CN117372850A - Data identification method and system for laser point cloud modeling - Google Patents

Data identification method and system for laser point cloud modeling Download PDF

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Publication number
CN117372850A
CN117372850A CN202311434417.4A CN202311434417A CN117372850A CN 117372850 A CN117372850 A CN 117372850A CN 202311434417 A CN202311434417 A CN 202311434417A CN 117372850 A CN117372850 A CN 117372850A
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point cloud
laser point
modeling data
cloud modeling
preset
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刘清
李区生
宋嘉鹏
李伟鹏
梁仁政
黄剑
张震林
李劲东
经纬明
王晖
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Guangxi Institute Of Natural Resources Remote Sensing
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Guangxi Institute Of Natural Resources Remote Sensing
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/98—Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/64—Three-dimensional objects

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  • General Physics & Mathematics (AREA)
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  • Theoretical Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Laser Beam Processing (AREA)

Abstract

The invention relates to the technical field of data identification of laser point cloud modeling, and discloses a data identification method and a system of laser point cloud modeling, wherein the method comprises the following steps: acquiring laser point cloud modeling data to be identified; performing data cleaning on laser point cloud modeling data to be identified, and extracting quality information of the laser point cloud modeling data after data cleaning; performing quality scoring on laser point cloud modeling data to be identified according to quality information of the laser point cloud modeling data; determining the quality grade of the laser point cloud modeling data according to the relation between the quality grade of the laser point cloud modeling data and the preset quality grade of the laser point cloud modeling data; and identifying whether the laser point cloud modeling data can be used according to the relation between the quality grade of the laser point cloud modeling data and the preset quality grade. The method comprehensively improves the identification quality of the laser point cloud modeling data, and further can effectively and intuitively and quantitatively know the data credibility.

Description

Data identification method and system for laser point cloud modeling
Technical Field
The invention relates to the technical field of data identification of laser point cloud modeling, in particular to a data identification method and system of laser point cloud modeling.
Background
Laser point cloud modeling is a three-dimensional environment modeling method based on laser radar technology, point cloud data in the environment is collected through laser sensors, and then the discrete laser point clouds are utilized to restore and present the three-dimensional structure of an object, a scene or a topography. The modeling technology is widely applied to the fields of automatic driving, robot navigation, virtual reality, city planning and the like. Laser point cloud modeling generates a large number of discrete point cloud data by measuring the reflection or scattering of a laser beam, each point representing a surface point in space. These point cloud data can be used to generate highly accurate, detailed, three-dimensional models that provide high resolution representations of environments and objects.
Currently, there are some significant problems in the traditional laser point cloud modeling techniques, mainly manifested in the lack of specialized means to identify and process potentially low quality or invalid data. And this technical limitation is very prone to various problems including modeling distortion, target omission or false detection, etc. Especially in the fields of automatic driving, robot navigation and the like, such a lack of accurate recognition means for high-quality data becomes remarkable. However, conventional methods fail to effectively address the quality issues of laser point cloud data, thereby potentially presenting inaccurate understanding of the environment, compromising decision making and operational safety of automated driving and robotic navigation.
Therefore, it is urgently required to invent a technology for identifying data of laser point cloud modeling, which is used for solving the problem that low-quality or invalid data in the data of laser point cloud modeling is effectively identified in the conventional technology.
Disclosure of Invention
The purpose of the invention is that: the data identification method and the system for the laser point cloud modeling are used for solving the problem of how to effectively identify low-quality or invalid data in the data for the laser point cloud modeling in the traditional technology.
In one aspect, an embodiment of the present invention provides a data identification method for laser point cloud modeling, including:
acquiring laser point cloud modeling data to be identified;
performing data cleaning on the laser point cloud modeling data to be identified, and extracting quality information of the laser point cloud modeling data after data cleaning, wherein the quality information of the laser point cloud modeling data comprises the integrity of the laser point cloud modeling data, the resolution of the laser point cloud modeling data and the point cloud density of the laser point cloud modeling data;
performing quality scoring on the laser point cloud modeling data to be identified according to the quality information of the laser point cloud modeling data;
determining the quality grade of the laser point cloud modeling data according to the relation between the quality grade of the laser point cloud modeling data and the preset quality grade of the laser point cloud modeling data;
According to the relation between the quality grade of the laser point cloud modeling data and the preset quality grade, identifying whether the laser point cloud modeling data can be used or not comprises the following steps:
acquiring the quality grade Q of the laser point cloud modeling data, and judging whether the laser point cloud modeling data can be used or not according to the relation between the quality grade Q of the laser point cloud modeling data and the quality grade Q0 of the preset laser point cloud modeling data;
when Q is more than or equal to Q0, judging that the quality grade of the laser point cloud modeling data is higher than or equal to the quality grade of the preset laser point cloud modeling data, and judging that the laser point cloud modeling data can be used;
and when Q is smaller than Q0, judging that the quality level of the laser point cloud modeling data is lower than the quality level of the preset laser point cloud modeling data, and judging that the laser point cloud modeling data cannot be used.
Further, when data cleaning is performed on the laser point cloud modeling data to be identified and quality information of the laser point cloud modeling data after data cleaning is extracted, the method includes:
clearing abnormal data cloud points in the laser point cloud modeling data to be identified;
And acquiring the laser point cloud modeling data and the boundary of the laser point cloud modeling data after abnormal data cloud points are cleared, and removing the laser point cloud modeling data which is not positioned in the boundary of the laser point cloud modeling data in the laser point cloud modeling data according to the boundary of the laser point cloud modeling data.
Further, when the quality of the laser point cloud modeling data to be identified is scored according to the quality information of the laser point cloud modeling data, the method includes:
acquiring the current integrity L of the laser point cloud modeling data after data cleaning, and judging whether the laser point cloud modeling data after data cleaning is complete according to the relation between the current integrity L and the preset integrity L0:
when L is more than or equal to L0, judging that the integrity of the laser point cloud modeling data is more than or equal to the preset integrity, and judging that the laser point cloud modeling data after the data cleaning is complete;
when L is smaller than L0, judging that the integrity of the laser point cloud modeling data is smaller than the preset integrity, judging that the laser point cloud modeling data after the data are cleaned is incomplete, and grading the quality of the laser point cloud modeling data according to the relation between the current integrity L and the preset integrity L0.
