216 results about "Octree" patented technology

The invention discloses a BIM model automatic generation method and system based on point cloud data, and belongs to the field of building information modelling, and the transmission method comprises the steps: obtaining original point cloud data, and employing a filtering algorithm based on point cloud density adaptive adjustment to carry out the preprocessing of the point cloud data; constructing a voxel octree structure for the preprocessed point cloud data, and performing semantic classification on the point cloud; geometric modeling is carried out based on the segmented point cloud subsets, and a fitting algorithm is adopted to carry out shape completion on a point cloud area; semantic annotation is carried out on the components subjected to geometric reconstruction, a corresponding relation between component types and spatial attributes is constructed, fusion features based on a point feature histogram and a local curvature are adopted, and classification is carried out; a standard BIM component family is converted, and a three-dimensional BIM model is constructed through the mapping relation. According to the method, the voxel octree data structure and the deep semantic segmentation neural network model are combined, division and semantic recognition are performed on the point cloud data, the intelligent degree of the model is improved, and manual intervention is reduced.

The invention discloses a self-adaptive octree three-dimensional magnetic method inversion method fused with abnormal region recognition, and belongs to the technical field of three-dimensional magnetic method data inversion processing. Comprising the steps that initial inversion is rapidly completed on a coarse grid, the overall contour of an underground structure is obtained, a fuzzy c-means clustering algorithm is introduced, and a target area with significant magnetic anomaly is automatically extracted from a coarse solution; and constructing a multi-level octree grid in the extracted abnormal region to realize fine subdivision of the complex geological boundary, and further executing high-precision inversion by using a fine model as a new starting point. The adaptive octree grid has the characteristics of high efficiency, small grid number and small data fitting difference. The process of coarse inversion, intelligent identification, local refinement and fine inversion can be circularly executed as required, and computing resources are dynamically allocated. Compared with a traditional global fine grid scheme, the inversion process has the advantages that the inversion precision is maintained and even improved, and meanwhile, the number of grid units is remarkably reduced.

The invention relates to an OpenGL (Open Graphics Library)-based large-scale three-dimensional model object quick pickup method. The method comprises the following steps: converting an interactive operation of a user into a three-dimensional space structure; a CPU constructs octree space acceleration structures based on a bounding box of an object in a large-scale three-dimensional model, and leaf nodes, intersecting with a pickup ray or a pickup view pyramid, of an axis alignment bounding box in each octree space acceleration structure are screened out to serve as candidate leaf nodes; collecting triangles contained in all candidate leaf nodes, and constructing a candidate triangle set; dynamically allocating thread groups and threads of the GPU based on the number of candidate triangles in the candidate triangle set; and performing highlight display on the candidate triangles intersected with the pickup ray or the pickup view cone, and feeding back IDs of the corresponding candidate triangles. According to the method, the data transmission quantity and the video memory access pressure are remarkably reduced, the pickup response time is greatly shortened in a large-scale three-dimensional model scene, and the requirements of real-time interaction and visual operation can be met.

The invention relates to the technical field of three-dimensional models, in particular to a drawing method for making a three-dimensional model based on three-dimensional laser point clouds, which comprises the following steps of: sequentially reading point cloud data files of the three-dimensional laser point clouds, inserting coordinate values of each point into an octree structure, and when octree node buffer areas in a memory are full, drawing a three-dimensional laser point cloud into the octree structure; and writing the node data in the buffer area into a hard disk, and traversing the hard disk octree node set from bottom to top. According to the method, the three-dimensional laser point cloud data is inserted step by step, and the hierarchical aggregated octree structure is utilized, so that the point cloud data processing efficiency is effectively improved, and the memory occupation pressure is reduced; representative points in an octree structure are fused with an average normal to construct a continuous function field, a grid model is adaptively generated in a multi-detail-level mode, and the detail retaining capacity and drawing precision of the model are improved. Furthermore, accurate identification and positioning of topological features are realized by adopting calculation of a cell complex sequence and a topological feature noise rank.

