CN112650790B - Target point cloud plane determining method and device, electronic equipment and storage medium - Google Patents
Target point cloud plane determining method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN112650790B CN112650790B CN202011582206.1A CN202011582206A CN112650790B CN 112650790 B CN112650790 B CN 112650790B CN 202011582206 A CN202011582206 A CN 202011582206A CN 112650790 B CN112650790 B CN 112650790B
- Authority
- CN
- China
- Prior art keywords
- point cloud
- cloud data
- plane
- distance
- target
- 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.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
Abstract
The present disclosure relates to a target point cloud plane determining method, apparatus, electronic device and storage medium, including: selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; acquiring second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane; when the distance is larger than a preset distance, returning to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; and when the distance is smaller than or equal to the preset distance, determining the target point cloud plane according to the point cloud plane and the residual point cloud data. And removing a part of the point cloud plane with lower probability of being the target point cloud plane through the distance between the second point cloud data and the estimated point cloud plane, and not calculating the distance between the rest point cloud data and the point cloud plane, so as to reduce the calculated amount, thereby improving the integral calculation efficiency of the algorithm.
Description
Technical Field
The disclosure relates to the technical field of point cloud data processing, in particular to a target point cloud plane determining method, a device, electronic equipment and a storage medium.
Background
The random sampling consistency algorithm, namely the RANSAC algorithm, can be used for estimating the point cloud plane, when the RANSAC algorithm is used for estimating the point cloud plane, point cloud data are generally extracted randomly to estimate one point cloud plane, the distance between other point cloud data and the point cloud plane is calculated to determine whether the point cloud data are on the point cloud plane, the process is iterated continuously, and the plane with the largest point cloud data is found as the final plane. Then, in the algorithm, the distances between all the point cloud data and the point cloud plane need to be calculated, so that the calculation amount of the whole algorithm is large and the efficiency is low.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
In a first aspect, the present disclosure provides a target point cloud plane determination method, the method including: selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; acquiring second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane, wherein the second point cloud data is one point cloud data randomly selected from the point cloud data except the first point cloud data in the point cloud data set; when the distance is larger than a preset distance, returning to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; and when the distance is smaller than or equal to the preset distance, determining the target point cloud plane according to the point cloud plane and residual point cloud data, wherein the residual point cloud data are point cloud data except for first point cloud data and second point cloud data in the point cloud data set.
In a second aspect, the present disclosure provides a target point cloud plane determination apparatus, the apparatus comprising: the estimating module is used for selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; the acquisition module is used for acquiring second point cloud data, calculating the distance between the second point cloud data and the point cloud plane, wherein the second point cloud data is one point cloud data selected randomly from the point cloud data except the first point cloud data in the point cloud data set; the iteration module is used for returning to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane when the distance is larger than a preset distance; the determining module is configured to determine, when the distance is less than or equal to the preset distance, the target point cloud plane according to the point cloud plane and remaining point cloud data, where the remaining point cloud data is point cloud data except for first point cloud data and second point cloud data in the point cloud data set.
In a third aspect, the present disclosure provides a non-transitory computer-readable storage medium having stored thereon a computer program, wherein the above-described target point cloud plane determination method is implemented when the program is executed by a processing device.
In a fourth aspect, the present disclosure provides an electronic device, where the electronic device includes a storage device and a processing device, where the storage device stores a computer program, and the processing device implements the above-mentioned target point cloud plane determination method when executing the computer program in the storage device.
Through the technical scheme, a preset number of first point cloud data estimation point cloud planes are selected from the point cloud data set; randomly extracting one point cloud data from the point cloud data except the first point cloud data in the point cloud data set to obtain second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane; and when the distance is smaller than or equal to the preset distance, determining the target point cloud plane according to the point cloud plane and the residual point cloud data. And removing a part of the point cloud plane with lower probability of being the target point cloud plane through the distance between the second point cloud data and the estimated point cloud plane, and not calculating the distance between the rest point cloud data and the point cloud plane, so as to reduce the calculated amount, thereby improving the integral calculation efficiency of the algorithm.
Additional features and advantages of the present disclosure will be set forth in the detailed description which follows.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale. In the drawings:
fig. 1 is a flowchart of a target point cloud plane determination method according to an embodiment of the present application.
Fig. 2 is a flowchart of a target point cloud plane determination method according to another embodiment of the present application.
Fig. 3 is a flowchart of a target point cloud plane determination method according to still another embodiment of the present application.
Fig. 4 is a functional block diagram of a target point cloud plane determination device according to an embodiment of the present application.
Fig. 5 shows a schematic diagram of an electronic device suitable for implementing an embodiment of the application.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
Referring to fig. 1, an embodiment of the present application provides a method for determining a cloud plane of a target point, which may be applied to an electronic device, where the electronic device may be an intelligent device, or a local service, a cloud server, or the like.