Further, when judging that the laser point cloud modeling data after the data cleaning is incomplete and performing quality scoring on the laser point cloud modeling data according to the relationship between the current integrity L and the preset integrity L0, the method includes:
acquiring an integrity difference delta L between the current integrity L and a preset integrity L0, comparing the integrity difference delta L with the preset integrity difference delta L, selecting a corresponding quality score according to a comparison result, and grading the quality of the laser point cloud modeling data;
the method comprises the steps of presetting a first preset integrity difference delta L1 and a second preset integrity difference delta L2, presetting a first preset quality score M1, a second preset quality score M2 and a third preset quality score M3, wherein delta L1 is less than delta L2, and M1 is less than M2 and less than M3;
when DeltaL is less than or equal to DeltaL 1, selecting the first preset quality score M1 to perform quality scoring on the laser point cloud modeling data;
when DeltaL 1 < DeltaLis less than or equal to DeltaL 2, selecting the second preset quality score M2 to carry out quality scoring on the laser point cloud modeling data;
when DeltaL > DeltaL2, selecting the third preset quality score M3 to perform quality score on the laser point cloud modeling data;
When the i-th preset quality score M i is selected to perform quality scoring on the laser point cloud modeling data, i=1, 2,3, and determining the quality score of the laser point cloud modeling data as E1, setting E1=e× M i, wherein E is an initial quality score of the laser point cloud modeling data.
Further, when selecting the i-th preset quality score M i to score the quality of the laser point cloud modeling data and determining that the quality score of the laser point cloud modeling data is E1, the method includes:
acquiring the current resolution K of the laser point cloud modeling data, and judging whether the laser point cloud modeling data is clear or not according to the relation between the current resolution K of the laser point cloud modeling data and the preset resolution K0 of the laser point cloud modeling data;
when K is more than or equal to K0, judging that the resolution of the laser point cloud modeling data is larger than or equal to the preset resolution of the laser point cloud modeling data, and judging that the laser point cloud modeling data is clear;
when K is smaller than K0, judging that the resolution of the laser point cloud modeling data is smaller than the preset resolution of the laser point cloud modeling data, judging that the laser point cloud modeling data is unclear, and adjusting the quality score E1 of the laser point cloud modeling data according to the relation between the current resolution K of the laser point cloud modeling data and the preset resolution K0 of the laser point cloud modeling data.
Further, determining that the laser point cloud modeling data is unclear, and adjusting the quality score E1 of the laser point cloud modeling data according to a relationship between a current resolution K of the laser point cloud modeling data and a preset resolution K0 of the laser point cloud modeling data includes:
acquiring a resolution difference delta K between the current resolution K of the laser point cloud modeling data and a preset resolution K0 of the laser point cloud modeling data, comparing the resolution difference delta K with the preset resolution difference delta K, and selecting a corresponding adjustment coefficient according to a comparison result to adjust a quality score E1 of the laser point cloud modeling data;
the method comprises the steps of presetting a first preset resolution difference delta K1 and a second preset resolution difference delta K2, presetting a first preset adjustment coefficient N1, presetting a second preset adjustment coefficient N2 and a third preset adjustment coefficient N3, wherein delta K1 is less than delta K2, and 0 < N1 < N2 < N3 < 0.5;
when delta K is less than or equal to delta K1, selecting the first preset adjustment coefficient N1 to adjust the quality score E1 of the laser point cloud modeling data;
when delta K1 is less than or equal to delta K2, selecting the second preset adjustment coefficient N2 to adjust the quality score E1 of the laser point cloud modeling data;
When delta K > -delta K2, selecting the third preset adjustment coefficient N3 to adjust the quality score E1 of the laser point cloud modeling data;
when the ith preset adjustment coefficient N i is selected to adjust the quality score E1 of the laser point cloud modeling data, i=1, 2,3, and the adjusted quality score E2 of the laser point cloud modeling data is determined, and e2=e1× N i is set.
Further, when the ith preset adjustment coefficient N i is selected to adjust the quality score E1 of the laser point cloud modeling data and the adjusted quality score E2 of the laser point cloud modeling data is determined, the method includes:
acquiring the real-time point cloud density J of the laser point cloud modeling data, and judging whether a structure in the laser point cloud modeling data is specific or not according to the relation between the real-time point cloud density J of the laser point cloud modeling data and the preset point cloud density J0 of the laser point cloud modeling data:
when J is more than or equal to J0, judging that the point cloud density of the laser point cloud modeling data is larger than or equal to the preset point cloud density of the laser point cloud modeling data, and judging that the structure in the laser point cloud modeling data is specific;
when J is smaller than J0, judging that the point cloud density of the laser point cloud modeling data is smaller than the preset point cloud density of the laser point cloud modeling data, judging that the structure in the laser point cloud modeling data is not specific, and correcting the quality score E2 of the laser point cloud modeling data after adjustment according to the relation between the real-time point cloud density J of the laser point cloud modeling data and the preset point cloud density J0 of the laser point cloud modeling data.
Further, determining that the structure in the laser point cloud modeling data is not specific, and correcting the adjusted quality score E2 of the laser point cloud modeling data according to a relationship between the real-time point cloud density J of the laser point cloud modeling data and the preset point cloud density J0 of the laser point cloud modeling data, where the method includes:
acquiring a point cloud density difference delta J between the real-time point cloud density J of the laser point cloud modeling data and a preset point cloud density J0 of the laser point cloud modeling data, comparing the point cloud density difference delta J with the preset point cloud density difference delta J according to the point cloud density difference delta J, and selecting a corresponding correction coefficient according to a comparison result to correct the quality score E2 of the adjusted laser point cloud modeling data;
the method comprises the steps of presetting a first preset point cloud density difference delta J1 and a second preset point cloud density difference delta J2, presetting a first correction coefficient B1, a second preset correction coefficient B2 and a third preset correction coefficient B3, wherein delta J1 < [ delta J2 ], and 0.15 < B1 < B2 < B3 < 0.3;
when delta J is less than or equal to delta J1, selecting the first preset correction coefficient B1 to correct the quality score E2 of the adjusted laser point cloud modeling data;
When delta J1 is less than or equal to delta J2, selecting the second preset correction coefficient B2 to correct the quality score E2 of the adjusted laser point cloud modeling data;
when DeltaJ > DeltaJ2, selecting the third preset correction coefficient B3 to correct the quality score E2 of the adjusted laser point cloud modeling data;
when the ith preset correction coefficient B i is selected to correct the adjusted quality score E2 of the laser point cloud modeling data, i=1, 2,3, and the corrected quality score of the laser point cloud modeling data is determined to be E3, and e3=e2× B i is set.