A system and method for encoding and decoding the geometry of 3D point clouds using octree-based data structures are disclosed. The method involves encoding and decoding bitstreams containing octree structure information and vertex data, including the presence and position of vertices on cuboid edges corresponding to leaf nodes. The decoding process determines triangles connecting vertices within each cuboid, which are voxelized to reconstruct the 3D point cloud. To enhance voxelization accuracy, triangles may be extended along one or more sides based on a sampling distance parameter (dsampldsampl) or adaptive halo parameters. The encoding process utilizes similar principles to encode the octree structure and vertex information, supporting geometry reconstruction with high fidelity. The system employs the Möller-Trumbore algorithm and barycentric coordinate calculations with constraints based on dsampldsampl for voxelization. Extensions may include fixed or adaptive parameters encoded within the bitstream.

The invention provides a public space dynamic risk network assessment method fusing spatio-temporal characteristics, which relates to the technical field of network assessment, and comprises the steps of acquiring basic data, adaptively dividing grid units by adopting an octree algorithm, establishing a connection relation graph, calculating a risk propagation probability by utilizing a graph neural network, and calculating a risk propagation path by applying an improved A * algorithm. And finally outputting risk level distribution, propagation path risk change and prevention and control deployment suggestions. According to the invention, refined quantitative evaluation of public space risks is realized, and risk early warning accuracy and prevention and control measure pertinence are improved.

The invention discloses an adaptive edge detection iterative denoising point cloud reconstruction method and system. A regularization parameter and a kernel bandwidth are automatically set through point cloud normalization and local covariance noise estimation; an implicit curved surface is fitted based on a parameterized Gaussian field, and initial curved surface construction without a normal direction is realized. Edge detection is carried out by utilizing Voronoi covariance measurement, and the edge detection is continuously mapped into a projection weight, so that feature details of the point cloud are reserved in an acute angle region, and smooth denoising is realized in a flat region. The regularization intensity is dynamically adjusted through residual trend analysis to enhance detail representation, and a triangular mesh is extracted and output in combination with octree discretization and contour surface extraction. The method has unsupervised adaptivity, has outstanding performance in the aspects of edge fidelity, noise robustness and complex structure adaptability, can effectively process point cloud data with high noise, missing normal direction or irregular structure, and is suitable for the fields of engineering survey, reverse modeling, cultural relic digitization and the like.

The invention discloses a rendering method, which comprises the following steps: constructing an octree, and storing a to-be-rendered object in a to-be-rendered three-dimensional scene in a child node of the octree to obtain a space management octree; dividing the view cone into different view cone levels; traversing the space management octree, and determining a first intersection part between the space of the child node and the view cone and a view cone level to which the first intersection part belongs; distributing the first intersection part to different rendering queues according to the view cone hierarchy to which the first intersection part belongs; shielding and removing the first intersection parts in the different rendering queues to obtain current visible objects to be rendered; and rendering the current visible to-be-rendered object. According to the method, the octree is constructed to manage the three-dimensional scene space, the view cone levels are divided, the rendering queues are distributed, the view cone removing and multi-level shielding removing technologies are combined, the visible objects are accurately judged, the rendering load is effectively reduced, and the method has the advantages that the rendering performance is improved, space management is optimized, and the complex scene processing efficiency is improved.

The invention relates to the field of CIMs, and discloses a method and system for automatically degrading a CIM6-level three-dimensional model to CIM5 based on a semantic rule engine, and the method comprises the steps: obtaining CIM6-level three-dimensional model data, and constructing a scene hierarchical tree based on the three-dimensional model data; identifying the category, the attribute, the service label and the importance constant of each object in the scene hierarchical tree; calculating geometric complexity, semantic importance and topological dependency of each object, and calculating a comprehensive weight; matching the interval of the comprehensive weight and executing a corresponding processing operation based on the matched interval; performing grid merging and texture baking operation on the reserved object; performing grid instantiation on the proxy object after proxy replacement; collision holes in the model are detected through an octree in combination with a GJK algorithm to complete topological consistency verification, a lightweight CIM5-level model data packet is generated, and a difference report is output. According to the method, the consistency of the key semantic information and the topology is kept while the data volume of the CIM6 model is reduced.