Step S101: and selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane.
The point cloud data set is a set containing a plurality of point cloud data. In the point cloud data set, a preset number of point cloud data can be randomly selected as first point cloud data, and a point cloud plane is estimated by using the randomly selected first point cloud data.
The randomly selecting the preset number of first point cloud data may be randomly selecting 3 first point cloud data from the point cloud data set, and a point cloud plane may be estimated by using the 3 first point cloud data.
Specifically, a point cloud plane is estimated by using 3 point cloud data, which may be that a reference coordinate system is established, the coordinates of the first point cloud data are determined based on the reference coordinates, and then a corresponding plane equation is determined according to the coordinates of the first point cloud data.
Step S102: and acquiring second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane.
After estimating one point cloud plane using the randomly extracted 3 point cloud data, the second point cloud data may be continuously acquired. The second point cloud data is one point cloud data selected randomly from the point cloud data except the first point cloud data in the point cloud data set. That is, the second point cloud data is point cloud data different from the first point cloud data.
After the second point cloud data is obtained, a distance between the second point cloud data and the point cloud plane may be calculated, and whether the second point cloud data belongs to the point cloud plane is further determined according to the distance. The calculating the distance between the second point cloud data and the point cloud plane may be determining the coordinate of the second point cloud data in the reference coordinate system, and calculating the distance between the second point cloud data and the point cloud plane according to the coordinate of the second point cloud data and a plane equation.
Determining whether the second point cloud data belongs to the point cloud plane may be comparing the distance with a preset distance, if the distance is greater than the preset distance, executing step S101, and if the distance is less than or equal to the preset distance, executing step S103.
Step S103: and when the distance is larger than the preset distance, returning to execute the step of selecting the preset number of first point cloud data from the point cloud data set to estimate the point cloud plane.
After the distance between the second point cloud data and the point cloud plane is calculated, the distance can be compared with a preset distance. The preset distance is a preset critical value indicating a distance that the point cloud data belongs to a point cloud plane, that is, if the distance between the point cloud data and the point cloud plane is greater than the preset distance, the point cloud data belongs to the point cloud plane; and if the distance between the point cloud data and the point cloud plane is smaller than or equal to the preset distance, indicating that the point cloud data does not belong to the point cloud plane.
For example, if the preset distance is X, the distance between the point cloud data a and the point cloud plane is X1, and the distance between the point cloud data B and the point cloud plane is X2, where X1< X2, then the point cloud data a may be considered to belong to the point cloud plane, and the point cloud data B may not belong to the point cloud plane.
Therefore, when the distance between the second point cloud data and the point cloud plane is calculated to be greater than the preset distance, the second point cloud data may be considered not to belong to the point cloud plane, and since the target point cloud plane to be determined is the point cloud plane containing the most point cloud data, the probability that the point cloud plane is the target point cloud plane is considered to be very small, the point cloud plane may be directly removed, that is, the point cloud plane is not used to execute the subsequent step, but the step S101 is executed again, and the preset number of first point cloud data are selected from the point cloud data set again to estimate the point cloud plane.
Step S104: and when the distance is smaller than or equal to the preset distance, determining the target point cloud plane according to the point cloud plane and the residual point cloud data.
The target point cloud plane is the point cloud plane containing the most point cloud data in the point cloud data set. If the distance is smaller than or equal to the preset distance, the second point cloud data can be considered to belong to the point cloud plane, and the probability that the plane is the target point cloud plane is high, and the subsequent steps can be continuously executed by using the point cloud plane to determine the target point cloud plane.
When determining the target point cloud plane, it may be determined according to the point cloud plane and the remaining point cloud data. The rest point cloud data are point cloud data except the first point cloud data and the second point cloud data in the point cloud data set.
It may be appreciated that, a preset number of first point cloud data and one second point cloud data are randomly extracted from the point cloud data set, and then the point cloud data in the point cloud data set except for the first point cloud data and the second point cloud data are the remaining point cloud data. For example, the point cloud data set includes six point cloud data of a, B, C, D, E, F, and G, wherein the first randomly extracted point cloud data is a, C, G, and the second randomly extracted point cloud data is E, and then the remaining point cloud data is B, D, F.
The determining the target point cloud plane by using the point cloud plane and the residual point cloud data may be calculating a distance between each residual point cloud data and the point cloud plane, counting the number of the residual point cloud data that belong to the point cloud plane as a target number, and performing the above steps in a recycling manner, namely, performing random extraction of a preset number of first point cloud data from the point cloud data set to estimate the point cloud plane, then random extraction of second point cloud data, calculating a distance between the second point cloud data and the point cloud plane, and calculating a distance between each residual point cloud data and the point cloud plane when the distance is less than or equal to the preset distance. And performing multiple iterations to obtain multiple point cloud planes and target numbers corresponding to the point cloud planes, and determining the point cloud plane corresponding to the maximum value of the target numbers as the target point cloud plane.