Further, determining the quality level of the laser point cloud modeling data according to the relationship between the quality score of the laser point cloud modeling data and the preset quality score of the laser point cloud modeling data includes:
obtaining a modified quality score E3 of the laser point cloud modeling data,
presetting a first preset quality score T1 and a second preset quality score T2, presetting a first preset quality grade Q1, a second preset quality grade Q2 and a third preset quality grade Q3, wherein T1 is smaller than T2, Q1 is smaller than Q2 and Q3;
comparing the modified quality scores E3 of the laser point cloud modeling data with each preset quality score, and selecting a corresponding preset quality grade as the quality grade of the laser point cloud modeling data according to the comparison result;
When E3 is less than or equal to T1, selecting the first preset quality grade Q1 as the quality grade of the laser point cloud modeling data;
when T1 is more than E3 and less than or equal to T2, selecting the second preset quality grade Q2 as the quality grade of the laser point cloud modeling data;
when E3 is more than T2, selecting the third preset quality grade Q3 as the quality grade of the laser point cloud modeling data;
when the i-th preset quality level Q i is selected as the quality level of the laser point cloud modeling data, i=1, 2,3, and the quality level of the laser point cloud modeling data is determined to be Q, and q=q1, Q2, Q3 … Q i is set.
In another aspect, the embodiment of the present invention further provides a data identification system for modeling a laser point cloud, which is applicable to a data identification method for modeling a laser point cloud according to the above embodiments of the present invention, including:
the data acquisition module is electrically connected with the database and is used for acquiring laser point cloud modeling data to be identified.
The data cleaning module is electrically connected with the data acquisition module and is used for cleaning the laser point cloud modeling data to be identified and extracting quality information of the laser point cloud modeling data after data cleaning, wherein the quality information of the laser point cloud modeling data comprises the integrity of the laser point cloud modeling data, the resolution of the laser point cloud modeling data and the point cloud density of the laser point cloud modeling data;
The scoring module is electrically connected with the data cleaning module and is used for scoring the quality of the laser point cloud modeling data to be identified according to the quality information of the laser point cloud modeling data;
the central control module is electrically connected with the scoring module and is used for determining the quality grade of the laser point cloud modeling data according to the relation between the quality score of the laser point cloud modeling data and the preset quality score of the laser point cloud modeling data, and identifying whether the laser point cloud modeling data can be used or not according to the relation between the quality grade of the laser point cloud modeling data and the preset quality grade.
Compared with the prior art, the data identification method and the system for laser point cloud modeling have the beneficial effects that: by acquiring laser point cloud modeling data, an original data basis is provided for subsequent processing. And then, through a data cleaning step, possible noise and invalid information are removed, and the quality of the data is improved. Further, key quality information of laser point cloud modeling data is extracted from the aspects of integrity, resolution, point cloud density and the like, and a comprehensive basis is provided for quality evaluation. By means of quality scoring, the system quantitatively measures the overall quality of laser point cloud modeling data, and a user can clearly know the credibility of the data. The subsequent quality classification further simplifies the determination of the quality of the data by comparing with a preset quality classification, classifying the laser point cloud modeling data into two categories that are usable and unusable. This classification intuitively informs the user of the applicability of the data, providing an important basis for decision making. The whole flow emphasizes the attention to the quality of laser point cloud modeling data, is beneficial to improving the data reliability and ensures that accurate and reliable results are obtained in subsequent applications. Meanwhile, through the clear judgment standard, a user can more effectively decide whether to use the data, so that better data management and utilization are realized in various application scenes.
Drawings
Fig. 1 is a flow chart of a data identification method for modeling a laser point cloud according to an embodiment of the present invention.
FIG. 2 is a block diagram showing the structural connection of a data recognition system for modeling a laser point cloud according to an embodiment of the present invention
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
As shown in fig. 1, a data identification method for modeling a laser point cloud according to an embodiment of the present invention includes:
and step S100, acquiring laser point cloud modeling data to be identified.
Step 200, data cleaning is performed on laser point cloud modeling data to be identified, and quality information of the laser point cloud modeling data after data cleaning is extracted, wherein the quality information of the laser point cloud modeling data comprises the integrity of the laser point cloud modeling data, the resolution of the laser point cloud modeling data and the point cloud density of the laser point cloud modeling data.
And step S300, quality scoring is carried out on the laser point cloud modeling data to be identified according to the quality information of the laser point cloud modeling data.
Step S400, determining the quality grade of the laser point cloud modeling data according to the relation between the quality grade of the laser point cloud modeling data and the preset quality grade of the laser point cloud modeling data.
Step S500, according to the relation between the quality grade of the laser point cloud modeling data and the preset quality grade, whether the laser point cloud modeling data can be used or not is identified.
It can be appreciated that in step S100, laser point cloud modeling data to be identified is obtained, and raw data is provided for subsequent processing. Then, in step S200, the data is preprocessed by data cleaning, possible noise and invalid information are removed, and key quality information of the data including integrity, resolution and point cloud density is extracted, so that the quality of the data is improved. Next, in step S300, quality scoring is performed on the laser point cloud modeling data, and the overall quality level of the data is measured in a quantization manner, so that the user can accurately understand the credibility of the data. The last steps S400 and S500 further enhance the management and application of data quality. By comparing the quality score with the preset quality score in S400, the quality level of the data is determined, and the data is classified into different quality categories, so that the judgment of the user on the quality of the data is simplified. Finally, in S500, according to the relationship between the quality level and the preset level, it is identified whether the laser point cloud modeling data can be safely used, which provides a clear guidance for the user, and helps the user to decide whether to use the data in the subsequent application.
Specifically, in some embodiments of the present invention, identifying whether the laser point cloud modeling data is usable based on a relationship between a quality level of the laser point cloud modeling data and a preset quality level includes: acquiring the quality grade Q of the laser point cloud modeling data, and judging whether the laser point cloud modeling data can be used according to the relation between the quality grade Q of the laser point cloud modeling data and the quality grade Q0 of the preset laser point cloud modeling data: when Q is more than or equal to Q0, judging that the quality grade of the laser point cloud modeling data is higher than or equal to the quality grade of preset laser point cloud modeling data, and judging that the laser point cloud modeling data can be used. When Q is smaller than Q0, judging that the quality level of the laser point cloud modeling data is lower than the quality level of the preset laser point cloud modeling data, and judging that the laser point cloud modeling data cannot be used.
Specifically, in some embodiments of the present invention, when performing data cleaning on laser point cloud modeling data to be identified and extracting quality information of the laser point cloud modeling data after data cleaning, the method includes: and eliminating abnormal data cloud points in the laser point cloud modeling data to be identified. And acquiring laser point cloud modeling data after abnormal data cloud points are cleared and boundaries of the laser point cloud modeling data, and removing the laser point cloud modeling data which is not positioned in the boundaries of the laser point cloud modeling data in the laser point cloud modeling data according to the boundaries of the laser point cloud modeling data.
It can be appreciated that by removing the outlier cloud, outlier noise or invalid data that may be present in the laser point cloud modeling data is successfully removed, thereby improving the accuracy and quality of the data. This helps to avoid interference with the anomalous data during subsequent analysis and application, ensuring that the data better conforms to the actual scenario. And secondly, the laser point cloud modeling data after abnormal data cloud points are removed and the boundaries of the laser point cloud modeling data are obtained, so that the clear control of the data boundaries is realized. By eliminating the part which is not positioned inside the data boundary in the laser point cloud modeling data, the data possibly influenced by the external environment is further reduced, and the left data is ensured to be more accurate and reliable. This helps to increase the practicality of the data, making it more desirable for specific application scenarios.
Specifically, in some embodiments of the present invention, when quality scoring is performed on laser point cloud modeling data to be identified according to quality information of the laser point cloud modeling data, the method includes: acquiring the current integrity L of the laser point cloud modeling data after data cleaning, and judging whether the laser point cloud modeling data after data cleaning is complete or not according to the relation between the current integrity L and the preset integrity L0: when L is more than or equal to L0, judging that the integrity of the laser point cloud modeling data is more than or equal to the preset integrity, and judging that the laser point cloud modeling data after data cleaning is complete; when L is smaller than L0, judging that the integrity of the laser point cloud modeling data is smaller than the preset integrity, judging that the laser point cloud modeling data after data cleaning is incomplete, and grading the quality of the laser point cloud modeling data according to the relation between the current integrity L and the preset integrity L0.
Specifically, in some embodiments of the present invention, when determining that laser point cloud modeling data after data cleaning is incomplete and performing quality scoring on the laser point cloud modeling data according to a relationship between a current integrity L and a preset integrity L0, the method includes: obtaining an integrity difference delta L between the current integrity L and a preset integrity L0, wherein delta L=L-L0, comparing the integrity difference delta L with the preset integrity difference, selecting a corresponding quality score according to a comparison result, and carrying out quality score on laser point cloud modeling data: the method comprises the steps of presetting a first preset integrity difference delta L1 and a second preset integrity difference delta L2, presetting a first preset quality score M1, a second preset quality score M2 and a third preset quality score M3, wherein delta L1 is less than delta L2, and M1 is less than M2 and less than M3.
And when delta L is less than or equal to delta L1, selecting a first preset quality score M1 to carry out quality scoring on the laser point cloud modeling data.
And when DeltaL 1 < DeltaL2 is less than or equal to DeltaL 2, selecting a second preset quality score M2 to perform quality scoring on the laser point cloud modeling data.
When DeltaL > DeltaL2, selecting a third preset quality score M3 to perform quality score on the laser point cloud modeling data.
When the i-th preset quality score M i is selected to perform quality scoring on the laser point cloud modeling data, i=1, 2,3, and determining that the quality score of the laser point cloud modeling data is E1, setting e1=e×mi, where E is an initial quality score of the laser point cloud modeling data.
It can be understood that by judging whether the laser point cloud modeling data after data cleaning is complete, the system can effectively identify the situation that missing or incomplete data possibly exists. This helps to reduce misuse of incomplete data in subsequent analysis and application, improving reliability and practicality of the data. And secondly, introducing a concept of an integrity difference value, comparing the integrity difference value with a preset integrity difference value, and selecting a corresponding quality score. The difference comparison mechanism enables quality scoring to be more flexible, and the overall situation of data can be reflected more accurately. Different integrity differences correspond to different quality scores, enhancing the differential assessment of data integrity. Finally, dynamic adjustment of data quality is achieved by performing quality scoring on the laser point cloud modeling data according to the selected preset quality score. The dynamic adjustment mechanism enables the system to flexibly adjust the quality score according to actual conditions, and the overall quality level of the laser point cloud modeling data is reflected more accurately.
Specifically, in some embodiments of the present invention, when the i-th preset quality score M i is selected to score the quality of the laser point cloud modeling data and determine the quality score of the laser point cloud modeling data to be E1, the method includes: acquiring the current resolution K of the laser point cloud modeling data, and judging whether the laser point cloud modeling data is clear or not according to the relation between the current resolution K of the laser point cloud modeling data and the preset resolution K0 of the laser point cloud modeling data; when K is more than or equal to K0, judging that the resolution of the laser point cloud modeling data is larger than or equal to the preset resolution of the laser point cloud modeling data, and judging that the laser point cloud modeling data is clear. When K is smaller than K0, judging that the resolution of the laser point cloud modeling data is smaller than the preset resolution of the laser point cloud modeling data, judging that the laser point cloud modeling data is not clear, and adjusting the quality score E1 of the laser point cloud modeling data according to the relation between the current resolution K of the laser point cloud modeling data and the preset resolution K0 of the laser point cloud modeling data.
