steel bar segmentation and reduces the calculation cost, so that the method can meet the requirements of rapid and accurate measurement of the steel bar spacing in the power transmission and transformation extension project.

The invention relates to the technical field of building structure health monitoring, in particular to an unmanned aerial vehicle laser radar-based building point cloud deformation quantitative extraction method, which comprises the following steps of: based on building corner structure characteristics in point cloud data, carrying out quantitative evaluation on the overall inclination state of a building through point cloud segmentation, straight line fitting and angle calculation; based on an ideal plane hypothesis, performing plane fitting and point-to-plane distance analysis on the building facade point cloud in the point cloud data, identifying a local concave-convex deformation area of the building facade, and performing quantitative evaluation; and roof plane detection and wall surface normal vector analysis are carried out based on the point cloud data, and accurate quantification is carried out on parapet wall inclination based on region growth segmentation and PCA principal direction analysis. Through automatic processing and quantitative analysis of an algorithm, subjective errors and inconsistency possibly occurring in a traditional manual evaluation method are avoided, and objectivity and repeatability of an evaluation result are enhanced.