The preset distance is a value that can be set by a person skilled in the art according to actual needs, and is not specifically limited herein.
According to the target point cloud plane determining method provided by the application, a preset number of first point cloud data are selected from the point cloud data set to estimate the point cloud plane; acquiring second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane; when the distance is larger than a preset distance, returning to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; and when the distance is smaller than or equal to the preset distance, determining the target point cloud plane according to the point cloud plane and the residual point cloud data. The second point cloud data are randomly extracted, the distance between the second point cloud data and the point cloud plane is calculated, the plane with lower probability of being the target point cloud plane is removed, a large amount of calculation is avoided, the iterative process can be quickened, and the overall efficiency of the algorithm is improved.
Referring to fig. 2, another embodiment of the present application provides a method for determining a target point cloud plane, and a process for determining the target point cloud plane according to the target point cloud plane and the remaining point cloud data is described with emphasis on the above embodiment. In particular, the method may comprise the following steps.
Step S201: and selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane.
Step S202: and acquiring second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane.
Step S203: and when the distance is larger than the preset distance, returning to execute the step of selecting the preset number of first point cloud data from the point cloud data set to estimate the point cloud plane.
The steps S201 to S203 may refer to the corresponding parts of the foregoing embodiments, and are not described herein.
Step S204: and when the distance is smaller than or equal to the preset distance, acquiring the distance between each piece of residual point cloud data and the point cloud plane, and acquiring the target quantity of the residual point cloud data, wherein the distance is smaller than or equal to the preset distance.
And when the distance between the second point cloud data and the point cloud plane is smaller than the preset distance, executing a quantity acquisition operation on the point cloud plane. Specifically, the distance between each piece of remaining point cloud data and the point cloud plane may be obtained, that is, the distance between each piece of remaining point cloud data and the point cloud plane may be calculated. After the distance between each piece of residual point cloud data and the point cloud plane is calculated, comparing each obtained distance with the preset distance, and obtaining the number of the distances smaller than or equal to the preset distance as the target number corresponding to the point cloud plane.
In the previous example, the point cloud data set includes six point cloud data of a, B, C, D, E, F, and G, wherein the first randomly extracted point cloud data is a, C, G, and the second randomly extracted point cloud data is E, and then the remaining point cloud data is B, D, and F. Assume that a point cloud plane determined from first point cloud data is S ACG Respectively calculating point cloud data B, D and F and the point cloud plane S ACG Is X1, X2, X1, wherein X1<X<X2 indicates that the number of the point cloud data with the distance smaller than the preset distance is 2, thus the point cloud plane S ACG The corresponding target number is 2.
Step S205: and determining a target point cloud plane according to the target quantity.
After the target number corresponding to the point cloud plane is obtained, a step of selecting a preset number of first point cloud data from the point cloud data set to estimate the point cloud plane for multiple times can be returned to obtain multiple point cloud planes, and for each point cloud plane with a distance from the second point cloud data being smaller than or equal to the preset distance, a number obtaining operation is respectively performed to obtain the target number corresponding to each point cloud plane.
That is, after the target number corresponding to the current point cloud plane is obtained, selecting a preset number of first point cloud data from the point cloud data set may be performed in a return manner, and one point cloud plane may be estimated by using the first point cloud data selected at this time. And after the point cloud plane is obtained, continuously obtaining second point cloud data, calculating the distance between the second point cloud data and the point cloud plane, and obtaining the target number corresponding to the point cloud plane when the distance is smaller than or equal to the preset distance. The above process is continuously and circularly executed, and a plurality of point cloud planes and target numbers corresponding to the plurality of point cloud planes one by one can be obtained, so that the maximum target number can be determined in the plurality of target numbers, and the point cloud plane corresponding to the maximum target number is determined as the target plane.
In some embodiments, the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane may be performed in parallel, and the number acquisition operation may be performed separately for each of the point cloud planes having a distance from the second point cloud data less than or equal to the preset distance. That is, when calculating the distance between each remaining point and the point cloud plane and obtaining the target number of the point cloud points with the distance smaller than or equal to the preset distance, selecting a preset number of first point cloud data from the point cloud data set to estimate the point cloud plane may be performed simultaneously, so as to obtain a plurality of point cloud planes, and performing a number obtaining operation for each point cloud plane satisfying the condition that the distance is smaller than or equal to the preset distance.
After the target number corresponding to each point cloud plane is obtained, the target point cloud plane may be determined according to the target number, for example, the correspondence between the point cloud plane and the target number is shown in table 1.