Specifically, in some embodiments of the present invention, determining that laser point cloud modeling data is unclear, and adjusting the quality score E1 of the laser point cloud modeling data according to a relationship between a current resolution K of the laser point cloud modeling data and a preset resolution K0 of the laser point cloud modeling data includes: obtaining a resolution difference delta K between the current resolution K of the laser point cloud modeling data and a preset resolution K0 of the laser point cloud modeling data, wherein delta K=K-K0, comparing the current resolution K with the preset resolution difference, and selecting a corresponding adjustment coefficient according to a comparison result to adjust a quality score E1 of the laser point cloud modeling data: wherein the first preset resolution difference DeltaK 1 and the second preset resolution difference DeltaK 2 are preset, the first preset adjustment coefficient N1, the second preset adjustment coefficient N2 and the third preset adjustment coefficient N3 are preset, deltaK 1 < DeltaK2, and 0 < N1 < N2 < N3 < 0.5.
When the delta K is less than or equal to delta K1, a first preset adjustment coefficient N1 is selected to adjust the quality score E1 of the laser point cloud modeling data.
When delta K1 is less than delta K2, selecting a second preset adjustment coefficient N2 to adjust the quality score E1 of the laser point cloud modeling data.
When DeltaK > DeltaK2, a third preset adjustment coefficient N3 is selected to adjust the quality score E1 of the laser point cloud modeling data.
When the ith preset adjustment coefficient N i is selected to adjust the quality score E1 of the laser point cloud modeling data, i=1, 2,3, and the adjusted quality score E2 of the laser point cloud modeling data is determined, and e2=e1× N i is set.
It can be appreciated that by determining the sharpness of the laser point cloud modeling data, the system can effectively identify data conditions that may be ambiguous or otherwise unclear. This helps to reduce misuse of unclear data in subsequent applications, improving the visualization effect and interpretation of the data. Secondly, a concept of a resolution difference is introduced, comparison is carried out according to the resolution difference and a preset resolution difference, and a corresponding adjustment coefficient is selected to adjust the quality score of the laser point cloud modeling data. The difference comparison mechanism enables quality scores to be flexibly adjusted, and the overall definition of data can be reflected more accurately. Different resolution differences correspond to different adjustment coefficients, enhancing the differential evaluation of data clarity. Finally, the quality score of the laser point cloud modeling data is adjusted according to the selected preset adjustment coefficient, so that the dynamic adjustment of the data quality is realized. The dynamic adjustment mechanism enables the system to flexibly adjust the quality score according to actual conditions, and the overall definition level of the laser point cloud modeling data is reflected more accurately.
Specifically, in some embodiments of the present invention, when the ith preset adjustment coefficient N i is selected to adjust the laser point cloud modeling data quality score E1 and determine that the adjusted laser point cloud modeling data quality score is E2, the method includes: acquiring real-time point cloud density J of laser point cloud modeling data, and judging whether a structure in the laser point cloud modeling data is specific or not according to the relation between the real-time point cloud density J of the laser point cloud modeling data and preset point cloud density J0 of the laser point cloud modeling data: when J is more than or equal to J0, judging that the point cloud density of the laser point cloud modeling data is larger than or equal to the preset point cloud density of the laser point cloud modeling data, and judging that the structure in the laser point cloud modeling data is specific. When J is smaller than J0, judging that the point cloud density of the laser point cloud modeling data is smaller than the preset point cloud density of the laser point cloud modeling data, judging that the structure in the laser point cloud modeling data is not specific, and correcting the quality score E2 of the adjusted laser point cloud modeling data according to the relation between the real-time point cloud density J of the laser point cloud modeling data and the preset point cloud density J0 of the laser point cloud modeling data.
Specifically, in some embodiments of the present invention, it is determined that the structure in the laser point cloud modeling data is not specific, and when the adjusted quality score E2 of the laser point cloud modeling data is corrected according to the relationship between the real-time point cloud density J of the laser point cloud modeling data and the preset point cloud density J0 of the laser point cloud modeling data, the method includes: acquiring a point cloud density difference delta J between a real-time point cloud density J of laser point cloud modeling data and a preset point cloud density J0 of the laser point cloud modeling data, comparing the point cloud density difference delta J with the preset point cloud density difference according to the point cloud density difference delta J=J-J0, and selecting a corresponding correction coefficient according to a comparison result to correct an adjusted quality score E2 of the laser point cloud modeling data: the method comprises the steps of presetting a first preset point cloud density difference delta J1 and a second preset point cloud density difference delta J2, presetting a first correction coefficient B1, a second preset correction coefficient B2 and a third preset correction coefficient B3, wherein delta J1 < [ delta J2 ], and 0.15 < B1 < B2 < B3 < 0.3.
When the delta J is less than or equal to delta J1, a first preset correction coefficient B1 is selected to correct the quality score E2 of the adjusted laser point cloud modeling data.
When delta J1 is less than or equal to delta J2, selecting a second preset correction coefficient B2 to correct the quality score E2 of the adjusted laser point cloud modeling data.
When DeltaJ > DeltaJ2, selecting a third preset correction coefficient B3 to correct the quality score E2 of the adjusted laser point cloud modeling data.
When the ith preset correction coefficient B i is selected to correct the adjusted quality score E2 of the laser point cloud modeling data, i=1, 2,3, and the corrected quality score of the laser point cloud modeling data is determined to be E3, and e3=e2×bi is set.