The invention relates to an interstellar medium radiation transfer calculation method and device based on ray tracing. The method comprises the following steps: discretizing a three-dimensional space model of an interstellar medium by using a Cartesian grid to obtain a plurality of grid units, and emitting a plurality of uniformly distributed light rays at the center of each grid unit to construct a discretized radiation transfer equation; for each of the light rays, screening out the grid units through which the light rays penetrate by adopting an octree algorithm; calculating the length of a path where each light ray intersects with the grid units when the light ray passes through the grid units by adopting a flat plate method; and solving a discretized radiation transfer equation according to the path length to obtain a parameter equation of the interstellar medium. By adopting the method, the calculation amount of the ray tracing numerical integration in the radiation transfer equation can be reduced.

In one implementation, a method of decoding point cloud data is presented, comprising: decoding features representing voxel attributes in an octree structure, wherein a decoded feature for a current voxel is representative of at least a set of voxels that are still to be reconstructed; determining an attribute probability of the current voxel based on the decoded feature for the current voxel; decoding attribute information of voxels in the octree structure, wherein the attribute information for the current voxel is decoded based on the attribute probability for the current voxel; and reconstructing the point cloud based on the attribute information of voxels in the octree structure. On the encoder side, the features are extracted and encoded into the bitstream.

The invention discloses a dual-path three-dimensional point cloud compression reconstruction method fusing multi-scale geometric features. The method comprises the following steps: extracting representative skeleton anchor points from original point clouds by adopting farthest point sampling; predicting an octree node occupancy state by using an anchor encoder, and performing entropy coding to generate a skeleton bit stream; a skeleton condition feature encoder is introduced, probability distribution of local features is predicted by using skeleton context priori, cross-path context association is constructed, and a feature bit stream is generated; in the decoding stage, the sparse skeleton point set is restored from the skeleton bit stream, local features are recovered from the feature bit stream in combination with the skeleton structure context, fine-grained reconstruction is carried out on the point cloud through a geometric fusion point cloud up-sampling module, and finally the high-fidelity point cloud is obtained. According to the invention, the point cloud compression effect and reconstruction quality can be improved. The method can be widely applied to the point cloud field.

The invention belongs to the field of unmanned aerial vehicle (UAS) traffic management (UTM) and computer science, and particularly relates to a method and system for airspace modeling and path planning of a logistics unmanned aerial vehicle, and the method comprises the steps: S1, receiving multi-source data, and constructing a hierarchical mixed data model; s2, coding the control constraint data and the dynamic environment data into semantic attributes of nodes in a two-dimensional quadtree layer and / or voxels in a three-dimensional octree layer so as to generate a semantic airspace model containing computable rules; s3, based on the semantic airspace model, executing a hierarchical path planning algorithm to determine a flight path from the starting point to the ending point; and S4, combining the refined paths, and outputting a four-dimensional flight path containing a timestamp. The airspace rule is internalized into the model attribute, the real-time performance, safety and compliance of path planning are remarkably improved, the airspace resource utilization efficiency is improved, and key technical support is provided for large-scale urban unmanned aerial vehicle logistics operation.

A G-PCC coder may determine an occupancy of a reference child node in a reference node, wherein the reference node is in a reference frame of point cloud data used for inter prediction of a current node in a current frame of the point cloud data. The G-PCC coder may further determine a context for decoding a current occupancy bit of a current child node of the current node based on the occupancy of the reference child node, and arithmetic decode the current occupancy bit using the context.

The invention discloses a low-altitude path planning method and system based on a dynamic adaptive graph neural network, and belongs to the technical field of artificial intelligence and robot autonomous navigation. Aiming at the problems of large calculation amount of a fixed grid map, slow dynamic response of a static octree, failure of a traditional heuristic function on a non-uniform graph and the like, the invention provides the following scheme: constructing a resolution self-adaptive non-uniform graph by using the octree; a Morton code is adopted to realize rapid positioning of a dynamic obstacle and millisecond-level updating of a map; an attention mechanism is introduced into a graph attention network (GAT), weights are adaptively allocated to different neighbor nodes, features are aggregated, and a high-precision heuristic cost estimation value is output; and finally, integrating the estimated value into an algorithm to perform efficient path search. According to the method, the memory occupation and the calculation complexity are remarkably reduced, and the planning efficiency and the path safety in a dynamic environment are improved.

In one implementation, a method of decoding point cloud data is presented, comprising: decoding features representing voxel attributes in an octree structure, wherein a decoded feature for a current voxel is representative of at least a set of voxels that are still to be reconstructed; determining an attribute probability of the current voxel based on the decoded feature for the current voxel; decoding attribute information of voxels in the octree structure, wherein the attribute information for the current voxel is decoded based on the attribute probability for the current voxel; and reconstructing the point cloud based on the attribute information of voxels in the octree structure. On the encoder side, the features are extracted and encoded into the bitstream.

The invention discloses a point cloud compression method based on multi-dimensional feature fusion, electronic equipment and a medium, and aims to effectively reduce the compression bit rate while maintaining the precision of a space structure. According to the method, space, channel and topology redundant information is fused under a unified framework for the first time, and the method is suitable for efficient point cloud compression of various three-dimensional scenes such as automatic driving and virtual reality. Meanwhile, a local graph convolution Mama module is introduced, and the limitation that a point cloud topological structure is difficult to capture in a traditional method is effectively solved. Then, the octree nodes are divided into two dimensions of space and channel through a space-channel coupling grouping module, so that gradual feature fusion is realized, and the context information prediction precision is improved; experimental results show that the compression efficiency is remarkably improved on a plurality of radar and voxel human body model data sets.

The invention discloses a millimeter wave radar point cloud compression method based on heterogeneous representation and an implicit neural network. Firstly, point cloud fine geometric features are extracted through a point-based compression network, global semantics are aggregated through a voxel-based compression network, and point-voxel heterogeneous joint representation and preliminary compression are realized. Secondly, constructing an adaptive octree to code a compressed data space structure to generate a partitioned bit stream, and performing statistical compression on the voxel-level high-dimensional features by using context-aware entropy coding to generate a feature bit stream; then, joint transmission is performed on the partition bit stream, the feature bit stream and the neural network weight for implicit representation. And finally, decoding and recovering the coarse-grained point cloud at a receiving end, inputting the coarse-grained point cloud and a neural network weight into a point-based synthesis network based on implicit neural representation, and finally reconstructing a high-fidelity point cloud through continuous surface modeling and detail compensation. According to the method, the compression efficiency and the reconstruction quality of the millimeter wave radar point cloud under the low code rate are remarkably improved.

The invention discloses a steel truss extraction method based on point cloud segmentation and feature analysis, and belongs to the technical field of three-dimensional point cloud processing. The method mainly comprises the following steps: firstly, carrying out multi-scale filtering denoising on an original point cloud and constructing an octree index; local geometric features such as normal vectors, curvatures and first-order plane fitting errors of all the points are calculated; based on the adaptively selected seed points, carrying out region growth by combining a normal vector included angle, a curvature difference and an adaptive edge criterion to realize preliminary segmentation; and finally, a coarse-to-fine multi-layer cascade classifier is adopted to screen the segmented areas, and the steel truss point cloud is accurately extracted through geometric dimension filtering, shape feature screening and multi-dimensional feature matching in sequence. The method does not need pre-training, has the advantages of strong anti-interference capability, high extraction precision and high calculation efficiency, and is suitable for engineering scenes of bridge detection, building surveying and mapping and the like.

The invention discloses an industrial part point cloud completion method and system based on layered voxels, a medium and a product, and belongs to the technical field of three-dimensional point cloud data processing and intelligent perception.The method comprises the steps that original point cloud data of a target industrial part is obtained, a voxel hierarchical structure is constructed based on an octree space division rule to obtain a multi-level voxel grid, and the multi-level voxel grid is constructed; predicting the occupancy probability of each voxel based on a voxel generation network to obtain a voxel occupancy probability graph, constructing high-dimensional feature representation, generating initial completion point cloud data of a target industrial part under structural guidance constraint by adopting an avalanche type point generation mechanism, and carrying out a spatial relationship modeling algorithm and a position fine tuning process to obtain an initial completion point cloud data of the target industrial part; and optimizing the spatial distribution and the position coordinate precision of the initial completion point cloud data, performing processing based on an amplified position coding mechanism, and outputting the point cloud data of the target industrial part after completion. The problems that in the prior art, industrial part point cloud data details are missing, boundaries are fuzzy, redundant point clouds exist, and a topological structure is damaged are solved.

In one implementation, a method of encoding or decoding point cloud data is provided, comprising: obtaining a feature map representing attributes of voxels in an octree structure; determining one or more probability distribution parameters for a probability density function associated with an attribute of a current voxel, based on the feature map; determining a probability mass function of the attribute of the current voxel based on the one or more probability distribution parameters for the probability density function for the current voxel; and encoding or decoding attribute information of the current voxel in the octree structure, based on the probability mass function of the attribute for the current voxel.

A method for encoding a point cloud to generate a bitstream of compressed point cloud data is provided. The point cloud's geometry is represented by an octree-based structure with a plurality of nodes having parent-child relationships by recursively splitting a volumetric space containing the point cloud into sub-volumes each associated with a node of the octree-based structure. The method includes: determining a coding mode, wherein the coding mode includes a planar coding mode and an angular coding mode; obtaining coding context information for a present child node, entropy encoding the present child node based on the obtained coding context information to produce encoded data for the bitstream.

The invention discloses a dynamic octree mapping method and system based on probability semantic fusion and a storage medium, and mainly solves the problem that a traditional octree map lacks semantic information and cannot support an advanced cognitive task. According to the implementation scheme, the method comprises the following steps: acquiring an RGB image, depth information and pose data through a sensor; extracting pixel-level semantic probability distribution by using a pre-trained semantic segmentation network; geometric information in the perception data is projected to a three-dimensional space based on the pose, and a three-dimensional observation point with semantic information is formed; performing ray projection on octree nodes, respectively updating geometric occupancy states of penetrated non-end-point nodes, and synchronously updating geometric occupancy states and semantic belief distribution of end-point surface nodes by using three-dimensional observation point data; semantic information is continuously optimized through Bayesian probability fusion, and dynamic pruning and map optimization are realized based on semantic entropy. According to the method, the map storage efficiency and the calculation speed can be remarkably improved while refined and real-time updated geometric and semantic information is given. The method can be used for map construction and storage of visual SLAM positioning, and fine three-dimensional object modeling and environment information description.

The invention discloses a 3D Gaussian splash rendering optimization method based on a Unity engine in VR, and relates to the technical field of computer graphics. PLY point cloud files are converted into SOG high-compression formats containing multi-level LOD information, after low-transparency Gaussian points are removed, Morton coding sorting is carried out on the center coordinates of the Gaussian points, and then the 3D Gaussian splash rendering optimization method based on the Unity engine in VR is realized. The method comprises the following steps: constructing an octree spatial index containing a spatial bounding box and multiple LOD data, removing Gaussian points in a grading manner according to a camera distance, selecting an LOD hierarchy, carrying out deep barrel sorting on visible Gaussian points, executing drawing by taking a barrel as a unit, and combining near-to-far sorting, an improved mixed equation and screen space edge removal and amplification operation to obtain a target image. According to the method, through multi-dimensional optimization such as loading, space elimination and rendering, the loading efficiency of the VR equipment is improved, the rendering burden is reduced, Tile-Based hardware is adapted, and smooth operation of 3D Gaussian splashing on the VR all-in-one machine is realized.

The invention provides a Gaussian data tree index construction method and device supporting incremental updating and a rendering method.The Gaussian data tree index construction method comprises the steps that firstly, normalization processing and integer format conversion are conducted on original Gaussian data, spatial index key values are generated based on coordinates of an integer format, and the spatial index key values serve as coded values corresponding to the original Gaussian data; segmenting the original Gaussian data into a plurality of data blocks according to the coded values; then constructing a local octree; then, generating a global octree; a storage offset is distributed to each node of the global octree, and a byte range corresponding to each node is determined based on the storage offset; each data block stores the original Gaussian data according to the corresponding storage offset and the sequence of the coded values. The subsequent query efficiency can be improved by storing the data blocks according to the coded value sequence, meanwhile, the memory mapping technology is adopted to process the oversized node data so as to reduce IO overhead, and the storage layout and the output mode effectively improve the data writing efficiency and the subsequent reading performance.

The invention discloses a block file space topology aggregation and cross-modal indexing method based on HDF5, and relates to the technical field of electric digital data processing. The method comprises the following steps of point cloud space division, space data separation and storage, multi-modal data fusion and multi-level index construction. The method comprises the following steps: performing space division on a point cloud space through an octree algorithm to obtain octree nodes, analyzing the relevance between the space division and a memory in real time to determine whether to perform octree division dynamic adjustment, then encoding each octree node, performing space block data separation storage, and simultaneously performing point cloud data access optimization. According to the method, the multi-level index system of the point cloud space is established, then spatial attribute aggregation is performed to establish the spatial diagram, multi-modal data integration fusion is performed, and finally the multi-level index system of the point cloud space is established, so that the efficiency of accessing and retrieving the image block file space is improved, and the problem of low cross-modal retrieval efficiency of the point cloud space divided based on an octree algorithm in the prior art is solved.

The invention relates to the technical field of building information model optimization, in particular to a large-volume BIM model optimization system based on intelligent loading, which comprises a model analysis unit, a function identification unit, a simplified execution unit and a model recombination unit, and is characterized in that the model analysis unit reads an IFC file and separates component data; the function identification unit outputs a decoration component (first class) set, a non-bearing structure (second class) set and a bearing component (third class) set through geometric and semantic dual-channel analysis, the simplified execution unit performs edge folding on the first class, performs vertex clustering on the second class and skips simplification on the third class, and the model recombination unit binds metadata and constructs a dynamic octree index. The problems that a traditional simplification strategy is single and core information is lost are solved, the model loading efficiency and application precision are improved, and the method is suitable for complex building scenes.

The invention discloses a railway construction monitoring optimization method based on point cloud recognition, and the method comprises the steps: obtaining a unified coordinate system track structure three-dimensional point cloud data set, and forming refined priority measurement for the node expansion and node combination of a three-dimensional voxel octree data structure; a self-adaptive voxel grid is obtained; mapping the unified coordinate system track structure three-dimensional point cloud data set to an adaptive voxel grid according to a time phase; outputting the voxel anomaly representation set to obtain a voxel anomaly degree field updated through spatial consistency constraint; and a track engineering index curve set capable of realizing mileage positioning is formed, a grading early warning result set is output, the grading early warning result set is associated with the voxel spatial index codes, the voxel resolution labels and the mileage positioning information, and a structured alarm report is formed. According to the method, the spatial analysis precision of special-shaped key parts of turnouts, bridge and tunnel joints and fasteners is remarkably improved, unnecessary high-resolution calculation of stable areas is avoided, and the overall calculation resource consumption is reduced.

The invention discloses an LOD model generation method. The method comprises the following steps: constructing an octree topological structure according to a ground object three-dimensional model; judging whether the grid unit of any effective leaf node of each level of any visual angle has the grid unit of the adjacent effective leaf node or not; if the Boolean matrixes exist, marking the Boolean matrixes corresponding to the two adjacent grid units to be connected to obtain a connected region set; traversing each connected region in the connected region set, and determining the simplest contour of the connected region to generate a target contour set; subdividing the target contour set to generate a target polygonal grid set; performing vertex de-duplication on the target polygonal grid set to obtain a heterogeneous grid octree structure; and performing node aggregation from bottom to top on the heterogeneous grid octree structure to generate a multi-level LOD model. According to the invention, the construction of the LOD model with light weight and high fidelity is realized. The invention further discloses a device for implementing the method, electronic equipment and a storage medium.

In one implementation, we propose a bitwise octree coding approach based on deep neural networks and operations on 3D sparse tensors. To encode / decode a certain level of detail (LoD) in an octree, geometric features are first inherited from the previous LoD by upsampling. Then based on the already encoded / decoded voxels, the point cloud geometry is firstly refined by pruning, followed by combining with the known context information. In the end, feature aggregation and probability estimation can be applied to obtain the occupancy probabilities for actual arithmetic encoding / decoding. A corresponding probabilistic training strategy is also proposed for our bitwise octree coding approach.