TABLE 1
| Point cloud plane | Point cloud plane 1 | Point cloud plane 2 | Point cloud plane 3 | Point cloud plane 4 |
| Target quantity | Y1 | Y2 | Y3 | Y4 |
In table 1, the number of targets corresponding to the point cloud plane 1 is Y1, the number of targets corresponding to the point cloud plane 2 is Y2, the number of targets corresponding to the point cloud plane 3 is Y3, the number of targets corresponding to the point cloud plane 4 is Y4, and assuming that Y2> Y1> Y4> Y3, that is, the maximum number of targets is Y2, it can be known from table 1 that the point cloud plane corresponding to the target number Y2 is the point cloud plane 2, and therefore, the point cloud plane 2 can be determined as the target point cloud plane.
It should be noted that, in the above step, after the step S202 is performed, if the distance is greater than the preset distance, the step S203 is performed, and if the distance is less than or equal to the preset distance, the steps S204 and S205 are performed.
According to the target point cloud plane determining method provided by the embodiment of the application, the distance between the second point cloud data and the point cloud plane estimated by the first point cloud data is calculated, and when the distance is greater than the preset distance, the subsequent calculation is not performed by using the point cloud plane, so that the calculated amount is reduced; when the distance is smaller than or equal to the preset distance, calculating the distance between the point cloud plane and each piece of residual point cloud data, obtaining the target number corresponding to the point cloud plane, returning to execute the step of selecting the first point cloud data to estimate the point cloud plane so as to obtain a plurality of point cloud planes, further obtaining the target number corresponding to each point cloud plane, and determining the point cloud plane corresponding to the maximum target number as the target point cloud plane. And the plane with lower probability of being the target point cloud plane is removed through the distance between the second point cloud data extracted randomly and the point cloud plane, so that a large number of calculations are avoided, the iterative process can be quickened, and the overall efficiency of the algorithm is improved.
Referring to fig. 3, another embodiment of the present application provides a method for determining a target point cloud plane, and the overall process of determining the target point cloud plane is described with emphasis on the foregoing embodiments. In particular, the method may comprise the following steps.
Step S301: and selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane.
Step S302: and acquiring second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane.
The steps S301 to S302 may refer to the corresponding parts of the foregoing embodiments, and are not described herein.
Step S303: judging whether the distance is larger than a preset distance or not; if yes, go to step S301; if not, go to step S304.
When the distance between the second point cloud data and the point cloud plane is obtained, whether the distance is larger than a preset distance or not may be determined, if the distance is determined to be larger than the preset distance, step S301 is executed, and if the distance is determined to be smaller than or equal to the preset distance, step S304 is executed. The relevant content of this step can be referred to the corresponding parts of the previous embodiments.
Step S304: and acquiring the distance between each piece of residual point cloud data and the point cloud plane, and acquiring the target quantity of the residual point cloud data with the distance smaller than or equal to the preset distance.
Step S305: and obtaining the target quantity corresponding to each point cloud plane.
And acquiring the distance between each point cloud data and the point cloud plane, obtaining a plurality of distances, comparing each obtained distance with the preset distance, and acquiring the number of the distances smaller than or equal to the preset distance as the target number.
After step S304 is performed, the execution step S301 is returned, and when the execution step S301 is returned, a plurality of point cloud planes satisfying the distance being less than or equal to the preset distance may be obtained, and for each of the point cloud planes, the target number corresponding to the point cloud plane may be obtained. Therefore, the target number corresponding to each point cloud plane can be obtained.
Step S306: judging whether iteration conditions are met; if yes, go to step S307; if not, step S301 is performed.
When the target number corresponding to each point cloud plane is obtained, it may be determined whether an iteration condition is satisfied, that is, whether the loop may be ended, and if it is determined that the iteration condition is satisfied, it is indicated that the loop may be ended, and step S307 is executed. If it is determined that the iteration condition is not satisfied, indicating that the loop still needs to be continued, step S301 is executed.
In some embodiments, the preset number of times may be preset, that is, the number of times of performing the step of selecting a preset number of first point cloud data from the point cloud data set to estimate the point cloud plane. That is, the number of times of executing the step of selecting the preset number of first point cloud data from the point cloud data set to estimate the point cloud plane is recorded, and if the number of times recorded is equal to the preset number of times, indicating that the iteration condition is satisfied, the loop may be ended, and step S307 is executed; if the recorded times are smaller than or equal to the preset times, the step of selecting a preset number of first point cloud data from the point cloud data set to estimate the point cloud plane still needs to be carried out in a returning mode, wherein the step of selecting the preset number of first point cloud data from the point cloud data set does not meet the iteration condition.