It can be appreciated that by determining whether the structure in the laser point cloud modeling data is specific, the system can more accurately learn the actual characteristics of the data. This is of great importance for scenarios requiring accurate structural information in subsequent applications, such as autopilot or robotic navigation, and can avoid misuse of data that is not specific to the structure. And secondly, introducing a concept of a point cloud density difference value, comparing the density difference value with a preset density difference value, and selecting a corresponding correction coefficient for adjustment. The difference comparison mechanism enables quality scores to be corrected more flexibly, and structural features of laser point cloud modeling data can be reflected more accurately. Different density differences correspond to different correction coefficients, and differential evaluation of the data structure characteristics is enhanced. And finally, correcting the quality score of the laser point cloud modeling data according to the selected preset correction coefficient, so as to realize dynamic adjustment of the data quality. The dynamic adjustment mechanism enables the system to flexibly adjust the quality score according to actual conditions, and the overall structure characteristic level of the laser point cloud modeling data is reflected more accurately.
Specifically, in some embodiments of the present invention, determining the quality level of the laser point cloud modeling data according to a relationship between a quality score of the laser point cloud modeling data and a preset quality score of the laser point cloud modeling data includes: and acquiring a quality score E3 of the corrected laser point cloud modeling data. The method comprises the steps of presetting a first preset quality score T1 and a second preset quality score T2, presetting a first preset quality grade Q1, a second preset quality grade Q2 and a third preset quality grade Q3, wherein T1 is smaller than T2, and Q1 is smaller than Q2 and smaller than Q3. Comparing the quality scores E3 of the corrected laser point cloud modeling data with each preset quality score, and selecting a corresponding preset quality grade as the quality grade of the laser point cloud modeling data according to the comparison result: when E3 is less than or equal to T1, a first preset quality grade Q1 is selected as the quality grade of the laser point cloud modeling data. When T1 is more than E3 and less than or equal to T2, selecting a second preset quality grade Q2 as the quality grade of the laser point cloud modeling data. When E3 > T2, then a third preset quality level Q3 is selected as the laser point cloud modeling data quality level. When the i-th preset quality level Qi is selected as the laser point cloud modeling data quality level, i=1, 2,3, and the laser point cloud modeling data quality level is determined to be Q, q=q1, Q2, Q3 … Q i is set.
In summary, the embodiment of the invention provides a data identification method for laser point cloud modeling, which provides an original data basis for subsequent processing by acquiring laser point cloud modeling data. And then, through a data cleaning step, possible noise and invalid information are removed, and the quality of the data is improved. Further, key quality information of laser point cloud modeling data is extracted from the aspects of integrity, resolution, point cloud density and the like, and a comprehensive basis is provided for quality evaluation. By means of quality scoring, the system quantitatively measures the overall quality of laser point cloud modeling data, and a user can clearly know the credibility of the data. The subsequent quality classification further simplifies the determination of the quality of the data by comparing with a preset quality classification, classifying the laser point cloud modeling data into two categories that are usable and unusable. This classification intuitively informs the user of the applicability of the data, providing an important basis for decision making. The whole flow emphasizes the attention to the quality of laser point cloud modeling data, is beneficial to improving the data reliability and ensures that accurate and reliable results are obtained in subsequent applications. Meanwhile, through the clear judgment standard, a user can more effectively decide whether to use the data, so that better data management and utilization are realized in various application scenes.
Referring to fig. 2, some embodiments of the present invention further provide a data identification system for modeling a laser point cloud, which is suitable for use in a data identification method for modeling a laser point cloud according to the foregoing embodiments of the present invention, including: the system comprises a data acquisition module, a data cleaning module, a scoring module and a central control module. The data acquisition module is electrically connected with the database and is used for acquiring laser point cloud modeling data to be identified. The data cleaning module is electrically connected with the data acquisition module and is used for cleaning the data of the laser point cloud modeling data to be identified and extracting quality information of the laser point cloud modeling data after the data cleaning, wherein the quality information of the laser point cloud modeling data comprises the integrity of the laser point cloud modeling data, the resolution of the laser point cloud modeling data and the point cloud density of the laser point cloud modeling data; the scoring module is electrically connected with the data cleaning module and is used for scoring the quality of the laser point cloud modeling data to be identified according to the quality information of the laser point cloud modeling data; the central control module is electrically connected with the scoring module and is used for determining the quality grade of the laser point cloud modeling data according to the relation between the quality score of the laser point cloud modeling data and the preset quality score of the laser point cloud modeling data and identifying whether the laser point cloud modeling data can be used or not according to the relation between the quality grade of the laser point cloud modeling data and the preset quality grade.
It can be understood that the data identification system for laser point cloud modeling in the embodiments of the present invention is applicable to the data identification method for laser point cloud modeling in the embodiments of the present invention, so that the data identification system for laser point cloud modeling in the embodiments of the present invention has the same beneficial effects as the data identification method for laser point cloud modeling, and therefore will not be described again.
The foregoing is merely an example of the present invention, and the scope of the present invention is not limited thereto, and all changes made in the structure according to the present invention should be considered as falling within the scope of the present invention without departing from the gist of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the system described above and the related description may refer to the corresponding process in the foregoing method embodiment, which is not repeated here.
It should be noted that, in the system provided in the foregoing embodiment, only the division of the foregoing functional modules is illustrated, in practical application, the foregoing functional allocation may be performed by different functional modules, that is, the modules or steps in the embodiment of the present invention are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further split into multiple sub-modules, so as to complete all or part of the functions described above. The names of the modules and steps related to the embodiments of the present invention are merely for distinguishing the respective modules or steps, and are not to be construed as unduly limiting the present invention.
Those of skill in the art will appreciate that the various illustrative modules, method steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the program(s) corresponding to the software modules, method steps, may be embodied in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Those skilled in the art may implement the described functionality using different approaches for each particular application, but such implementation is not intended to be limiting.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus/apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus/apparatus.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will fall within the scope of the present invention.
The above is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.

