CN115330984B - Data processing method and device for suspended matter rejection - Google Patents
Data processing method and device for suspended matter rejection Download PDFInfo
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Abstract
The application discloses a data processing method and device for suspended matter rejection. The method comprises the following steps: obtaining a model to be processed; carrying out vertex extraction processing based on a preset semantic label on the model to be processed to obtain marked vertex data; carrying out space construction processing according to the marked vertex data to obtain a suspended matter detection space, wherein the suspended matter detection space is a model space with suspended matters in the three-dimensional reconstruction model; and performing suspended matter vertex deleting treatment on the suspended matter detection space to obtain a target optimization model. The method comprises the steps of extracting vertexes based on semantic segmentation from a model to be processed, constructing a suspended matter detection interval according to the extracted vertexes, and carrying out different suspended matter detection and elimination in different intervals by constructing different suspended matter detection intervals, so that the problem of low efficiency in suspended matter elimination of a three-dimensional reconstruction model in the prior art is solved, and the technical effect of improving the suspended matter elimination efficiency in the three-dimensional model is realized.
Description
Technical Field
The present application relates to the field of computers, and in particular, to a data processing method and apparatus for suspended matter rejection.
Background
Along with the continuous development of information technology, the three-dimensional scene is reconstructed through a model and then subjected to engineering or project simulation on a computer, and the three-dimensional scene is officially applied to aircrafts or other fields needing motion simulation test, and due to the influence of various complex characteristics and other factors, some noise models are suspended in the urban model, belong to noise data, the size of the reconstructed model is increased and influence on the later related space operation is achieved, so that suspended matters of the reconstructed three-dimensional model are required to be removed.
Therefore, the existing suspended matter elimination of the three-dimensional reconstruction model has the problem of low efficiency.
Disclosure of Invention
The main aim of the application is to provide a data processing method and device for removing suspended matters, so as to solve the technical problem of low efficiency of removing suspended matters in a three-dimensional reconstruction model in the prior art, and realize the technical effect of improving the efficiency of removing suspended matters in the three-dimensional model.
To achieve the above object, a first aspect of the present application proposes a data processing method for suspended matter rejection, including:
Obtaining a model to be processed, wherein the model to be processed is a three-dimensional reconstruction model with suspended matters;
carrying out vertex extraction processing based on a preset semantic label on the model to be processed to obtain marked vertex data, wherein the marked vertex data are vertex data with the preset semantic label;
performing space construction processing according to the marked vertex data to obtain a suspended matter detection space, wherein the suspended matter detection space is a model space with suspended matters in the three-dimensional reconstruction model; and
and performing suspended matter vertex deleting treatment on the suspended matter detection space to obtain a target optimization model.
Optionally, performing space construction processing according to the marked vertex data, and obtaining a suspended matter detection space includes:
identifying the marked vertex data to obtain reference marked vertex data, wherein the reference marked vertex data is used for representing vertex data with a semantic label of a road;
carrying out road plane construction processing according to the reference mark vertex data to obtain reference plane data, wherein the reference plane data is data for representing a road plane in the model to be processed; and
And carrying out model space construction processing on the basis of the reference plane data in a preset direction to obtain the suspended matter detection space, wherein the preset direction corresponds to the position of the suspended matters in the model to be processed.
Optionally, performing model space construction processing in a preset direction based on the reference plane data, and obtaining the suspended matter detection space includes:
constructing a road interval section based on the reference plane data to obtain a road space, wherein the road interval section is a model space surrounding the road plane;
identifying the road space to obtain a first road space plane and a second road space plane, wherein the first road space plane and the second road space plane are different planes of the road space;
matching the road space plane corresponding to the preset direction in the first road space plane and the second road space plane to obtain a process detection plane; and
and carrying out model space construction processing based on the process detection plane and the preset direction to obtain the suspended matter detection space, wherein the suspended matter detection space is a three-dimensional model space constructed by taking the process detection plane as a model space plane and taking the preset direction as a model space plane expansion direction.
Optionally, performing vertex extraction processing based on a preset semantic label on the model to be processed, and obtaining marked vertex data includes:
identifying the model to be processed to obtain suspended matter characteristic data, wherein the suspended matter characteristic data is used for representing suspended matter characteristics in the model to be processed;
matching semantic tags corresponding to the feature data of the suspended matters in a preset semantic tag database to obtain the preset semantic tags;
carrying out orthographic projection processing on the model to be processed to obtain a model projection image; and
and carrying out vertex extraction processing based on the preset semantic label on the model projection image to obtain marked vertex data, wherein the marked vertex data are data used for representing suspended matter vertices in the model to be processed.
Optionally, performing suspension vertex deletion processing on the suspension detection space, and obtaining the target optimization model includes:
identifying the suspended matter detection space to obtain spatial position characteristic data, wherein the spatial position characteristic data are data used for representing the position characteristic of the suspended matter detection space in the model to be optimized;
Matching semantic tags corresponding to the spatial position feature data in a preset semantic tag database to obtain a target semantic tag, wherein the target semantic tag is a semantic tag of a suspension to be deleted;
performing vertex extraction processing on the suspended matter detection space according to the target semantic tag to obtain suspended matter vertex data, wherein the suspended matter vertex data is suspended matter vertex data with the target semantic tag; and
and deleting the suspended matter vertex data in the suspended matter detection space to obtain the target optimization model.
Optionally, performing suspension vertex deletion processing on the suspension detection space, and obtaining the target optimization model includes:
identifying the suspended matter detection space to obtain a plurality of vertex data to be detected, wherein the vertex data to be detected are data of a plurality of vertexes in the suspended matter space;
detecting the plurality of vertex data to be detected based on the connected components to obtain a plurality of suspended matter vertex data, wherein the plurality of suspended matter vertex data are data of vertexes with the connected components in the vertex data to be detected; and
And deleting the vertex data of the plurality of suspended matters in the model to be optimized to obtain the target optimization model.
According to a second aspect of the present application, there is provided a data processing apparatus for suspended matter rejection, comprising:
the model acquisition module is used for acquiring a model to be processed, wherein the model to be processed is a three-dimensional reconstruction model with suspended matters;
the vertex extraction module is used for carrying out vertex extraction processing on the model to be processed based on a preset semantic label to obtain marked vertex data, wherein the marked vertex data are vertex data with the preset semantic label;
the detection space construction module is used for carrying out space construction processing according to the marked vertex data to obtain a suspended matter detection space, wherein the suspended matter detection space is a model space with suspended matters in the three-dimensional reconstruction model; and
and the result module is used for deleting the suspended matter vertex of the suspended matter detection space to obtain a target optimization model.
Optionally, the detection space construction module includes:
the identification module is used for identifying the marked vertex data to obtain reference marked vertex data, wherein the reference marked vertex data are vertex data used for representing a semantic label of a road;
The plane construction module is used for carrying out road plane construction processing according to the reference mark vertex data to obtain reference plane data, wherein the reference plane data is data for representing a road plane in the model to be processed; and
and the suspended matter detection space construction module is used for carrying out model space construction processing on the basis of the reference plane data in a preset direction to obtain the suspended matter detection space, wherein the preset direction corresponds to the position of the suspended matter in the model to be processed.
According to a third aspect of the present application, there is provided a computer-readable storage medium storing computer instructions for causing the computer to execute the above-described data processing method for suspended matter removal.
According to a fourth aspect of the present application, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform the data processing method for suspended matter rejection described above.
The technical scheme provided by the embodiment of the application can comprise the following beneficial effects:
in the application, a model to be processed is obtained, and the model to be processed is a three-dimensional reconstruction model with suspended matters; carrying out vertex extraction processing based on a preset semantic label on the model to be processed to obtain marked vertex data, wherein the marked vertex data is vertex data with the preset semantic label; carrying out space construction processing according to the marked vertex data to obtain a suspended matter detection space, wherein the suspended matter detection space is a model space with suspended matters in the three-dimensional reconstruction model; and performing suspended matter vertex deleting treatment on the suspended matter detection space to obtain a target optimization model. The method comprises the steps of extracting vertexes based on semantic segmentation from a model to be processed, constructing a suspended matter detection interval according to the extracted vertexes, and carrying out different suspended matter detection and elimination in different intervals by constructing different suspended matter detection intervals, so that the problem of low efficiency in suspended matter elimination of a three-dimensional reconstruction model in the prior art is solved, and the technical effect of improving the suspended matter elimination efficiency in the three-dimensional model is realized.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, are included to provide a further understanding of the application and to provide a further understanding of the application with regard to the other features, objects and advantages of the application. The drawings of the illustrative embodiments of the present application and their descriptions are for the purpose of illustrating the present application and are not to be construed as unduly limiting the present application. In the drawings:
FIG. 1 is a flow chart of a data processing method for suspended matter rejection provided herein;
FIG. 2 is a flow chart of a data processing method for suspended matter rejection provided herein;
FIG. 3 is a flow chart of a data processing method for suspended matter rejection provided herein;
FIGS. 4 and 5 are schematic diagrams of one construction of pavement spacing space provided herein;
FIG. 6 is a schematic diagram of one embodiment of a constructed suspension detection space provided herein;
FIG. 7 is a schematic diagram of a three-dimensional reconstruction model interval provided herein;
FIG. 8 is a schematic diagram of a data processing apparatus for suspended matter removal provided in the present application;
fig. 9 is a schematic structural diagram of another data processing device for suspended matter removal provided in the present application.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe the embodiments of the present application described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In the present application, the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal" and the like indicate an azimuth or a positional relationship based on that shown in the drawings. These terms are used primarily to better describe the present application and its embodiments and are not intended to limit the indicated device, element or component to a particular orientation or to be constructed and operated in a particular orientation.
Also, some of the terms described above may be used to indicate other meanings in addition to orientation or positional relationships, for example, the term "upper" may also be used to indicate some sort of attachment or connection in some cases. The specific meaning of these terms in this application will be understood by those of ordinary skill in the art as appropriate.
Furthermore, the terms "mounted," "configured," "provided," "connected," "coupled," and "sleeved" are to be construed broadly. For example, "connected" may be in a fixed connection, a removable connection, or a unitary construction; may be a mechanical connection, or an electrical connection; may be directly connected, or indirectly connected through intervening media, or may be in internal communication between two devices, elements, or components. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art as the case may be.
When the three-dimensional model is constructed, due to the influence of various complex characteristics and other factors in the construction of the model, some noise models float in the three-dimensional model, are not communicated with the model structure of the three-dimensional model, and are suspended in the three-dimensional model to form suspended matters, wherein the suspended matters are noise models which are not communicated with the model structure in the three-dimensional model, the suspended matters increase the data quantity of the three-dimensional model and influence the follow-up space treatment aiming at the three-dimensional model, and the suspended matters in the model need to be removed.
Fig. 1 is a flowchart of a data processing method for suspended matter removal provided in the present application, as shown in fig. 1, the method includes the following steps:
s101: obtaining a model to be processed;
the model to be processed is a three-dimensional reconstruction model with suspended matters, the three-dimensional reconstruction model is a three-dimensional reconstruction model comprising an urban model, the suspended matters are distributed at different positions in the three-dimensional reconstruction model, and different suspended matters exist in the three-dimensional reconstruction model according to different types of the suspended matters.
In an alternative embodiment of the present application, obtaining the model to be processed includes: obtaining an initial three-dimensional model, wherein the initial three-dimensional model is a model obtained after the three-dimensional model is constructed, such as an urban three-dimensional model, and performing semantic tag pretreatment on the initial three-dimensional model to obtain the model to be processed. The semantic label preprocessing of the initial three-dimensional model comprises the following steps: acquiring initial three-dimensional model data, wherein the initial three-dimensional model data is data for representing an initial three-dimensional model and comprises vertex data, image data and the like in the initial three-dimensional model; identifying initial three-dimensional model data to obtain image data, wherein the image data is data of model images in the initial three-dimensional model, carrying out semantic label distribution processing on each pixel in the model images based on a preset convolutional neural network model to obtain preprocessed model images, and carrying out semantic label distribution processing on model vertexes and model images in the initial three-dimensional model based on the preset convolutional neural network model to obtain a model to be processed. For example, if the initial three-dimensional image is a three-dimensional model of a city, the three-dimensional model of the city includes markers such as roads, trees, houses, etc. for representing different marks in the city, semantic label distribution processing based on a preset convolutional neural network model is performed on the three-dimensional model of the initial city, semantic label distribution is performed on model vertices in the three-dimensional model of the initial city for representing various city markers, road vertex marking is performed on pixels in the three-dimensional model of the city for representing model vertices of the roads, tree vertex marking is performed on pixels in the three-dimensional model of the city for representing model vertices of the trees, etc., so as to obtain the city model to be processed.
In the embodiment of the application, the label with the semantic category marked on the vertex of the three-dimensional model is obtained by preprocessing the three-dimensional model based on the semantic label, so that in the model optimization process, the label corresponding to the requirement in the three-dimensional model is conveniently optimized based on the model optimization requirement of a user, and the optimization efficiency in the three-dimensional model is improved.
S102: carrying out vertex extraction processing based on a preset semantic label on the model to be processed to obtain marked vertex data;
marking vertex data is vertex data with preset semantic labels, the preset semantic labels comprise semantic labels of a road and semantic labels corresponding to suspended matters, the road semantic labels are used for marking and extracting road vertices in a model to be processed, a road plane is constructed through the road vertices, model space segmentation is carried out on the model to be processed through the constructed road plane, suspended matters are respectively detected and removed for different three-dimensional model spaces, suspended matters are removed through arrangement of subareas, and suspended matters removing efficiency is improved; and the semantic tags corresponding to the suspended matters correspond to the suspended matters, and the suspended matters in the model to be processed are removed according to different suspended matters, so that the suspended matters removing efficiency is improved.
Fig. 2 is a flowchart of a data processing method for suspended matter removal provided in the present application, as shown in fig. 2, the method includes the following steps:
s201: identifying a model to be processed to obtain feature data of suspended matters;
the suspension characteristic data is data for representing the characteristics of the suspension in the model to be processed, the model to be processed is identified, and the first suspension characteristic data and the second suspension characteristic data are obtained, wherein the first suspension characteristic data are data of the first suspension, the second suspension characteristic data are data of the second suspension, the first suspension and the second suspension can be suspensions of different types, such as suspension trees or suspension street lamps in an urban model, and the first suspension and the second suspension can be suspensions of different positions, such as suspensions in the urban model (including suspension trees, suspension street lamps, suspension buildings, and the like) and suspensions below the urban model (noise data below a pavement plane).
S202: matching semantic tags corresponding to the feature data of the suspended matters in a preset semantic tag database to obtain preset semantic tags;
and identifying the feature data of the suspended matters to obtain position feature data of the suspended matters, wherein the position feature data of the suspended matters are used for representing the positions of the suspended matters in the three-dimensional reconstruction model, if the suspended matters are suspended matters below the urban model (such as suspended matters model generated by noise data below urban pavement), road semantic labels are obtained, a suspended matter detection space below the urban model is constructed through the road semantic labels, the suspended matters model generated by the noise data possibly does not have corresponding semantic labels, suspension eliminating operation is difficult to be carried out on the suspended matters model generated by the noise data through vertex extraction based on the semantic labels, and a suspended matters detection interval is constructed through the road semantic labels, so that the effect of eliminating suspended matters in the three-dimensional model is improved.
If the suspended matters are suspended matters (such as suspended trees, suspended street lamps, suspended buildings and the like) in the city model, road semantic tags (such as tree semantic tags, street lamp semantic tags, building semantic tags and the like) corresponding to the suspended matters types are obtained, suspended matter vertex extraction in the three-dimensional model is carried out by setting the semantic tags corresponding to the suspended matters types, the effect of removing suspended matters according to the types is achieved, and the efficiency of removing suspended matters in the three-dimensional model is improved.
In another optional embodiment of the present application, if the suspended matter is a suspended matter (such as a suspended tree, a suspended street lamp, a suspended building, etc.) located in the city model, in addition to obtaining a road semantic tag (such as a tree semantic tag, a street lamp semantic tag, a building semantic tag, etc.) corresponding to the type of the suspended matter, a road semantic tag is obtained, and construction of a road plane is performed through the road semantic tag. The range of the suspended matter identification interval is determined based on the extracted road interval, the suspended matter identification interval is not required to be constructed based on suspended matter vertexes, the calculation power consumption for constructing the detection interval through the suspended matter vertexes is reduced, the efficiency of constructing the suspended matter identification interval is improved, and the technical effect of improving the efficiency of eliminating suspended matters in the three-dimensional model is achieved.
S203: carrying out orthographic projection treatment on the model to be treated to obtain a model projection image;
the model to be processed is a three-dimensional reconstruction model, and when the vertexes in the three-dimensional reconstruction model are extracted, orthographic projection is carried out on the three-dimensional reconstruction model, so that an orthographic projection image of the three-dimensional reconstruction model is obtained.
The model to be processed is a three-dimensional reconstruction model, when vertexes in the three-dimensional reconstruction model are extracted, orthographic projection is carried out on the three-dimensional reconstruction model to obtain an orthographic projection image of the three-dimensional reconstruction model, and road distribution in the three-dimensional reconstruction model can be conveniently identified through a top view.
S204: and carrying out vertex extraction processing based on a preset semantic label on the model projection image to obtain marked vertex data.
The marked vertex data is data representing vertices of suspensions in the model to be processed. The preset semantic label can also be a road semantic label, and the road vertex corresponding to the road semantic label is extracted from the model projection image to obtain marked vertex data.
In the embodiment of the application, the road vertex corresponding to the road semantic label in the model to be processed is extracted, so that the road vertex in the three-dimensional reconstruction model and the suspended matter vertex are extracted, the suspended matter detection interval is conveniently constructed based on the road vertex through extraction, the technical effect of improving the suspended matter removing efficiency in the three-dimensional model is realized, the suspended matter is removed through the extracted suspended matter vertex, and the technical effect of improving the suspended matter removing efficiency in the three-dimensional model is realized.