In other embodiments, it may be determined whether the number of the obtained point cloud planes satisfying the distance condition is less than or equal to the preset distance condition is greater than or equal to a preset threshold. That is, a preset threshold is preset, the number of point cloud planes, in which the distance between the second point cloud data and the point cloud plane is smaller than or equal to the preset distance, is recorded, if the number is greater than or equal to the preset threshold, indicating that the iteration condition is satisfied, step S307 may be executed; if the number is smaller than the preset threshold, the iteration condition is not met, and the step of selecting the preset number of first point cloud data from the point cloud data set to estimate the point cloud plane still needs to be carried out.
It should be noted that, the preset times and the preset threshold may be an empirical value commonly used by those skilled in the art, or may be a value determined according to actual needs, which is not specifically limited in the embodiment of the present application.
Step S307: and determining the point cloud plane corresponding to the maximum target quantity as the target point cloud plane.
When a plurality of point cloud planes and a corresponding target number are acquired, a point cloud plane corresponding to a maximum value in the target number may be determined as the target point cloud plane.
According to the target point cloud plane determining method provided by the embodiment of the application, a first point cloud data is utilized to estimate a point cloud plane, the distance between a second point cloud data and the point cloud plane is calculated, when the distance is smaller than or equal to a preset distance, the distance between each piece of residual point cloud data and the point cloud plane is obtained, and the target number of the pieces of residual point cloud data, of which the distance is smaller than or equal to the preset distance, is obtained; returning to the first point cloud data to estimate the point cloud plane to obtain a plurality of point cloud planes with the distance from the second point cloud data being smaller than or equal to the preset distance; acquiring the target number corresponding to each point cloud plane; and when the iteration condition is met, determining the point cloud plane corresponding to the maximum target quantity as the target point cloud plane. The plane with lower probability of being the target point cloud plane is removed through the distance between the second point cloud data extracted randomly and the point cloud plane, so that a large number of calculations are avoided, the iterative process can be quickened, the overall efficiency of an algorithm is improved, and when iteration conditions are met, the accuracy of the determined target point cloud plane is ensured by determining the target point cloud plane.
Referring to fig. 4, a target point cloud plane determining apparatus 400 provided by an embodiment of the present application is shown, where the target point cloud plane determining apparatus 400 includes an estimation module 401, an acquisition module 402, an iteration module 403, and a determination module 404.
The estimation module 401 is configured to select a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; the obtaining module 402 is configured to obtain second point cloud data, calculate a distance between the second point cloud data and the point cloud plane, where the second point cloud data is one point cloud data selected randomly from point cloud data except the first point cloud data in the point cloud data set; the iteration module 403 is configured to return to perform a step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane when the distance is greater than a preset distance; the determining module 404 is configured to determine, when the distance is less than or equal to the preset distance, the target point cloud plane according to the point cloud plane and remaining point cloud data, where the remaining point cloud data is point cloud data other than the first point cloud data and the second point cloud data in the point cloud data set.
Further, the determining module 404 is further configured to perform, for the point cloud plane, the following number acquisition operations: obtaining the distance between each piece of residual point cloud data and the point cloud plane, and obtaining the target quantity of the residual point cloud data with the distance smaller than or equal to the preset distance; and determining a target point cloud plane according to the target quantity.
Further, the determining module 404 is further configured to return to performing the step of selecting the first point cloud data of the preset number from the point cloud data sets to estimate the point cloud plane multiple times; the number acquisition operation is respectively executed for each point cloud plane with the distance from the second point cloud data being smaller than or equal to the preset distance, so that the target number corresponding to each point cloud plane is obtained; and determining the point cloud plane corresponding to the maximum target quantity as the target point cloud plane.
Further, the determining module 404 is further configured to return to performing the step of selecting the preset number of first point cloud data from the point cloud data set for a preset number of times, or return to performing the step of selecting the preset number of first point cloud data from the point cloud data set for a plurality of times for estimating the point cloud plane until the number of the point cloud planes having a distance from the second point cloud data less than or equal to the preset distance is greater than or equal to a preset threshold.
Further, the determining module 404 is further configured to execute the returning multiple times to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane, and execute the number acquisition operation separately for each of the point cloud planes having a distance from the second point cloud data less than or equal to the preset distance.
Further, the estimation module 401 is further configured to establish a reference coordinate system, and determine coordinates of the first point cloud data based on the reference coordinate system; and determining a corresponding plane equation according to the coordinates of the first point cloud data.
Further, the obtaining module 402 is further configured to determine coordinates of the second point cloud data in the reference coordinate system; and calculating the distance between the second point cloud data and the point cloud plane according to the coordinates of the second point cloud data and the plane equation.
It should be noted that, for convenience and brevity of description, specific working processes of the apparatus and modules described above may refer to corresponding processes in the foregoing method embodiments, which are not described herein again.