Claims (10)

1. The data identification method for the laser point cloud modeling is characterized by comprising the following steps of:
acquiring laser point cloud modeling data to be identified;
performing data cleaning on the laser point cloud modeling data to be identified, and extracting quality information of the laser point cloud modeling data after data cleaning, wherein the quality information of the laser point cloud modeling data comprises the integrity of the laser point cloud modeling data, the resolution of the laser point cloud modeling data and the point cloud density of the laser point cloud modeling data;
performing quality scoring on the laser point cloud modeling data to be identified according to the quality information of the laser point cloud modeling data;
Determining the quality grade of the laser point cloud modeling data according to the relation between the quality grade of the laser point cloud modeling data and the preset quality grade of the laser point cloud modeling data;
according to the relation between the quality grade of the laser point cloud modeling data and the preset quality grade, identifying whether the laser point cloud modeling data can be used or not comprises the following steps:
acquiring the quality grade Q of the laser point cloud modeling data, and judging whether the laser point cloud modeling data can be used or not according to the relation between the quality grade Q of the laser point cloud modeling data and the quality grade Q0 of the preset laser point cloud modeling data;
when Q is more than or equal to Q0, judging that the quality grade of the laser point cloud modeling data is higher than or equal to the quality grade of the preset laser point cloud modeling data, and judging that the laser point cloud modeling data can be used;
and when Q is smaller than Q0, judging that the quality level of the laser point cloud modeling data is lower than the quality level of the preset laser point cloud modeling data, and judging that the laser point cloud modeling data cannot be used.
2. The method for identifying data of laser point cloud modeling according to claim 1, wherein when data cleaning is performed on the laser point cloud modeling data to be identified and quality information of the laser point cloud modeling data after data cleaning is extracted, the method comprises:
Clearing abnormal data cloud points in the laser point cloud modeling data to be identified;
and acquiring the laser point cloud modeling data and the boundary of the laser point cloud modeling data after abnormal data cloud points are cleared, and removing the laser point cloud modeling data which is not positioned in the boundary of the laser point cloud modeling data in the laser point cloud modeling data according to the boundary of the laser point cloud modeling data.
3. The data identification method of laser point cloud modeling according to claim 1, wherein when quality scoring is performed on the laser point cloud modeling data to be identified according to quality information of the laser point cloud modeling data, comprising:
acquiring the current integrity L of the laser point cloud modeling data after data cleaning, and judging whether the laser point cloud modeling data after data cleaning is complete according to the relation between the current integrity L and the preset integrity L0:
when L is more than or equal to L0, judging that the integrity of the laser point cloud modeling data is more than or equal to the preset integrity, and judging that the laser point cloud modeling data after the data cleaning is complete;
when L is smaller than L0, judging that the integrity of the laser point cloud modeling data is smaller than the preset integrity, judging that the laser point cloud modeling data after the data are cleaned is incomplete, and grading the quality of the laser point cloud modeling data according to the relation between the current integrity L and the preset integrity L0.
4. The method for identifying data of laser point cloud modeling according to claim 3, wherein when determining that the laser point cloud modeling data after the data cleaning is incomplete and performing quality scoring on the laser point cloud modeling data according to a relationship between the current integrity L and a preset integrity L0, the method comprises:
acquiring an integrity difference delta L between the current integrity L and a preset integrity L0, comparing the integrity difference delta L with the preset integrity difference delta L, selecting a corresponding quality score according to a comparison result, and grading the quality of the laser point cloud modeling data;
the method comprises the steps of presetting a first preset integrity difference delta L1 and a second preset integrity difference delta L2, presetting a first preset quality score M1, a second preset quality score M2 and a third preset quality score M3, wherein delta L1 is less than delta L2, and M1 is less than M2 and less than M3;
when DeltaL is less than or equal to DeltaL 1, selecting the first preset quality score M1 to perform quality scoring on the laser point cloud modeling data;
when DeltaL 1 < DeltaLis less than or equal to DeltaL 2, selecting the second preset quality score M2 to carry out quality scoring on the laser point cloud modeling data;
When DeltaL > DeltaL2, selecting the third preset quality score M3 to perform quality score on the laser point cloud modeling data;
when the i-th preset quality score Mi is selected to perform quality scoring on the laser point cloud modeling data, i=1, 2,3, determining the quality score of the laser point cloud modeling data as E1, and setting E1 = E Mi, wherein E is an initial quality score of the laser point cloud modeling data.
5. The method for identifying data of laser point cloud modeling according to claim 4, wherein when selecting an i-th preset quality score Mi to perform quality scoring on the laser point cloud modeling data and determining that the quality score of the laser point cloud modeling data is E1, comprising:
acquiring the current resolution K of the laser point cloud modeling data, and judging whether the laser point cloud modeling data is clear or not according to the relation between the current resolution K of the laser point cloud modeling data and the preset resolution K0 of the laser point cloud modeling data;
when K is more than or equal to K0, judging that the resolution of the laser point cloud modeling data is larger than or equal to the preset resolution of the laser point cloud modeling data, and judging that the laser point cloud modeling data is clear;
When K is smaller than K0, judging that the resolution of the laser point cloud modeling data is smaller than the preset resolution of the laser point cloud modeling data, judging that the laser point cloud modeling data is unclear, and adjusting the quality score E1 of the laser point cloud modeling data according to the relation between the current resolution K of the laser point cloud modeling data and the preset resolution K0 of the laser point cloud modeling data.
6. The method for recognizing laser point cloud modeling data according to claim 5, wherein determining that the laser point cloud modeling data is unclear and adjusting the laser point cloud modeling data quality score E1 according to a relationship between a current resolution K of the laser point cloud modeling data and a preset resolution K0 of the laser point cloud modeling data comprises:
acquiring a resolution difference delta K between the current resolution K of the laser point cloud modeling data and a preset resolution K0 of the laser point cloud modeling data, comparing the resolution difference delta K with the preset resolution difference delta K, and selecting a corresponding adjustment coefficient according to a comparison result to adjust a quality score E1 of the laser point cloud modeling data;
The method comprises the steps of presetting a first preset resolution difference delta K1 and a second preset resolution difference delta K2, presetting a first preset adjustment coefficient N1, presetting a second preset adjustment coefficient N2 and a third preset adjustment coefficient N3, wherein delta K1 is less than delta K2, and 0 < N1 < N2 < N3 < 0.5;
when delta K is less than or equal to delta K1, selecting the first preset adjustment coefficient N1 to adjust the quality score E1 of the laser point cloud modeling data;
when delta K1 is less than or equal to delta K2, selecting the second preset adjustment coefficient N2 to adjust the quality score E1 of the laser point cloud modeling data;
when delta K > -delta K2, selecting the third preset adjustment coefficient N3 to adjust the quality score E1 of the laser point cloud modeling data;
when the ith preset adjustment coefficient Ni is selected to adjust the quality score E1 of the laser point cloud modeling data, i=1, 2,3, and the adjusted quality score E2 of the laser point cloud modeling data is determined, and e2=e1×ni is set.
7. The method for identifying data of laser point cloud modeling according to claim 6, wherein when the i-th preset adjustment coefficient Ni is selected to adjust the quality score E1 of the laser point cloud modeling data and the adjusted quality score E2 of the laser point cloud modeling data is determined, the method comprises:
Acquiring the real-time point cloud density J of the laser point cloud modeling data, and judging whether a structure in the laser point cloud modeling data is specific or not according to the relation between the real-time point cloud density J of the laser point cloud modeling data and the preset point cloud density J0 of the laser point cloud modeling data:
when J is more than or equal to J0, judging that the point cloud density of the laser point cloud modeling data is larger than or equal to the preset point cloud density of the laser point cloud modeling data, and judging that the structure in the laser point cloud modeling data is specific;
when J is smaller than J0, judging that the point cloud density of the laser point cloud modeling data is smaller than the preset point cloud density of the laser point cloud modeling data, judging that the structure in the laser point cloud modeling data is not specific, and correcting the quality score E2 of the laser point cloud modeling data after adjustment according to the relation between the real-time point cloud density J of the laser point cloud modeling data and the preset point cloud density J0 of the laser point cloud modeling data.
8. The method for identifying data of laser point cloud modeling according to claim 7, wherein determining that the structure in the laser point cloud modeling data is not specific, and correcting the adjusted quality score E2 of the laser point cloud modeling data according to a relationship between the real-time point cloud density J of the laser point cloud modeling data and the preset point cloud density J0 of the laser point cloud modeling data, comprises:
Acquiring a point cloud density difference delta J between the real-time point cloud density J of the laser point cloud modeling data and a preset point cloud density J0 of the laser point cloud modeling data, comparing the point cloud density difference delta J with the preset point cloud density difference delta J according to the point cloud density difference delta J, and selecting a corresponding correction coefficient according to a comparison result to correct the quality score E2 of the adjusted laser point cloud modeling data;
the method comprises the steps of presetting a first preset point cloud density difference delta J1 and a second preset point cloud density difference delta J2, presetting a first correction coefficient B1, a second preset correction coefficient B2 and a third preset correction coefficient B3, wherein delta J1 < [ delta J2 ], and 0.15 < B1 < B2 < B3 < 0.3;
when delta J is less than or equal to delta J1, selecting the first preset correction coefficient B1 to correct the quality score E2 of the adjusted laser point cloud modeling data;
when delta J1 is less than or equal to delta J2, selecting the second preset correction coefficient B2 to correct the quality score E2 of the adjusted laser point cloud modeling data;
when DeltaJ > DeltaJ2, selecting the third preset correction coefficient B3 to correct the quality score E2 of the adjusted laser point cloud modeling data;
When the ith preset correction coefficient Bi is selected to correct the adjusted quality score E2 of the laser point cloud modeling data, i=1, 2 and 3, determining that the quality score of the laser point cloud modeling data after correction is E3, and setting E3=E2×Bi.
9. The method for identifying data of laser point cloud modeling as claimed in claim 8, wherein determining the quality level of the laser point cloud modeling data according to a relationship between the quality score of the laser point cloud modeling data and a preset quality score of the laser point cloud modeling data comprises:
obtaining a modified quality score E3 of the laser point cloud modeling data,
presetting a first preset quality score T1 and a second preset quality score T2, presetting a first preset quality grade Q1, a second preset quality grade Q2 and a third preset quality grade Q3, wherein T1 is smaller than T2, Q1 is smaller than Q2 and Q3;
comparing the modified quality scores E3 of the laser point cloud modeling data with each preset quality score, and selecting a corresponding preset quality grade as the quality grade of the laser point cloud modeling data according to the comparison result;
when E3 is less than or equal to T1, selecting the first preset quality grade Q1 as the quality grade of the laser point cloud modeling data;
When T1 is more than E3 and less than or equal to T2, selecting the second preset quality grade Q2 as the quality grade of the laser point cloud modeling data;
when E3 is more than T2, selecting the third preset quality grade Q3 as the quality grade of the laser point cloud modeling data;
when the i-th preset quality level Qi is selected as the laser point cloud modeling data quality level, i=1, 2,3, and the laser point cloud modeling data quality level is determined to be Q, and q=q1, Q2, Q3 … Qi is set.
10. A data recognition system for modeling a laser point cloud, adapted for use in a data recognition method for modeling a laser point cloud according to any one of claims 1 to 9, comprising:
the data acquisition module is electrically connected with the database and is used for acquiring laser point cloud modeling data to be identified.
The data cleaning module is electrically connected with the data acquisition module and is used for cleaning the laser point cloud modeling data to be identified and extracting quality information of the laser point cloud modeling data after data cleaning, wherein the quality information of the laser point cloud modeling data comprises the integrity of the laser point cloud modeling data, the resolution of the laser point cloud modeling data and the point cloud density of the laser point cloud modeling data;
The scoring module is electrically connected with the data cleaning module and is used for scoring the quality of the laser point cloud modeling data to be identified according to the quality information of the laser point cloud modeling data;
the central control module is electrically connected with the scoring module and is used for determining the quality grade of the laser point cloud modeling data according to the relation between the quality score of the laser point cloud modeling data and the preset quality score of the laser point cloud modeling data, and identifying whether the laser point cloud modeling data can be used or not according to the relation between the quality grade of the laser point cloud modeling data and the preset quality grade.
CN202311434417.4A 2023-11-01 2023-11-01 Data identification method and system for laser point cloud modeling Pending CN117372850A (en)

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