S103: carrying out space construction processing according to the marked vertex data to obtain a suspended matter detection space;
the suspended matter detection space is a model space with suspended matters in the three-dimensional reconstruction model.
Fig. 3 is a flowchart of a data processing method for suspended matter removal provided in the present application, as shown in fig. 3, the method includes the following steps:
s301: identifying the marked vertex data to obtain reference marked vertex data;
the reference mark vertex data is used for representing vertex data with a road semantic label, the road has the function of positioning reference on the distribution of markers in the three-dimensional model, the suspended matter identification interval is determined based on the extracted road interval, the suspended matter identification interval is not required to be constructed based on suspended matter vertices, the calculation power consumption for constructing the detection interval through the suspended matter vertices is reduced, the efficiency of constructing the suspended matter identification interval is improved, and the technical effect of improving the model optimization efficiency is realized.
S302: carrying out road plane construction processing according to the reference mark vertex data to obtain reference plane data;
the reference plane data are data for representing the road plane in the model to be processed, three reference mark vertexes are randomly taken out from the reference mark vertex data, whether the three reference mark vertexes are collinear or not is judged, if not, plane construction is carried out according to the three non-collinear road reference mark vertexes, and a reference plane is obtained; if collinear, the selection is re-performed until a single road reference marker vertex that is not collinear is obtained. And calculating the distances between a plurality of reference mark vertexes in the reference mark vertexes and the reference plane, judging whether the distances between the reference mark vertexes and the reference plane meet a preset distance threshold value or not, wherein the reference mark vertexes meeting the preset distance threshold value are reference plane points, when the number of the reference plane points meet the preset reference plane point threshold value, obtaining a road reference plane, and when the number of the reference plane points meet the reference plane of the preset reference plane point threshold value, obtaining a reference plane of the road reference plane, wherein the number of the reference plane points corresponding to the reference plane meets the reference plane of the preset reference plane point threshold value.
S303: and carrying out model space construction processing on the basis of the reference plane data in a preset direction to obtain a suspended matter detection space.
Constructing a road interval section based on reference plane data to obtain a road space, wherein the road interval section is a model space surrounding the road plane; identifying road space to obtain a first road space plane and a second road space plane, wherein the first road space plane and the second road space plane are planes with different road spaces; matching road space planes corresponding to a preset direction in a first road space plane and the second road space plane to obtain a process detection plane; and performing model space construction processing based on the process detection plane and a preset direction to obtain a suspended matter detection space, wherein the suspended matter detection space is a three-dimensional model space constructed by taking the process detection plane as a model space plane and taking the preset direction as a model space plane expansion direction.
And constructing a road interval section based on the reference plane data to obtain a road space, wherein the road interval section is a model space surrounding a road plane, the road interval section extends to two sides based on the road reference plane respectively, a first road space is determined according to the farthest reference mark vertexes of the two sides from the reference plane respectively, the distance between the farthest reference mark vertexes of the first side and the reference plane is the distance between the farthest reference mark vertexes of the second side and the reference plane, the distance between the farthest reference mark vertexes of the second side and the reference plane is the distance between the farthest reference mark vertexes of the second side and the reference plane, the first road space is in a +height, a second road space is constructed according to the first road space, the second road space is below the first road space, and the height of the second road space is in a threshold range of between [5% and 15% ].
Vertex extraction processing based on preset semantic labels is carried out on the model projection image, so that a set R= { of road vertices marked with the road semantic labels is obtained, the construction of a road reference plane based on the set of road vertices identified above, and obtaining a road reference plane, and calculating the furthest point from the plane positive plane and the furthest point from the plane negative plane in R, wherein the furthest distance is the furthest point from the plane negative plane, a rectangular bounding box is constructed, and the height of the bounding box is as shown in fig. 4. Based on this, the bounding box expands downward by a detection space of a height (between [5% and 15% of threshold range ]), and the space formed by this bounding box is the road surface space, as shown in fig. 5.
The vertex set p= {, the first and second areas of all three-dimensional models below the "road surface space" are acquired, the point below the "road surface space" and farthest therefrom is acquired, the distance from the road surface space to the road surface space lower plane is the height of the suspended matter detection section, and the suspended matter detection space is constructed and obtained as shown in fig. 6.
In the three-dimensional reconstruction model, the three-dimensional reconstruction model is divided into different model sections according to a road plane, as shown in fig. 7, and after the suspension detection construction treatment is performed on the three-dimensional reconstruction model, a plurality of model spaces are obtained, namely, a first model space 41, a second model space 42 and a third model space 43 from top to bottom, wherein the second model space 42 is a road space section surrounding a road reference plane, the first model space 41 is a city model section located on the road space section, and the third model space 43 is a suspended matter detection space located below the road space section.
In another alternative embodiment of the present application, if the city model section above the road plane is subjected to suspended matter detection and rejection, the model section is constructed based on the upper plane of the first road space upwards, so as to obtain the city model suspended matter detection space.
S104: and performing suspended matter vertex deleting treatment on the suspended matter detection space to obtain a target optimization model.
Identifying a suspended matter detection space to obtain a plurality of vertex data to be detected, wherein the vertex data to be detected are the data of a plurality of vertexes in the suspended matter space; detecting the plurality of vertex data to be detected based on the connected components to obtain a plurality of suspended matter vertex data, wherein the plurality of suspended matter vertex data are the data of the vertices with the connected components in the vertex data to be detected; and deleting the vertex data of the plurality of suspended matters in the model to be optimized to obtain the target optimization model.
For example, the suspension detection space is a three-dimensional model space below the road plane, the set q= {,.+ -. Of all vertices in the suspension detection space is calculated, and based on the three-dimensional reconstruction model, acquiring all connected components in the suspension detection space, and deleting vertex and patch data corresponding to the connected components from the original model.
In another alternative embodiment of the present application, there is provided a data processing method for suspended matter rejection, including: identifying a suspended matter detection space to obtain spatial position characteristic data, wherein the spatial position characteristic data are data used for representing the position characteristics of the suspended matter detection space in a model to be optimized; matching semantic tags corresponding to the spatial position feature data in a preset semantic tag database to obtain target semantic tags, wherein the target semantic tags are semantic tags of suspended matters to be deleted; performing vertex extraction processing on the suspended matter detection space according to the target semantic tag to obtain suspended matter vertex data, wherein the suspended matter vertex data are suspended matter vertex data with the target semantic tag; and deleting the suspended matter vertex data in the suspended matter detection space to obtain a target optimization model.
For example, if the suspended matter detection space is an urban model space above a road plane, semantic tags corresponding to suspended matters to be removed in the urban model space are obtained, target semantic tags are obtained, if the suspended matters to be removed in the urban model space are suspended trees, suspended street lamps or suspended buildings and the like, corresponding tree semantic tags, street lamp semantic tags or building semantic tags and the like are obtained in a preset semantic tag database, and detection and removal processing is performed on the suspended matters in the urban model based on the target semantic tags.
The detection and elimination treatment of suspended matters in the city model comprises the following steps: and carrying out vertex extraction processing on the model to be processed based on a preset semantic tag to obtain marked vertex data, wherein the marked vertex data is data extracted from the city model, the marked vertex data is vertex data with the preset semantic tag, the preset semantic tag corresponds to suspended matters in the model to be processed, and carrying out suspended matter judgment processing on the marked vertex data based on a preset suspended matter rule to obtain suspended matter vertex data. And deleting the suspended matter vertex data to obtain the target optimization model.
The suspension judgment processing for the marked vertex data based on the preset suspension rule comprises the following steps: clustering the marked vertex data to obtain first vertex data, wherein the first vertex data is used for representing clustered first vertices; performing vertex growth processing on the first vertex based on a preset topological structure to obtain second vertex data, wherein the second vertex data are data used for representing the second vertex obtained by the first vertex through multiple topological growth; traversing the second vertex data in the preset traversal vertex database to judge whether the second vertex data exists in the preset traversal vertex database, and if the second vertex data exists in the preset traversal vertex database, marking the vertex data to meet a preset suspended matter rule to obtain suspended matter vertex data; and if the second vertex data does not exist in the preset traversal vertex database, performing vertex growth processing on the first vertex based on the topological structure in an iterative manner until the second vertex data exists in the traversal vertex database, and obtaining the suspended matter vertex data.
Fig. 8 is a schematic structural diagram of a data processing apparatus for suspended matter removal provided in the present application, as shown in fig. 8, the apparatus includes:
the model obtaining module 51 is configured to obtain a model to be processed, where the model to be processed is a three-dimensional reconstruction model with suspended matters;
the vertex extraction module 52 is configured to perform vertex extraction processing based on a preset semantic tag on the model to be processed to obtain labeled vertex data, where the labeled vertex data is vertex data with the preset semantic tag;
the detection space construction module 53 is configured to perform space construction processing according to the labeled vertex data to obtain a suspended matter detection space, where the suspended matter detection space is a model space with suspended matters in the three-dimensional reconstruction model; and
and a result module 54, configured to perform suspension vertex deletion processing on the suspension detection space, so as to obtain a target optimization model.
Fig. 9 is a schematic structural diagram of another data processing apparatus for suspended matter removal provided in the present application, as shown in fig. 9, the apparatus includes:
the identifying module 61 is configured to identify the marked vertex data to obtain reference marked vertex data, where the reference marked vertex data is vertex data used for representing a semantic label of a link;
The plane construction module 62 is configured to perform road plane construction processing according to the reference mark vertex data to obtain reference plane data, where the reference plane data is data for representing a road plane in the model to be processed;
the suspended matter detection space construction module 63 performs model space construction processing in a preset direction based on the reference plane data, and obtains a suspended matter detection space, wherein the preset direction corresponds to the position of the suspended matter in the model to be processed.
The specific manner in which the operations of the units in the above embodiments are performed has been described in detail in the embodiments related to the method, and will not be described in detail here.
In summary, in the present application, a model to be processed is obtained, where the model to be processed is a three-dimensional reconstruction model with suspended matters; carrying out vertex extraction processing based on a preset semantic label on the model to be processed to obtain marked vertex data, wherein the marked vertex data is vertex data with the preset semantic label; carrying out space construction processing according to the marked vertex data to obtain a suspended matter detection space, wherein the suspended matter detection space is a model space with suspended matters in the three-dimensional reconstruction model; and performing suspended matter vertex deleting treatment on the suspended matter detection space to obtain a target optimization model. The method comprises the steps of extracting vertexes based on semantic segmentation from a model to be processed, constructing a suspended matter detection interval according to the extracted vertexes, and carrying out different suspended matter detection and elimination in different intervals by constructing different suspended matter detection intervals, so that the problem of low efficiency in suspended matter elimination of a three-dimensional reconstruction model in the prior art is solved, and the technical effect of improving the suspended matter elimination efficiency in the three-dimensional model is realized.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
It will be apparent to those skilled in the art that the elements or steps of the application described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, or they may alternatively be implemented in program code executable by computing devices, such that they may be stored in a memory device for execution by the computing devices, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps within them may be fabricated into a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the same, but rather, various modifications and variations may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (8)
1. A data processing method for suspended matter rejection, comprising:
obtaining a model to be processed, wherein the model to be processed is a three-dimensional reconstruction model with suspended matters;
carrying out vertex extraction processing based on a preset semantic label on the model to be processed to obtain marked vertex data, wherein the marked vertex data are vertex data with the preset semantic label;
performing space construction processing according to the marked vertex data to obtain a suspended matter detection space, wherein the suspended matter detection space is a model space with suspended matters in the three-dimensional reconstruction model; and
performing suspended matter vertex deleting treatment on the suspended matter detection space to obtain a target optimization model;
identifying the marked vertex data to obtain reference marked vertex data, wherein the reference marked vertex data is used for representing vertex data with a semantic label of a road;
carrying out road plane construction processing according to the reference mark vertex data to obtain reference plane data, wherein the reference plane data is data for representing a road plane in the model to be processed; and
Performing model space construction processing on the basis of the reference plane data in a preset direction to obtain the suspended matter detection space, wherein the preset direction corresponds to the position of the suspended matter in the model to be processed;
the suspension judgment processing for the marked vertex data based on the preset suspension rule comprises the following steps: clustering the marked vertex data to obtain first vertex data, wherein the first vertex data is used for representing clustered first vertices; performing vertex growth processing on the first vertex based on a preset topological structure to obtain second vertex data, wherein the second vertex data are data used for representing the second vertex obtained by the first vertex through multiple topological growth; traversing the second vertex data in the preset traversal vertex database to judge whether the second vertex data exists in the preset traversal vertex database, and if the second vertex data exists in the preset traversal vertex database, marking the vertex data to meet a preset suspended matter rule to obtain suspended matter vertex data; and if the second vertex data does not exist in the preset traversal vertex database, performing vertex growth processing on the first vertex based on the topological structure in an iterative manner until the second vertex data exists in the traversal vertex database, and obtaining the suspended matter vertex data.
2. The data processing method according to claim 1, wherein performing model space construction processing in a preset direction based on the reference plane data, obtaining the suspended matter detection space includes:
constructing a road interval section based on the reference plane data to obtain a road space, wherein the road interval section is a model space surrounding the road plane;
identifying the road space to obtain a first road space plane and a second road space plane, wherein the first road space plane and the second road space plane are different planes of the road space;
matching the road space plane corresponding to the preset direction in the first road space plane and the second road space plane to obtain a process detection plane; and
and carrying out model space construction processing based on the process detection plane and the preset direction to obtain the suspended matter detection space, wherein the suspended matter detection space is a three-dimensional model space constructed by taking the process detection plane as a model space plane and taking the preset direction as a model space plane expansion direction.
3. The data processing method according to claim 1, wherein performing vertex extraction processing based on a preset semantic label on the model to be processed to obtain labeled vertex data includes:
Identifying the model to be processed to obtain suspended matter characteristic data, wherein the suspended matter characteristic data is used for representing suspended matter characteristics in the model to be processed;
matching semantic tags corresponding to the feature data of the suspended matters in a preset semantic tag database to obtain the preset semantic tags;
carrying out orthographic projection processing on the model to be processed to obtain a model projection image; and
and carrying out vertex extraction processing based on the preset semantic label on the model projection image to obtain marked vertex data, wherein the marked vertex data are data used for representing suspended matter vertices in the model to be processed.
4. The data processing method according to claim 1, wherein performing a suspended matter vertex deletion process on the suspended matter detection space to obtain a target optimization model includes:
identifying the suspended matter detection space to obtain spatial position characteristic data, wherein the spatial position characteristic data are data used for representing the position characteristics of the suspended matter detection space in the model to be processed;
matching semantic tags corresponding to the spatial position feature data in a preset semantic tag database to obtain a target semantic tag, wherein the target semantic tag is a semantic tag of a suspension to be deleted;
Performing vertex extraction processing on the suspended matter detection space according to the target semantic tag to obtain suspended matter vertex data, wherein the suspended matter vertex data is suspended matter vertex data with the target semantic tag; and
and deleting the suspended matter vertex data in the suspended matter detection space to obtain the target optimization model.
5. The data processing method according to claim 1, wherein performing a suspended matter vertex deletion process on the suspended matter detection space to obtain a target optimization model includes:
identifying the suspended matter detection space to obtain a plurality of vertex data to be detected, wherein the vertex data to be detected are data of a plurality of vertexes in the suspended matter space;
detecting the plurality of vertex data to be detected based on the connected components to obtain a plurality of suspended matter vertex data, wherein the plurality of suspended matter vertex data are data of vertexes with the connected components in the vertex data to be detected; and
and deleting the vertex data of the plurality of suspended matters in the model to be processed to obtain the target optimization model.
6. A data processing apparatus for suspended matter rejection, comprising:
the model acquisition module is used for acquiring a model to be processed, wherein the model to be processed is a three-dimensional reconstruction model with suspended matters;
the vertex extraction module is used for carrying out vertex extraction processing on the model to be processed based on a preset semantic label to obtain marked vertex data, wherein the marked vertex data are vertex data with the preset semantic label;
the detection space construction module is used for carrying out space construction processing according to the marked vertex data to obtain a suspended matter detection space, wherein the suspended matter detection space is a model space with suspended matters in the three-dimensional reconstruction model; and
the result module is used for deleting suspended matter vertexes of the suspended matter detection space to obtain a target optimization model;
wherein, the detection space construction module includes:
the identification module is used for identifying the marked vertex data to obtain reference marked vertex data, wherein the reference marked vertex data are vertex data used for representing a semantic label of a road;
the plane construction module is used for carrying out road plane construction processing according to the reference mark vertex data to obtain reference plane data, wherein the reference plane data is data for representing a road plane in the model to be processed; and
The suspended matter detection space construction module is used for carrying out model space construction processing on the basis of the reference plane data in a preset direction to obtain the suspended matter detection space, wherein the preset direction corresponds to the position of the suspended matter in the model to be processed;
the suspension judgment processing for the marked vertex data based on the preset suspension rule comprises the following steps: clustering the marked vertex data to obtain first vertex data, wherein the first vertex data is used for representing clustered first vertices; performing vertex growth processing on the first vertex based on a preset topological structure to obtain second vertex data, wherein the second vertex data are data used for representing the second vertex obtained by the first vertex through multiple topological growth; traversing the second vertex data in the preset traversal vertex database to judge whether the second vertex data exists in the preset traversal vertex database, and if the second vertex data exists in the preset traversal vertex database, marking the vertex data to meet a preset suspended matter rule to obtain suspended matter vertex data; and if the second vertex data does not exist in the preset traversal vertex database, performing vertex growth processing on the first vertex based on the topological structure in an iterative manner until the second vertex data exists in the traversal vertex database, and obtaining the suspended matter vertex data.
7. A computer-readable storage medium storing computer instructions for causing the computer to execute the data processing method for suspended matter removal according to any one of claims 1 to 5.
8. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor to cause the at least one processor to perform the data processing method for suspended matter rejection of any one of claims 1-5.
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