Referring now to fig. 5, a schematic diagram of an electronic device 500 suitable for use in implementing embodiments of the present disclosure is shown. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 5 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 5, the electronic device 500 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 501, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
In general, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 507 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 508 including, for example, magnetic tape, hard disk, etc.; and communication means 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 shows an electronic device 500 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or from the storage means 508, or from the ROM 502. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing device 501.
It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: 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 disclosure, 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. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some implementations, the electronic device may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; acquiring second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane, wherein the second point cloud data is one point cloud data randomly selected from the point cloud data except the first point cloud data in the point cloud data set; when the distance is larger than a preset distance, returning to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; and when the distance is smaller than or equal to the preset distance, determining the target point cloud plane according to the point cloud plane and residual point cloud data, wherein the residual point cloud data are point cloud data except for first point cloud data and second point cloud data in the point cloud data set.
Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ 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 kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present disclosure may be implemented in software or hardware. The name of a module does not in some cases define the module itself.
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.
According to one or more embodiments of the present disclosure, example 1 provides a target point cloud plane determination method, the method comprising selecting a preset number of first point cloud data estimation point cloud planes from a point cloud data set; acquiring second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane, wherein the second point cloud data is one point cloud data randomly selected from the point cloud data except the first point cloud data in the point cloud data set; when the distance is larger than a preset distance, returning to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; and when the distance is smaller than or equal to the preset distance, determining the target point cloud plane according to the point cloud plane and residual point cloud data, wherein the residual point cloud data are point cloud data except for first point cloud data and second point cloud data in the point cloud data set.
In accordance with one or more embodiments of the present disclosure, example 2 provides the method of example 1, comprising: for the point cloud plane, performing the following number acquisition operations: obtaining the distance between each piece of residual point cloud data and the point cloud plane, and obtaining the target quantity of the residual point cloud data with the distance smaller than or equal to the preset distance; and determining a target point cloud plane according to the target quantity.
In accordance with one or more embodiments of the present disclosure, example 3 provides the method of example 2, comprising: returning to execute the step of selecting the preset number of first point cloud data from the point cloud data set for multiple times to estimate the point cloud plane; the performing the following number acquisition operations for the point cloud plane includes: the number acquisition operation is respectively executed for each point cloud plane with the distance from the second point cloud data being smaller than or equal to the preset distance, so that the target number corresponding to each point cloud plane is obtained; the determining the target point cloud plane according to the target number includes: and determining the point cloud plane corresponding to the maximum target quantity as the target point cloud plane.
In accordance with one or more embodiments of the present disclosure, example 4 provides the method of example 3, comprising: and returning to execute the step of selecting the preset number of first point cloud data from the point cloud data set for the preset number of times, or returning to execute the step of selecting the preset number of first point cloud data from the point cloud data set for the preset number of times for estimating the point cloud plane until the number of the point cloud planes, the distance between which is smaller than or equal to the preset distance from the second point cloud data, is larger than or equal to a preset threshold value.
Example 5 provides the method of example 3, comprising: and executing the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane in parallel for a plurality of times, and executing the number acquisition operation respectively for each point cloud plane with a distance from the second point cloud data smaller than or equal to the preset distance.
Example 6 provides the method of example 1, comprising: establishing a reference coordinate system, and determining the coordinates of the first point cloud data based on the reference coordinate system; and determining a corresponding plane equation according to the coordinates of the first point cloud data.
Example 7 provides the method of example 6, comprising: determining coordinates of the second point cloud data in the reference coordinate system; and calculating the distance between the second point cloud data and the point cloud plane according to the coordinates of the second point cloud data and the plane equation.
According to one or more embodiments of the present disclosure, example 8 provides a target point cloud plane determination apparatus, the apparatus comprising: the estimating module is used for selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; the acquisition module is used for acquiring second point cloud data, calculating the distance between the second point cloud data and the point cloud plane, wherein the second point cloud data is one point cloud data selected randomly from the point cloud data except the first point cloud data in the point cloud data set; the iteration module is used for returning to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane when the distance is larger than a preset distance; the determining module is configured to determine, when the distance is less than or equal to the preset distance, the target point cloud plane according to the point cloud plane and remaining point cloud data, where the remaining point cloud data is point cloud data except for first point cloud data and second point cloud data in the point cloud data set.
According to one or more embodiments of the present disclosure, example 9 provides a non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that the program, when executed by a processing device, implements the steps of the method of any of examples 1-7.
In accordance with one or more embodiments of the present disclosure, example 10 provides an electronic device, comprising: a storage device having a computer program stored thereon; processing means for executing the computer program in the storage means to implement the steps of the method of any one of examples 1-7.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this disclosure is not limited to the specific combinations of features described above, but also covers other embodiments which may be formed by any combination of features described above or equivalents thereof without departing from the spirit of the disclosure. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).
Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims. The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Claims (7)
1. A target point cloud plane determination method, the method comprising:
selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane;
acquiring second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane, wherein the second point cloud data is one point cloud data randomly selected from the point cloud data except the first point cloud data in the point cloud data set;
when the distance is larger than a preset distance, returning to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane;
when the distance is less than or equal to the preset distance,
for the point cloud plane, performing the following number acquisition operations: obtaining the distance between each piece of residual point cloud data and the point cloud plane, and obtaining the target number of the residual point cloud data with the distance smaller than or equal to the preset distance;
determining a target point cloud plane according to the target quantity, wherein the target point cloud plane specifically comprises: returning to execute the step of selecting the preset number of first point cloud data from the point cloud data set for a preset number of times, or returning to execute the step of selecting the preset number of first point cloud data from the point cloud data set for a plurality of times for estimating the point cloud plane until the number of the point cloud planes, the distance between which is less than or equal to the preset distance from the second point cloud data, is greater than or equal to a preset threshold; the performing the following number acquisition operations for the point cloud plane includes: the number acquisition operation is respectively executed for each point cloud plane with the distance from the second point cloud data being smaller than or equal to the preset distance, so that the target number corresponding to each point cloud plane is obtained; the determining the target point cloud plane according to the target number includes: and determining the point cloud plane corresponding to the maximum target quantity as the target point cloud plane, wherein the residual point cloud data are point cloud data except for the first point cloud data and the second point cloud data in the point cloud data set.
2. The method according to claim 1, wherein the step of returning the selection of a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane is performed in parallel a plurality of times, and the number acquisition operation is performed separately for each of the point cloud planes having a distance from the second point cloud data less than or equal to the preset distance.
3. The method of claim 1, wherein selecting a predetermined number of first point cloud data estimation point cloud planes from the set of point cloud data comprises:
establishing a reference coordinate system, and determining the coordinates of the first point cloud data based on the reference coordinate system;
and determining a corresponding plane equation according to the coordinates of the first point cloud data.
4. A method according to claim 3, wherein said calculating the distance of the second point cloud data from the point cloud plane comprises:
determining coordinates of the second point cloud data in the reference coordinate system;
and calculating the distance between the second point cloud data and the point cloud plane according to the coordinates of the second point cloud data and the plane equation.
5. A target point cloud plane determination apparatus, characterized in that the apparatus comprises:
The estimating module is used for selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane;
the acquisition module is used for acquiring second point cloud data, calculating the distance between the second point cloud data and the point cloud plane, wherein the second point cloud data is one point cloud data selected randomly from the point cloud data except the first point cloud data in the point cloud data set;
the iteration module is used for returning to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane when the distance is larger than a preset distance;
the determining module is configured to perform, when the distance is less than or equal to the preset distance, the following number acquisition operations for the point cloud plane: obtaining the distance between each piece of residual point cloud data and the point cloud plane, and obtaining the target number of the residual point cloud data with the distance smaller than or equal to the preset distance;
determining a target point cloud plane according to the target quantity, wherein the target point cloud plane specifically comprises: returning to execute the step of selecting the preset number of first point cloud data from the point cloud data set for a preset number of times, or returning to execute the step of selecting the preset number of first point cloud data from the point cloud data set for a plurality of times for estimating the point cloud plane until the number of the point cloud planes, the distance between which is less than or equal to the preset distance from the second point cloud data, is greater than or equal to a preset threshold; the performing the following number acquisition operations for the point cloud plane includes: the number acquisition operation is respectively executed for each point cloud plane with the distance from the second point cloud data being smaller than or equal to the preset distance, so that the target number corresponding to each point cloud plane is obtained; the determining the target point cloud plane according to the target number includes: and determining the point cloud plane corresponding to the maximum target quantity as the target point cloud plane, wherein the residual point cloud data are point cloud data except for the first point cloud data and the second point cloud data in the point cloud data set.
6. A non-transitory computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processing device, implements the steps of the method according to any one of claims 1-4.
