CN113496543A - Point cloud data screening method and device, electronic equipment and storage medium - Google Patents
Point cloud data screening method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN113496543A CN113496543A CN202010256426.9A CN202010256426A CN113496543A CN 113496543 A CN113496543 A CN 113496543A CN 202010256426 A CN202010256426 A CN 202010256426A CN 113496543 A CN113496543 A CN 113496543A
- Authority
- CN
- China
- Prior art keywords
- point cloud
- octree
- nodes
- data
- structure data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 63
- 238000012216 screening Methods 0.000 title claims abstract description 36
- 238000000547 structure data Methods 0.000 claims abstract description 108
- 238000005070 sampling Methods 0.000 claims abstract description 75
- 238000004590 computer program Methods 0.000 claims description 6
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000004088 simulation Methods 0.000 description 13
- 230000008520 organization Effects 0.000 description 10
- 230000008569 process Effects 0.000 description 8
- 238000010586 diagram Methods 0.000 description 7
- 230000003287 optical effect Effects 0.000 description 6
- 230000001788 irregular Effects 0.000 description 4
- 238000000638 solvent extraction Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000009877 rendering Methods 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/005—Tree description, e.g. octree, quadtree
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
- G06T3/4053—Scaling of whole images or parts thereof, e.g. expanding or contracting based on super-resolution, i.e. the output image resolution being higher than the sensor resolution
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/149—Segmentation; Edge detection involving deformable models, e.g. active contour models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Computer Graphics (AREA)
- Geometry (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention discloses a method, a device, equipment and a storage medium for screening point cloud data, wherein the method comprises the following steps: carrying out grid subdivision on the original point cloud to obtain octree structure data corresponding to the original point cloud; and respectively sampling point clouds in leaf nodes of each level in the octree structure data to obtain target point clouds. By the technical scheme of the embodiment of the invention, the purposes of sampling the original point cloud data and obtaining the high-resolution point cloud data representing the detailed characteristics of the object are achieved.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a point cloud data screening method and device, electronic equipment and a storage medium.
Background
The loading and display of the point cloud data are important components of the automatic driving simulation platform. Because the point cloud data collected by the vehicle-mounted radar has rich details, a complex structure and a large data volume, the point cloud data is loaded and displayed at one time, so that certain requirements are not only required for a storage space, but also for hardware equipment, such as a memory, a Central Processing Unit (CPU), a display card and the like. Therefore, the rapid and smooth display of point cloud data in the automatic driving simulation platform is one of the main indexes for measuring the automatic driving simulation platform.
Currently, there are two common methods for displaying point cloud data: one method is to firstly rarefy the original point cloud data collected by the vehicle-mounted radar so as to reduce the data volume of the point cloud and further realize quick and smooth display. The other method is a grid division based display method, firstly grid division is carried out on original point cloud data collected by a vehicle-mounted radar, and point cloud data blocks meeting conditions are scheduled to be displayed according to the range of viewpoints during visual display.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
the rarefying algorithm of the point cloud data does not have a certain standard and is often considered, in order to quickly load and display the point cloud data, if the rarefying rate is too large, the data distortion is serious, and the obtained result cannot meet the requirements of automatically driving each algorithm module; if the rarefaction rate is too small, the purpose of quick display cannot be realized. In the display method based on grid division, for irregular and complex ground objects, violent grid division can cause excessive point cloud data in some grids, and point cloud data in some grids is too little or no point cloud data, so that the display result cannot correctly reflect the organization form of the point cloud.
Disclosure of Invention
The embodiment of the invention provides a point cloud data screening method and device, electronic equipment and a storage medium, which achieve the purposes of rarefying original point cloud data and obtaining high-resolution point cloud data representing detailed characteristics of an object.
In a first aspect, an embodiment of the present invention provides a point cloud data screening method, including:
carrying out grid subdivision on the original point cloud to obtain octree structure data corresponding to the original point cloud;
and respectively sampling point clouds in leaf nodes of each level in the octree structure data to obtain target point clouds.
In a second aspect, an embodiment of the present invention further provides a point cloud data screening apparatus, including:
the subdivision module is used for carrying out grid subdivision on the original point cloud to obtain octree structure data corresponding to the original point cloud;
and the sampling module is used for respectively sampling point clouds in leaf nodes of each level in the octree structure data to obtain target point clouds.
In a third aspect, an embodiment of the present invention further provides an apparatus, where the apparatus includes:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the point cloud data screening method steps as provided by any embodiment of the invention.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the point cloud data screening method provided in any embodiment of the present invention.
The embodiment of the invention has the following advantages or beneficial effects:
the method comprises the steps of obtaining octree structure data corresponding to original point cloud by performing grid subdivision on the original point cloud, achieving the purpose of storing the original point cloud in blocks by utilizing the octree structure data (namely a three-dimensional voxel array), and well expressing the point cloud organization form of an object with an irregular shape; the point clouds in all levels of leaf nodes in the octree structure data are further sampled respectively to obtain the target point cloud, so that the aim of rarefying the original point cloud is fulfilled, and high-resolution point cloud data representing the detailed characteristics of the object are obtained, so that the point cloud organization form of the object can still be well expressed by the residual point cloud data after rarefying, and the problem of distortion of the body of the object is avoided.
Drawings
Fig. 1 is a flowchart of a point cloud data screening method according to an embodiment of the present invention;
FIG. 2 is a diagram of an octree structure according to an embodiment of the present invention;
fig. 3 is a flowchart of a point cloud data screening method according to a second embodiment of the present invention;
fig. 4 is a flowchart of a point cloud data screening method according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of a point cloud data screening apparatus according to a fourth embodiment of the present invention;
fig. 6 is a schematic structural diagram of an apparatus according to a fifth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a point cloud data screening method according to an embodiment of the present invention, which is applicable to sampling an original point cloud of an object (for example, a large amount of point clouds obtained by scanning a vehicle-mounted radar), so as to achieve a scene of rarefying the original point cloud. The method can be executed by a point cloud data screening device, which can be implemented by software and/or hardware.
As shown in fig. 1, the method specifically includes the following steps:
The original point cloud generally refers to a point cloud scanned by a vehicle-mounted radar. Because the point cloud detail features obtained by vehicle radar scanning are rich, the structure is complex, the data volume is large, and the processing performance of processing equipment such as a computer is limited, the original point cloud obtained by vehicle radar scanning is usually subjected to thinning processing before the point cloud is utilized to carry out related work, so that the data volume of the point cloud is reduced, and the point cloud representing the key features of the object is not lost as much as possible. For this, in this embodiment, mesh subdivision is performed on the original point cloud by using an octree algorithm logic, corresponding octree structure data is established, and point clouds in leaf nodes of each level in the octree structure data are respectively sampled on this basis to obtain a target point cloud. The octree is the extension of the quadtree in the three-dimensional space, and the regular grid subdivision is adopted, so that the octree has better operability and can meet the cutting algorithm of the three-dimensional visual cone.
Specifically, the mesh subdivision is performed on the original point cloud to obtain octree structure data corresponding to the original point cloud, and the mesh subdivision includes:
establishing an octree square external bounding box of the original point cloud, and storing the original point cloud under a root directory;
according to the grid subdivision convergence condition, carrying out grid subdivision on the external bounding box of the octree cube, and storing sub-nodes containing point clouds based on the directory hierarchy where the sub-nodes are located;
and sequencing the point cloud data in the octree leaf nodes into a set data format.
Further, the establishing of the external bounding box of the octree cube of the original point cloud comprises:
determining a minimum outer bounding box of the original point cloud;
and establishing the external bounding box of the octree cube of the original point cloud by taking the center of the minimum external bounding box as the center of the external bounding box of the octree cube and taking the maximum side length of the minimum external bounding box as the side length of the external bounding box of the octree cube.
The minimum outer bounding box of the original point cloud is a minimum box body capable of enclosing or containing the original point cloud, the shape of the box body is determined according to the organization shape of the original point cloud, and if the organization shape of the original point cloud is the shape of an automobile, the shape of the box body is also the shape of the automobile; if the organization shape of the original point cloud is the shape of a telegraph pole, the shape of the box body can be the smallest cuboid surrounding the telegraph pole.
Carrying out octree grid subdivision on the root node according to grid subdivision convergence conditions, wherein the grid subdivision convergence conditions are generally two, one is a quantity threshold value of point clouds contained in the node, namely, when the quantity of the point clouds in the node obtained by subdivision is smaller than the quantity threshold value, the subdivision is stopped; the other is an octree depth threshold of grid subdivision, namely, when the depth of the subdivided octree reaches the octree depth threshold, the subdivision is stopped. In this embodiment, an octree depth threshold is taken as an example of a mesh partitioning convergence condition, and an octree mesh partitioning process of the original point cloud is described.
The bounding box outside the octree cube is considered the root node of the octree, which contains all of the original point cloud. Setting an initial value of octree depth to be 0, equally dividing the root node into 8 sub-nodes, distributing the corresponding original point cloud to the corresponding 8 sub-nodes, and increasing the octree depth by 1; traversing the 8 sub-nodes, and discarding the sub-nodes which are empty, namely discarding the sub-nodes which do not contain the point cloud; and storing the sub-nodes which are not empty, specifically, newly building a file under a root directory where the root node is located, wherein the newly built file is used as a primary directory to store the sub-nodes which are not empty, and the essence is to store point clouds in the sub-nodes, so that one-time grid subdivision of the octree is completed, and the nodes of a first level of the octree structure data, namely the sub-nodes which are not empty, are correspondingly obtained. Referring to fig. 2, a schematic diagram of octree structure data is shown, assuming that a root node of an octree is a root node 200, after a first mesh subdivision, nodes of a first level obtained include a node 210 and a node 280, and nodes 212 are all empty nodes and are discarded.
And judging whether the octree depth (currently 1) reaches the depth threshold (hypothesis 2), wherein 1 is smaller than 2, so that the octree depth is determined not to reach the depth threshold, and continuously performing grid subdivision on the nodes 210 and 280, wherein the subdivision process is similar to that of the root node 200. Specifically, the node 210 is equally divided into 8 sub-nodes, the point cloud in the node 210 is allocated to the corresponding 8 sub-nodes, the node 280 is equally divided into 8 sub-nodes, the point cloud in the node 280 is allocated to the corresponding 8 sub-nodes, and the octree depth value is increased by 1 to 2. Traversing the newly split 16 sub-nodes (respectively, 8 sub-nodes under the node 210 and 8 sub-nodes under the node 280), discarding the sub-nodes which are empty, and storing the sub-nodes which are not empty, specifically, newly building a file under the primary directory where the node 210 and the node 280 are located, where the newly built file is used as a secondary directory, and storing the sub-nodes which are not empty, where the essence is to store point clouds in the sub-nodes, so far, once mesh subdivision of the octree is completed, and correspondingly obtaining nodes of a second level of the octree structure data, that is, the sub-nodes which are not empty, such as node sets 211 and 281 shown in fig. 2.
And (4) carrying out grid subdivision according to the process until the depth of the octree reaches a depth threshold value, and stopping grid subdivision. And finally, sequencing the point cloud data in the octree leaf nodes into a set data format, such as a binary data format. The purpose of serializing the point cloud data in the octree leaf nodes into the set data format is to lay a foundation for deep utilization of the point cloud data in the follow-up process, such as point cloud loading and display, the data format which can be identified by a machine is a binary data format, and the point cloud data in the octree leaf nodes are serialized into the binary data format and then directly called during the follow-up loading and display. It should be explained that the octree leaf nodes refer to nodes occupied by the point cloud or nodes without the point cloud, and in the mesh subdivision process, nodes without the point cloud are already discarded, so the leaf nodes in the embodiment refer to nodes occupied by the point cloud. It is understood that, by setting a threshold, when the ratio of the area volume of the node occupied by the point cloud to the complete area volume of the node reaches the threshold, the node can be considered to be occupied by the point cloud.
As can be seen from the mesh subdivision process, the octree structure data corresponding to the original point cloud may specifically include the following information: octree level information, which nodes each level includes, which point clouds each node includes, and node type information identifying whether a node is a leaf node.
And 120, respectively sampling point clouds in leaf nodes of each level in the octree structure data to obtain target point clouds.
Specifically, the point clouds in leaf nodes of each level in the octree structure data are sampled by levels, so that high-resolution point cloud data representing detailed features of an object can be obtained, the octree structure data (namely, a three-dimensional voxel array) can achieve the purpose of storing original point clouds in blocks, the point cloud organization form of an object with an irregular shape can be well expressed, and further, the leaf nodes of each level are sampled, so that the purpose of reducing the point cloud data volume is achieved, point clouds in different position areas of the object can be sampled, complete point cloud data representing different position features of the object can be obtained, and the organization form of the original point cloud of the object can be well expressed without distortion.
According to the technical scheme of the embodiment, the octree structure data corresponding to the original point cloud is obtained by performing grid subdivision on the original point cloud, so that the aim of storing the original point cloud in blocks by utilizing the octree structure data (namely a three-dimensional voxel array) is fulfilled, and the point cloud organization form of an irregular object can be well expressed; the point clouds in all levels of leaf nodes in the octree structure data are further sampled respectively to obtain the target point cloud, so that the aim of rarefying the original point cloud is fulfilled, and high-resolution point cloud data representing the detailed characteristics of the object are obtained, so that the point cloud organization form of the object can still be well expressed by the residual point cloud data after rarefying, and the problem of distortion of the body of the object is avoided.
Example two
Fig. 3 is a flowchart of a point cloud data screening method according to a second embodiment of the present invention, and this embodiment describes in detail the step 120 "respectively sample point clouds in leaf nodes of each level in the octree structure data to obtain a target point cloud" on the basis of the second embodiment. Wherein explanations of the same or corresponding terms as those of the above-described embodiments are omitted.
Referring to fig. 3, the method for screening point cloud data provided in this embodiment specifically includes the following steps:
and 310, performing grid subdivision on the original point cloud to obtain octree structure data corresponding to the original point cloud.
And 320, sampling leaf nodes of the current level according to a set sampling rate aiming at nodes of each level in the octree structure data, and storing the sampled target point cloud in a directory of the current level.
For example, the current hierarchy is a first hierarchy in the octree structure data, and assuming that the first hierarchy includes 3 leaf nodes, each leaf node is sampled, and the target point cloud obtained by sampling is stored in a first-level directory corresponding to the first hierarchy. It should be noted that, the target point clouds obtained by sampling the 3 leaf nodes respectively are all stored in the new file, and the new file is not separately created for each leaf node, because all the point clouds obtained by sampling the 3 leaf nodes respectively represent the characteristic point clouds of the original point clouds at the current level together, and when deep utilization of the point clouds is performed in the future, all the point clouds obtained by sampling the level are used together, and therefore, separate storage is not performed.
Optionally, the leaf nodes may be sampled layer by layer from bottom to top starting from the lowest layer of the octree structure data, that is, a multi-resolution target point cloud is constructed from the sub-nodes to the root node; the leaf nodes can also be sampled layer by layer from top to bottom from the topmost layer of the octree structure data, namely, the multi-resolution target point cloud is constructed from the root node to the child nodes.
And step 330, traversing the sub-nodes of the non-leaf nodes of the current level, sampling the leaf nodes under the sub-nodes according to a set sampling rate, and storing the target point cloud obtained by sampling in the directory of the level where the sub-nodes are located.
And 340, executing the traversal operation of the sub-nodes aiming at the non-leaf nodes under the sub-nodes until the lowest level of the octree structure data is reached.
The method for determining the set sampling rate comprises the following steps:
determining the set sampling rate based on the hierarchy of the sampled leaf nodes in the octree structure data and the sum of the number of root leaf nodes and cotyledon nodes in the octree structure data;
the root and leaf nodes comprise leaf nodes obtained by performing primary grid subdivision on an octal tree square external bounding box based on original point cloud, and the cotyledon nodes comprise leaf nodes under child nodes of which father nodes are non-leaf nodes.
Taking fig. 2 as an example, assuming nodes 210 and 280 are leaf nodes, nodes 210 and 280 are root leaf nodes. If the node 210 is a non-leaf node and the 8 child nodes 211 are leaf nodes, the 8 child nodes are child leaf nodes, i.e., the parent node 210 is a non-leaf node.
Illustratively, determining the set sampling rate based on the hierarchy of the sampled leaf node in the octree structure data and the sum of the number of root leaf nodes and cotyledon nodes in the octree structure data includes:
the set sampling rate is determined according to the following formula: SamoleRatio ═ samplesize (vector) -Depthcurrent-1;
wherein, the SamoleRatio represents a set sampling rate, the samplesize (vector) represents the sum of the number of root leaf nodes and child leaf nodes in the octree structure data, and the DepthCurrent represents the level number of the currently sampled leaf node in the octree structure data.
Further, when mesh subdivision is performed on the original point cloud to obtain octree structure data corresponding to the original point cloud, the method further includes: and establishing a data index structure of the octree structure data. The data index structure specifically includes information pointing to the storage location of each node in the octree structure data, point cloud data of the corresponding node can be found based on the information, and spatial location area information of each node is also included. The nodes in the octree structure data are actually point cloud blocks, and each point cloud carries coordinate information, so that the spatial region information of each node can be determined, and in an automatic driving simulation platform or other simulation scenes of three-dimensional spaces, the spatial position region information can be used for determining the position relationship between an observation view point and the point cloud to be displayed, so as to determine which spatial regions of the point cloud need to be loaded and displayed according to the position relationship.
The data index structure can also be used for determining each level leaf node in the octree structure data in the process of sampling point clouds in each level leaf node, and the sum of the number of root leaf nodes and cotyledon nodes in the octree structure data is used for determining the sampling rate.
Specifically, step (1): adding the root node of the octree into a node linked list Vector based on the data index structure, and entering the step (2) if the last node of the linked list is not empty;
step (2): if the last node of the linked list is a non-leaf node, entering the step (3), and if the last node of the linked list is a leaf node, entering the step (4);
and (3): adding 8 sub-nodes of the non-leaf node into a node linked list Vector, deleting the last node if the last node of the linked list is empty, and returning to the step (2) if the last node is not empty;
and (4): and sampling the leaf nodes based on a set sampling rate, and storing the target point cloud obtained by sampling in an octree structure data directory where the sampled leaf nodes are located. And (3) deleting the current last node (namely the current sampled leaf node) of the linked list after sampling is finished, and returning to the step (2).
According to the technical scheme of the embodiment, leaf nodes of the current hierarchy are sampled according to a set sampling rate aiming at nodes of each hierarchy in the octree structure data, and target point clouds obtained by sampling are stored in a directory of the current hierarchy; traversing sub-nodes of non-leaf nodes of the current level, sampling leaf nodes under the sub-nodes according to a set sampling rate, and storing target point clouds obtained by sampling in a directory of the level where the sub-nodes are located; and executing the sub-node traversal operation aiming at the non-leaf nodes under the sub-nodes until the lowest level of the octree structure data is reached, so that the aim of traversing each level of leaf nodes in the original point cloud octree structure data is fulfilled, and the high-resolution point cloud data representing the detailed characteristics of the object is obtained by sampling each level of leaf nodes.
EXAMPLE III
Fig. 4 is a flowchart of a point cloud data screening method provided by the third embodiment of the present invention, and in this embodiment, on the basis of the third embodiment of the present invention, the obtained high-resolution target point cloud of the original point cloud is applied to the automatic driving simulation platform, so that the automatic driving simulation platform achieves the purpose of displaying the point cloud quickly and smoothly, thereby meeting the real-time requirement of automatic driving. Wherein explanations of the same or corresponding terms as those of the above-described embodiments are omitted.
As shown in fig. 4, the method comprises the steps of:
And 420, respectively sampling point clouds in leaf nodes of each layer in the octree structure data to obtain target point clouds.
The LOD (levels of details layering technology) provides an effective solution for solving the contradiction between the simulation effect and the simulation real-time performance, and has been widely applied to real-time rendering based on three-dimensional grids. In order to solve the scheduling problem of real-time display of massive point cloud data, in the technical scheme of this embodiment, on the basis of constructing a data index structure of octree structure data, a random sampling method is used, point clouds of leaf nodes in the octree structure data are taken as starting points, the point clouds in the leaf nodes are sampled level by level in a depth traversal order, octree multi-resolution LOD data of the point clouds are obtained, and the octree multi-resolution LOD data are stored in a directory file structure of the octree structure data. And when the point cloud is loaded and displayed, acquiring the point cloud to be loaded based on the directory file structure of the octree structure data, and loading and displaying the point cloud.
And 430, determining the position relation between the observation viewpoint and each node according to the spatial position area of each node in the octree structure data recorded by the data index structure.
The data index structure specifically includes information pointing to the storage location of each node in the octree structure data, point cloud data of the corresponding node can be found based on the information, and spatial location area information of each node is also included. The nodes in the octree structure data are actually point cloud blocks, and each point cloud carries coordinate information, so that the spatial region information of each node can be determined, and in an automatic driving simulation platform or other simulation scenes of three-dimensional spaces, the spatial position region information can be used for determining the position relationship between an observation view point and the point cloud to be displayed, so as to determine which spatial regions of the point cloud need to be loaded and displayed according to the position relationship. In simulating a three-dimensional physical space, the observation viewpoint is a point of the three-dimensional physical space.
And step 440, determining the point cloud to be displayed, which needs to be loaded from the external memory to the internal memory, according to the position relation.
And 450, acquiring the point cloud to be displayed from the target point cloud according to the storage position information of the target point cloud recorded by the data index structure in an external memory, and loading and displaying the point cloud.
The target point cloud is stored in an external memory, the data index structure is stored in an internal memory, the data index structure stores information pointing to the storage position of each node in the octree structure data, and meanwhile, based on the content disclosed in the embodiment, the target point cloud obtained by sampling the leaf nodes of each level in the octree structure data according to the set sampling rate is also correspondingly stored in the directory of the current level. Therefore, the target point clouds stored in the hierarchy where each node is located can be obtained based on the information of the storage position of each node recorded by the data index structure, and the point clouds to be displayed and needing to be loaded and displayed are selected from the target point clouds.
Specifically, when the observation viewpoint is located outside a target parent node in the octree structure data and the distance between the observation viewpoint and the center point of the outer bounding box of the octree cube of the original point cloud is smaller than a first threshold and larger than a second threshold, the target point cloud corresponding to the octree structure data hierarchy where the target parent node is located in the external memory is determined as the point cloud to be displayed. And traversing child nodes under the target parent node when the observation viewpoint is positioned outside the octree target parent node and the distance between the observation viewpoint and the central point of the outer bounding box of the octree square of the original point cloud is smaller than a second threshold value, and determining the target point cloud corresponding to the octree structure data level where the target child node is positioned in the external memory as the point cloud to be displayed if the distance between the center of the target child node and the observation viewpoint is smaller than a third threshold value.
And when the distance between the observation viewpoint and the center point of the external bounding box of the octree cube of the original point cloud is smaller than a first threshold and larger than a second threshold, all the father nodes which do not surround the observation viewpoint are the target father nodes.
Taking fig. 2 as an example, it is assumed that the target parent node is a node 210, a hierarchy of the node 210 in the octree structure data is a first hierarchy, a target point cloud corresponding to the first hierarchy is a point cloud obtained by sampling a leaf node of the first hierarchy, and if the leaf node of the first hierarchy is the node 210 and the node 280, the target point cloud corresponding to the first hierarchy is a point cloud obtained by sampling the node 210 and the node 280, and the target point cloud has been acquired and stored in the sampling stage in the scheme described in the above embodiment.
Further, with the continuous movement of the observation viewpoint, if the distance between the center of the target sub-node and the observation viewpoint is greater than or equal to a third threshold, the target point cloud corresponding to the octree structure data hierarchy where the target sub-node is located is unloaded from the memory to release the memory space.
Furthermore, the thread responsible for loading and displaying the point cloud to be displayed and the thread responsible for determining the point cloud to be displayed are two different threads. When the point cloud data is drawn by using an OpenGL graphic rendering language, another thread is developed to judge in advance which multi-resolution point cloud data needs to be loaded from an external memory or unloaded from an internal memory, so that the efficiency of internal and external memory scheduling can be improved on the premise of not influencing the drawing thread, and smooth display and scheduling of massive point clouds are realized.
The internal and external memory scheduling technology is a technology in which a part of data to be processed by a computer is stored in an internal memory, and the other part of the data is stored in an external storage device and is scheduled through I/O. This technique is a common technique when the processing data capacity is larger than the computer memory. According to the technical scheme, on the basis of establishing multi-resolution LOD point cloud data under an octree data index structure, the octree data index structure of the point cloud is stored in an internal memory through a multi-resolution point cloud internal and external memory scheduling strategy based on an observation view point, the multi-resolution data of the point cloud is stored in an external storage device, and the multi-resolution point clouds which need to be loaded and displayed currently are determined according to the position relation between the observation view point and nodes in the octree data structure.
The technical scheme of the embodiment can be applied to an automatic driving simulation platform, octree grid partitioning is carried out on original point cloud according to a depth threshold, the established octree structure data is stored in an external memory, leaf nodes are sampled layer by layer from the bottom layer to the top layer by a random sampling method (namely multi-resolution point cloud data are constructed from sub-nodes to root nodes), octree multi-resolution LOD data of the point cloud are obtained and are correspondingly stored in a hierarchy directory of the octree structure data, and finally internal and external memory scheduling is carried out on the multi-resolution LOD data of the point cloud based on an observation view point, so that smooth display of massive point cloud is realized.
The following is an embodiment of the point cloud data screening apparatus provided in the embodiments of the present invention, and the apparatus and the point cloud data screening method of each embodiment belong to the same inventive concept, and details that are not described in detail in the embodiments of the point cloud data screening apparatus may refer to the embodiments of the point cloud data screening method.
Example four
Fig. 5 is a schematic structural diagram of a point cloud data screening apparatus provided in the fourth embodiment of the present invention, where the apparatus specifically includes: a subdivision module 510 and a sampling module 520.
The subdivision module 510 is configured to perform mesh subdivision on an original point cloud to obtain octree structure data corresponding to the original point cloud; and the sampling module 520 is configured to sample point clouds in leaf nodes of each level in the octree structure data to obtain target point clouds.
Further, the dividing module 510 includes:
the system comprises an establishing unit, a processing unit and a processing unit, wherein the establishing unit is used for establishing an octree cube external bounding box of an original point cloud and storing the original point cloud under a root directory;
the subdivision unit is used for carrying out grid subdivision on the external bounding box of the octree square according to a grid subdivision convergence condition and storing sub-nodes containing point clouds based on the directory hierarchy of the sub-nodes;
and the serialization unit is used for serializing the point cloud data in the octree leaf nodes into a set data format.
Further, the establishing unit includes:
a determining subunit, configured to determine a minimum outer bounding box of the original point cloud;
and the establishing subunit is used for establishing the external bounding box of the octree cube of the original point cloud by taking the center of the minimum external bounding box as the center of the external bounding box of the octree cube and taking the maximum side length of the minimum external bounding box as the side length of the external bounding box of the octree cube.
Further, the sampling module 520 includes:
the sampling unit is used for sampling leaf nodes of the current hierarchy according to a set sampling rate aiming at nodes of each hierarchy in the octree structure data, and storing target point clouds obtained by sampling in a directory of the current hierarchy;
the traversal unit is used for traversing sub-nodes of non-leaf nodes of the current level, sampling leaf nodes under the sub-nodes according to a set sampling rate, and storing target point clouds obtained by sampling in a directory of the level where the sub-nodes are located; and executing the traversal operation of the sub-nodes aiming at the non-leaf nodes under the sub-nodes until the lowest level of the octree structure data is reached.
Further, the sampling module 520 further includes:
the determining unit is used for determining the set sampling rate based on the hierarchy of the sampled leaf nodes in the octree structure data and the sum of the number of root leaf nodes and cotyledon nodes in the octree structure data;
the root and leaf nodes comprise leaf nodes obtained by performing primary grid subdivision on an octal tree square external bounding box based on original point cloud, and the cotyledon nodes comprise leaf nodes under child nodes of which father nodes are non-leaf nodes.
Further, the determining unit is specifically configured to:
the set sampling rate is determined according to the following formula: SamoleRatio ═ samplesize (vector) -Depthcurrent-1;
wherein, SamoleRatio represents the set sampling rate, samplesize (vector) represents the sum of the number of root leaf nodes and child leaf nodes in the octree structure data, and DepthCurrent represents the level of the currently sampled leaf node in the octree structure data.
Further, the apparatus further comprises: the establishing module is used for carrying out grid subdivision on the original point cloud, obtaining octree structure data corresponding to the original point cloud and establishing a data index structure of the octree structure data.
Further, the apparatus further comprises:
a position relation determining module, configured to determine a position relation between an observation viewpoint and each node according to a spatial position region of each node in the octree structure data recorded by a data index structure of the octree structure data;
the point cloud to be displayed determining module is used for determining the point cloud to be displayed which needs to be loaded from the external memory to the internal memory according to the position relation;
the loading module is used for acquiring the point cloud to be displayed from the target point cloud according to the storage position information of the target point cloud recorded by the data index structure in an external memory, and loading and displaying the point cloud;
the target point cloud is stored in an external memory, and the data index structure is stored in the internal memory.
Further, the to-be-displayed point cloud determining module is specifically configured to:
when an observation viewpoint is positioned outside a target parent node in octree structure data and the distance between the observation viewpoint and the center point of an outer bounding box of an octree cube of an original point cloud is smaller than a first threshold value and larger than a second threshold value, determining the target point cloud corresponding to the octree structure data hierarchy where the target parent node is positioned in an external memory as the point cloud to be displayed;
and traversing child nodes under the target parent node when the observation viewpoint is positioned outside the octree target parent node and the distance between the observation viewpoint and the central point of the outer bounding box of the octree square of the original point cloud is smaller than a second threshold value, and determining the target point cloud corresponding to the octree structure data level where the target child node is positioned in the external memory as the point cloud to be displayed if the distance between the center of the target child node and the observation viewpoint is smaller than a third threshold value.
Further, the apparatus further comprises:
and the unloading module is used for unloading the target point cloud corresponding to the octree structure data hierarchy where the target sub-node is located from the memory if the distance between the center of the target sub-node and the observation viewpoint is greater than or equal to a third threshold value.
Furthermore, the thread responsible for loading and displaying the point cloud to be displayed and the thread responsible for determining the point cloud to be displayed are two different threads.
The technical scheme of the embodiment can be applied to an automatic driving simulation platform, octree grid partitioning is carried out on original point cloud according to a depth threshold, the established octree structure data is stored in an external memory, leaf nodes are sampled layer by layer from the bottom layer to the top layer by a random sampling method (namely multi-resolution point cloud data are constructed from sub-nodes to root nodes), octree multi-resolution LOD data of the point cloud are obtained and are correspondingly stored in a hierarchy directory of the octree structure data, and finally internal and external memory scheduling is carried out on the multi-resolution LOD data of the point cloud based on an observation view point, so that smooth display of massive point cloud is realized.
The point cloud data screening device provided by the embodiment of the invention can execute the point cloud data screening method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the point cloud data screening method.
EXAMPLE five
Fig. 6 is a schematic structural diagram of an apparatus according to a fourth embodiment of the present invention. Fig. 6 illustrates a block diagram of an exemplary device 12 suitable for use in implementing embodiments of the present invention. The device 12 shown in fig. 6 is only an example and should not bring any limitations to the functionality and scope of use of the embodiments of the present invention.
As shown in FIG. 6, device 12 is in the form of a general purpose computing device. The components of device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. Device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, and commonly referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. The system memory 28 may include at least one program product having a set of program modules (e.g., a segmentation module 510 and a sampling module 520 in a point cloud data screening device) configured to perform the functions of embodiments of the present invention.
A program/utility 40 having a set of program modules 42 (e.g., a segmentation module 510 and a sampling module 520 in a point cloud data screening device) may be stored, for example, in the system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may include an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The processing unit 16 executes various functional applications and point cloud data screening by running a program stored in the system memory 28, for example, implementing the steps of a point cloud data screening method provided by the embodiment of the present invention, the method includes:
carrying out grid subdivision on the original point cloud to obtain octree structure data corresponding to the original point cloud;
and respectively sampling point clouds in leaf nodes of each level in the octree structure data to obtain target point clouds.
Of course, those skilled in the art can understand that the processor may also implement the technical solution of the point cloud data screening method provided by any embodiment of the present invention.
EXAMPLE six
The sixth embodiment provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the point cloud data screening method provided in any embodiment of the present invention, where the method includes:
carrying out grid subdivision on the original point cloud to obtain octree structure data corresponding to the original point cloud;
and respectively sampling point clouds in leaf nodes of each level in the octree structure data to obtain target point clouds.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer-readable storage medium may be, for example but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It will be understood by those skilled in the art that the modules or steps of the invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of computing devices, and optionally they may be implemented by program code executable by a computing device, such that it may be stored in a memory device and executed by a computing device, or it may be separately fabricated into various integrated circuit modules, or it may be fabricated by fabricating a plurality of modules or steps thereof into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (13)
1. A point cloud data screening method is characterized by comprising the following steps:
carrying out grid subdivision on the original point cloud to obtain octree structure data corresponding to the original point cloud;
and respectively sampling point clouds in leaf nodes of each level in the octree structure data to obtain target point clouds.
2. The method of claim 1, wherein the mesh subdivision of the original point cloud to obtain octree structure data corresponding to the original point cloud comprises:
establishing an octree square external bounding box of the original point cloud, and storing the original point cloud under a root directory;
according to the grid subdivision convergence condition, carrying out grid subdivision on the external bounding box of the octree cube, and storing sub-nodes containing point clouds based on the directory hierarchy where the sub-nodes are located;
and sequencing the point cloud data in the octree leaf nodes into a set data format.
3. The method of claim 2, wherein the creating an octree cube outer bounding box of the original point cloud comprises:
determining a minimum outer bounding box of the original point cloud;
and establishing the external bounding box of the octree cube of the original point cloud by taking the center of the minimum external bounding box as the center of the external bounding box of the octree cube and taking the maximum side length of the minimum external bounding box as the side length of the external bounding box of the octree cube.
4. The method according to any one of claims 1 to 3, wherein the separately sampling point clouds in leaf nodes of each level in the octree structure data to obtain a target point cloud comprises:
sampling leaf nodes of the current hierarchy according to a set sampling rate aiming at nodes of each hierarchy in the octree structure data, and storing target point clouds obtained by sampling in a directory of the current hierarchy;
traversing sub-nodes of non-leaf nodes of the current level, sampling leaf nodes under the sub-nodes according to a set sampling rate, and storing target point clouds obtained by sampling in a directory of the level where the sub-nodes are located;
and executing the traversal operation of the sub-nodes aiming at the non-leaf nodes under the sub-nodes until the lowest level of the octree structure data is reached.
5. The method of claim 4, wherein the determining of the set sampling rate comprises:
determining the set sampling rate based on the hierarchy of the sampled leaf nodes in the octree structure data and the sum of the number of root leaf nodes and cotyledon nodes in the octree structure data;
the root and leaf nodes comprise leaf nodes obtained by performing primary grid subdivision on an octal tree square external bounding box based on original point cloud, and the cotyledon nodes comprise leaf nodes under child nodes of which father nodes are non-leaf nodes.
6. The method of claim 5, wherein determining the set sampling rate based on a hierarchy of sampled leaf nodes in the octree structure data and a sum of a number of root leaf nodes and cotyledon nodes in the octree structure data comprises:
the set sampling rate is determined according to the following formula: SamoleRatio ═ samplesize (vector) -Depthcurrent-1;
wherein, SamoleRatio represents the set sampling rate, samplesize (vector) represents the sum of the number of root leaf nodes and child leaf nodes in the octree structure data, and DepthCurrent represents the level of the currently sampled leaf node in the octree structure data.
7. The method according to any one of claims 1 to 3, wherein when performing mesh subdivision on the original point cloud to obtain octree structure data corresponding to the original point cloud, the method further comprises: and establishing a data index structure of the octree structure data.
8. The method of claim 7, further comprising:
determining the position relation between an observation viewpoint and each node according to the spatial position area of each node in the octree structure data recorded by the data index structure;
determining the point cloud to be displayed which needs to be loaded from an external memory to an internal memory according to the position relation;
acquiring the point cloud to be displayed from the target point cloud according to the storage position information of the target point cloud recorded by the data index structure in an external memory, and loading and displaying the point cloud;
the target point cloud is stored in an external memory, and the data index structure is stored in the internal memory.
9. The method according to claim 8, wherein the determining the point cloud to be displayed, which needs to be loaded from an external memory to an internal memory, according to the position relationship comprises:
when an observation viewpoint is positioned outside a target parent node in octree structure data and the distance between the observation viewpoint and the center point of an outer bounding box of an octree cube of an original point cloud is smaller than a first threshold value and larger than a second threshold value, determining the target point cloud corresponding to the octree structure data hierarchy where the target parent node is positioned in an external memory as the point cloud to be displayed;
and traversing child nodes under the target parent node when the observation viewpoint is positioned outside the octree target parent node and the distance between the observation viewpoint and the central point of the outer bounding box of the octree square of the original point cloud is smaller than a second threshold value, and determining the target point cloud corresponding to the octree structure data level where the target child node is positioned in the external memory as the point cloud to be displayed if the distance between the center of the target child node and the observation viewpoint is smaller than a third threshold value.
10. The method of claim 9, further comprising:
and if the distance between the center of the target sub-node and the observation viewpoint is larger than or equal to a third threshold value, unloading the target point cloud corresponding to the octree structure data hierarchy where the target sub-node is located from the memory.
11. A point cloud data screening device, comprising:
the subdivision module is used for carrying out grid subdivision on the original point cloud to obtain octree structure data corresponding to the original point cloud;
and the sampling module is used for respectively sampling point clouds in leaf nodes of each level in the octree structure data to obtain target point clouds.
12. An apparatus, characterized in that the apparatus comprises:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the point cloud data screening method steps of any of claims 1-10.
13. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the point cloud data screening method steps of any one of claims 1 to 10.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010256426.9A CN113496543A (en) | 2020-04-02 | 2020-04-02 | Point cloud data screening method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010256426.9A CN113496543A (en) | 2020-04-02 | 2020-04-02 | Point cloud data screening method and device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113496543A true CN113496543A (en) | 2021-10-12 |
Family
ID=77994166
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010256426.9A Pending CN113496543A (en) | 2020-04-02 | 2020-04-02 | Point cloud data screening method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113496543A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113722415A (en) * | 2021-11-04 | 2021-11-30 | 北京四维图新科技股份有限公司 | Point cloud data processing method and device, electronic equipment and storage medium |
| CN114399592A (en) * | 2021-12-24 | 2022-04-26 | 华北电力科学研究院有限责任公司 | Point cloud space data processing method and device |
| CN115080680A (en) * | 2022-07-19 | 2022-09-20 | 高德软件有限公司 | Laser point cloud data processing method, device and equipment for high-precision map |
| WO2025059878A1 (en) * | 2023-09-19 | 2025-03-27 | 华为技术有限公司 | Point cloud data transmission method and apparatus, and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090048782A1 (en) * | 2007-08-17 | 2009-02-19 | Hong Fu Jin Precision Industry (Shenzhen) Co., Ltd. | System and method for filtering a point cloud |
| CN105335997A (en) * | 2015-10-10 | 2016-02-17 | 燕山大学 | Complex structure point cloud processing algorithm bases on Poisson reconstruction |
| CN106951643A (en) * | 2017-03-22 | 2017-07-14 | 广东工业大学 | A kind of complicated outside plate three dimensional point cloud compressing method of hull and device |
-
2020
- 2020-04-02 CN CN202010256426.9A patent/CN113496543A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090048782A1 (en) * | 2007-08-17 | 2009-02-19 | Hong Fu Jin Precision Industry (Shenzhen) Co., Ltd. | System and method for filtering a point cloud |
| CN105335997A (en) * | 2015-10-10 | 2016-02-17 | 燕山大学 | Complex structure point cloud processing algorithm bases on Poisson reconstruction |
| CN106951643A (en) * | 2017-03-22 | 2017-07-14 | 广东工业大学 | A kind of complicated outside plate three dimensional point cloud compressing method of hull and device |
Non-Patent Citations (1)
| Title |
|---|
| 杨振发;万刚;李锋;李滨;: "多分辨率LOD的海量点云显示技术研究", 地理空间信息, vol. 14, no. 10, pages 22 - 25 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113722415A (en) * | 2021-11-04 | 2021-11-30 | 北京四维图新科技股份有限公司 | Point cloud data processing method and device, electronic equipment and storage medium |
| CN114399592A (en) * | 2021-12-24 | 2022-04-26 | 华北电力科学研究院有限责任公司 | Point cloud space data processing method and device |
| CN115080680A (en) * | 2022-07-19 | 2022-09-20 | 高德软件有限公司 | Laser point cloud data processing method, device and equipment for high-precision map |
| WO2025059878A1 (en) * | 2023-09-19 | 2025-03-27 | 华为技术有限公司 | Point cloud data transmission method and apparatus, and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113496543A (en) | Point cloud data screening method and device, electronic equipment and storage medium | |
| WO2020098531A1 (en) | Object loading method, device, storage medium, and electronic device | |
| CN110738721B (en) | Three-dimensional scene rendering acceleration method and system based on video geometric analysis | |
| US10585948B2 (en) | Method and device for constructing spatial index of massive point cloud data | |
| CN111862292B (en) | Data rendering method and device for transmission line corridor and computer equipment | |
| CN111467806B (en) | Method, device, medium and electronic equipment for generating resources in game scene | |
| KR101546703B1 (en) | System for processing massive bim data of building | |
| CN108170807B (en) | Map data processing method, map drawing method, map data processing device, map drawing device, map data processing equipment and storage medium | |
| CN113066160B (en) | Method for generating scene data of indoor mobile robot | |
| US20050174351A1 (en) | Method and apparatus for large-scale two-dimensional mapping | |
| CN114066917B (en) | Cleaning method, cleaning device, electronic equipment and storage medium | |
| US20220375186A1 (en) | Method and apparatus for generating bounding box, device and storage medium | |
| CN116502303A (en) | A BIM model visualization method based on scene-level instance information enhancement | |
| CN116030176A (en) | Three-dimensional model rendering method, device, equipment and medium based on cluster division | |
| CN119067856A (en) | A geographic vector data rendering method, storage medium, device and computer program product based on WebGPU | |
| CN112023400B (en) | Altitude map generation method, device, equipment and storage medium | |
| CN111179414B (en) | Terrain LOD generation method | |
| CN116129025A (en) | Three-dimensional model rendering method, device, equipment and medium based on model depth | |
| CN113778961B (en) | Production management method, device and system for CIM model data | |
| US20230196674A1 (en) | Method and apparatus for processing three dimentional graphic data, device, storage medium and product | |
| CN111790152B (en) | Method and device for loading objects in scene, storage medium and electronic equipment | |
| CN114648607B (en) | Inclined three-dimensional model reconstruction and dynamic scheduling method based on CAD platform | |
| CN111870954B (en) | Altitude map generation method, device, equipment and storage medium | |
| CN115239895B (en) | Mass data loading and optimal rendering method for GIS water environment 3D map | |
| CN113538478B (en) | Box boundary extraction method, system, equipment and storage medium based on image |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20211012 |
|
| RJ01 | Rejection of invention patent application after publication |