7. An electronic device, comprising:
a storage device having a computer program stored thereon;
processing means for executing said computer program in said storage means to carry out the steps of the method according to any one of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011582206.1A CN112650790B (en) | 2020-12-28 | 2020-12-28 | Target point cloud plane determining method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011582206.1A CN112650790B (en) | 2020-12-28 | 2020-12-28 | Target point cloud plane determining method and device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112650790A CN112650790A (en) | 2021-04-13 |
| CN112650790B true CN112650790B (en) | 2023-10-13 |
Family
ID=75363503
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011582206.1A Active CN112650790B (en) | 2020-12-28 | 2020-12-28 | Target point cloud plane determining method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112650790B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117061684A (en) * | 2022-05-07 | 2023-11-14 | 北京字跳网络技术有限公司 | Special effect video generation method and device, electronic equipment and storage medium |
| CN115797573B (en) * | 2023-02-09 | 2023-04-28 | 四川省公路规划勘察设计研究院有限公司 | Method, device and medium for measuring point cloud twinning geometric accuracy |
| CN116465302B (en) * | 2023-03-31 | 2023-11-10 | 中国地震局地质研究所 | Method, device, equipment and storage medium for monitoring fault movement |
| CN116128886A (en) * | 2023-04-18 | 2023-05-16 | 深圳市其域创新科技有限公司 | Point cloud data segmentation method and device, electronic equipment and storage medium |
| CN116740361B (en) * | 2023-08-11 | 2023-10-13 | 之江实验室 | Point cloud segmentation method and device, storage medium and electronic equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104463871A (en) * | 2014-12-10 | 2015-03-25 | 武汉大学 | Streetscape facet extraction and optimization method based on vehicle-mounted LiDAR point cloud data |
| CN105976312A (en) * | 2016-05-30 | 2016-09-28 | 北京建筑大学 | Point cloud automatic registering method based on point characteristic histogram |
| CN107564059A (en) * | 2017-07-11 | 2018-01-09 | 北京联合大学 | Object positioning method, device and NI Vision Builder for Automated Inspection based on RGB D information |
| CN111652926A (en) * | 2020-04-30 | 2020-09-11 | 南京理工大学 | Real-time three-dimensional target detection method based on sparse point cloud data |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108228798B (en) * | 2017-12-29 | 2021-09-17 | 百度在线网络技术（北京）有限公司 | Method and device for determining matching relation between point cloud data |
-
2020
- 2020-12-28 CN CN202011582206.1A patent/CN112650790B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104463871A (en) * | 2014-12-10 | 2015-03-25 | 武汉大学 | Streetscape facet extraction and optimization method based on vehicle-mounted LiDAR point cloud data |
| CN105976312A (en) * | 2016-05-30 | 2016-09-28 | 北京建筑大学 | Point cloud automatic registering method based on point characteristic histogram |
| CN107564059A (en) * | 2017-07-11 | 2018-01-09 | 北京联合大学 | Object positioning method, device and NI Vision Builder for Automated Inspection based on RGB D information |
| CN111652926A (en) * | 2020-04-30 | 2020-09-11 | 南京理工大学 | Real-time three-dimensional target detection method based on sparse point cloud data |
Non-Patent Citations (1)
| Title |
|---|
| 利用三维激光扫描技术检测建筑物平整度及垂直度;王二民 等;《测绘通报》;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112650790A (en) | 2021-04-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112650790B (en) | Target point cloud plane determining method and device, electronic equipment and storage medium | |
| CN110413812B (en) | Neural network model training method and device, electronic equipment and storage medium | |
| CN110634047B (en) | Method and device for recommending house resources, electronic equipment and storage medium | |
| CN111784712B (en) | Image processing method, device, equipment and computer readable medium | |
| CN110222775B (en) | Image processing method, image processing device, electronic equipment and computer readable storage medium | |
| CN113934958B (en) | Page loading method and device, electronic equipment and computer readable medium | |
| CN111915689B (en) | Method, apparatus, electronic device, and computer-readable medium for generating an objective function | |
| CN111241137B (en) | Data processing method, device, electronic equipment and storage medium | |
| CN110555861B (en) | Optical flow calculation method and device and electronic equipment | |
| CN110378282B (en) | Image processing method and device | |
| CN111143355B (en) | Data processing method and device | |
| CN111680754B (en) | Image classification method, device, electronic equipment and computer readable storage medium | |
| CN112085733B (en) | Image processing method, image processing device, electronic equipment and computer readable medium | |
| CN111258582B (en) | Window rendering method and device, computer equipment and storage medium | |
| CN110209851B (en) | Model training method and device, electronic equipment and storage medium | |
| CN111382233A (en) | Similar text detection method and device, electronic equipment and storage medium | |
| CN112215789B (en) | Image defogging method, device, equipment and computer readable medium | |
| CN113808050B (en) | Denoising method, device and equipment for 3D point cloud and storage medium | |
| CN113435528B (en) | Method, device, readable medium and electronic equipment for classifying objects | |
| CN111814807B (en) | Method, apparatus, electronic device, and computer-readable medium for processing image | |
| CN111026983B (en) | Method, device, medium and electronic equipment for realizing hyperlink | |
| CN116266087A (en) | Icon click detection method, device, equipment and storage medium | |
| CN113191377A (en) | Method and apparatus for processing image | |
| CN114429627A (en) | Text feature extraction method and device, readable medium and electronic equipment | |
| CN114359673A (en) | Small sample smoke detection method, device and equipment based on metric learning |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |