CN118015221A - Building model construction method, device, electronic equipment and computer readable medium - Google Patents
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Abstract
Embodiments of the present disclosure disclose building model construction methods, apparatus, electronic devices, and computer-readable media. One embodiment of the method comprises the following steps: building a building object model based on building design information of the target building object; adjusting the building object model according to the building adjustment demand information; for each building sub-object parameter information, the following processing steps are performed: determining the parameter similarity meeting the preset condition in the parameter similarity set as an alternative parameter similarity; sending each historical building sub-object diagram corresponding to the alternative parameter similarity group to a user terminal; in response to receiving the selection information sent by the user terminal, determining a target historical building sub-object diagram according to the selection information; and rendering the adjusted building object model according to each target historical building sub-object diagram to obtain a rendered adjusted building object model. According to the embodiment, the construction time of the building model is shortened, and the accuracy of building construction is improved.
Description
Technical Field
Embodiments of the present disclosure relate to the field of building model construction, and in particular, to a building model construction method, apparatus, electronic device, and computer readable medium.
Background
Currently, it is often necessary to construct in advance a three-dimensional building model of a building to be constructed before building construction is performed. Currently, for building models, the following methods are generally adopted: and constructing the building model by technicians according to the demand text provided by the users.
However, the following technical problem generally exists in the above manner: and the model is built only according to the required text, so that the initially built building model does not meet the requirements of users, and needs to be repeatedly modified, so that the building model is long in building time.
The above information disclosed in this background section is only for enhancement of understanding of the background of the inventive concept and, therefore, may contain information that does not form the prior art that is already known to those of ordinary skill in the art in this country.
Disclosure of Invention
The disclosure is in part intended to introduce concepts in a simplified form that are further described below in the detailed description. The disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose building model construction methods, apparatus, electronic devices, and computer readable media to solve one or more of the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide a building model construction method, the method comprising: building a building object model based on building design information of the target building object; responding to the received building adjustment demand information sent by the user terminal, and adjusting the building object model according to the building adjustment demand information to obtain an adjusted building object model, wherein the adjusted building object model comprises a building sub-object group, and each building sub-object corresponds to one building sub-object parameter information; for each building sub-object parameter information, the following processing steps are performed: determining the type of the building sub-object corresponding to the building sub-object parameter information; acquiring a historical building sub-object diagram set corresponding to the building sub-object type, wherein each historical building sub-object diagram corresponds to historical building sub-object parameter information; determining the parameter similarity of the building sub-object parameter information and each historical building sub-object parameter information to obtain a parameter similarity set; determining the parameter similarity meeting the preset condition in the parameter similarity set as an alternative parameter similarity to obtain an alternative parameter similarity group; transmitting each historical building sub-object diagram corresponding to the alternative parameter similarity group to the user terminal; responding to receiving the selection information corresponding to each history building sub-object diagram sent by the user terminal, and determining a target history building sub-object diagram according to the selection information; and rendering the adjusted building object model according to the determined target historical building sub-object diagrams to obtain a rendered adjusted building object model serving as a target building model.
In a second aspect, some embodiments of the present disclosure provide a building model construction apparatus, the apparatus comprising: a construction unit configured to construct a building object model based on building design information of the target building object; the adjusting unit is configured to respond to the received building adjustment demand information sent by the user terminal, adjust the building object model according to the building adjustment demand information to obtain an adjusted building object model, wherein the adjusted building object model comprises a building sub-object group, and each building sub-object corresponds to one building sub-object parameter information; a determining unit configured to perform, for each building sub-object parameter information, the following processing steps: determining the type of the building sub-object corresponding to the building sub-object parameter information; acquiring a historical building sub-object diagram set corresponding to the building sub-object type, wherein each historical building sub-object diagram corresponds to historical building sub-object parameter information; determining the parameter similarity of the building sub-object parameter information and each historical building sub-object parameter information to obtain a parameter similarity set; determining the parameter similarity meeting the preset condition in the parameter similarity set as an alternative parameter similarity to obtain an alternative parameter similarity group; transmitting each historical building sub-object diagram corresponding to the alternative parameter similarity group to the user terminal; responding to receiving the selection information corresponding to each history building sub-object diagram sent by the user terminal, and determining a target history building sub-object diagram according to the selection information; and the rendering unit is configured to render the adjusted building object model according to the determined target historical building sub-object diagrams to obtain a rendered adjusted building object model serving as a target building model.
In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors causes the one or more processors to implement the method described in any of the implementations of the first aspect above.
In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.
The above embodiments of the present disclosure have the following advantageous effects: by the building model construction method of some embodiments of the present disclosure, building model construction time is shortened, and building construction accuracy is improved. Specifically, the construction time of the building model is long because: and constructing a model according to the required text only, so that the initially constructed building model does not meet the requirements of users and needs to be repeatedly modified. Based on this, the building model construction method of some embodiments of the present disclosure first constructs a building object model based on building design information of a target building object. Thus, the building object model can be preliminarily constructed. And secondly, in response to receiving the building adjustment demand information sent by the user terminal, adjusting the building object model according to the building adjustment demand information to obtain an adjusted building object model. The building object adjusting model comprises a building sub-object group, and each building sub-object corresponds to one piece of building sub-object parameter information. Thus, the building object model can be adjusted according to the adjustment demand information of the user. Then, for each building sub-object parameter information, the following processing steps are performed: determining the type of the building sub-object corresponding to the building sub-object parameter information; acquiring a historical building sub-object diagram set corresponding to the building sub-object type, wherein each historical building sub-object diagram corresponds to historical building sub-object parameter information; determining the parameter similarity of the building sub-object parameter information and each historical building sub-object parameter information to obtain a parameter similarity set; determining the parameter similarity meeting the preset condition in the parameter similarity set as an alternative parameter similarity to obtain an alternative parameter similarity group; transmitting each historical building sub-object diagram corresponding to the alternative parameter similarity group to the user terminal; and determining a target historical building sub-object diagram according to the selection information in response to receiving the selection information corresponding to each historical building sub-object diagram sent by the user terminal. Therefore, interaction with the user side can be performed, and the building graph corresponding to each building sub-object is determined. And finally, rendering the adjusted building object model according to the determined target historical building sub-object diagrams to obtain a rendered adjusted building object model serving as a target building model. Therefore, building model construction time is shortened, and building construction accuracy is improved.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
FIG. 1 is a flow chart of some embodiments of a building model construction method according to the present disclosure;
FIG. 2 is a flow chart of some embodiments of a building model construction apparatus according to the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a flow chart of some embodiments of a building model construction method according to the present disclosure. A flow 100 of some embodiments of a building model construction method according to the present disclosure is shown. The building model construction method comprises the following steps:
step 101, building a building object model based on building design information of a target building object.
In some embodiments, an executing subject (e.g., computing device) of the building model construction method may construct a building object model based on building design information of a target building object. The building design information may refer to construction attribute information of a target building object set in advance. The target building object may refer to a three-dimensional model of a building to be constructed. The architectural design information may include, but is not limited to: geometry, material properties and action characteristics of the building, and various parameters (parameters such as length, width, height, inner diameter and the like). That is, the execution subject may construct the building object model by building model construction software according to parameters recorded in the building design information. For example, the building object model may be drawn by CAD (Computer AIDED DRAFTING) drawing software. The building object model may refer to a model image of a three-dimensional building.
And step 102, in response to receiving the building adjustment requirement information sent by the user terminal, adjusting the building object model according to the building adjustment requirement information to obtain an adjusted building object model.
In some embodiments, the executing entity may adjust the building object model according to the building adjustment requirement information in response to receiving the building adjustment requirement information sent by the user terminal, to obtain an adjusted building object model. The building object adjusting model comprises a building sub-object group, and each building sub-object corresponds to one piece of building sub-object parameter information. The building adjustment requirement information may refer to information transmitted by the user for adjustment modification of the size and/or style of the building object model. For example, the building adjustment requirement information may include information of a position, a size, and a shape modified for the building object model. That is, the building object model can be modified and adjusted by CAD (Computer AIDED DRAFTING) drawing software to obtain an adjusted building object model.
It should be noted that the adjustment building object model is composed of a building sub-object group, and one building sub-object represents one building component. For example, a building sub-object may refer to a single building component such as a foundation pit, a bearing platform, a frame column, a ground beam, a main rib, a structural column, a brick pillar, a stair, a continuous beam, or a single building component such as a door or a window. The building sub-object parameter information may refer to parameter description information for a certain building sub-object, and may include: size parameter information, style information, and the like.
Step 103, for each building sub-object parameter information, performing the following processing steps:
Step 1031, determining the building sub-object type corresponding to the building sub-object parameter information.
In some embodiments, the execution body may determine a building sub-object type corresponding to the building sub-object parameter information. The building sub-object type may represent a component type of the building sub-object. For example, building sub-object types may include, but are not limited to: foundation pit, bearing platform, frame column, floor beam, main rib, structural column, brick pile, stairs, door, window, etc.
Step 1032, obtaining a historical building sub-object atlas corresponding to the building sub-object type.
In some embodiments, the execution body may obtain a historical building sub-object atlas corresponding to the building sub-object type. Wherein each historic building sub-object diagram corresponds to historic building sub-object parameter information. That is, a constructed complete history of building sub-object atlases pointed to by the above building sub-object types may be obtained from a local database.
Step 1033, determining the parameter similarity between the building sub-object parameter information and each historical building sub-object parameter information, and obtaining a parameter similarity set.
In some embodiments, the executing entity may determine the parameter similarity between the building sub-object parameter information and each historical building sub-object parameter information, to obtain a parameter similarity set. In practice, the executing body can determine the parameter similarity between the building sub-object parameter information and each historical building sub-object parameter information through a cosine similarity formula, so as to obtain a parameter similarity set.
And 1034, determining the parameter similarity meeting the preset condition in the parameter similarity set as an alternative parameter similarity, and obtaining an alternative parameter similarity group.
In some embodiments, the executing body may determine, as the candidate parameter similarity, the parameter similarity in the parameter similarity set that satisfies the preset condition, to obtain the candidate parameter similarity set. The preset conditions may be: the parameter similarity is greater than or equal to the preset similarity. For example, the preset similarity may be 0.95.
And step 1035, transmitting each historical building sub-object diagram corresponding to the candidate parameter similarity group to the user terminal.
In some embodiments, the executing body may send each historical building sub-object diagram corresponding to the candidate parameter similarity set to the user terminal.
Step 1036, in response to receiving the selection information corresponding to each of the history building sub-object diagrams sent by the user terminal, determining a target history building sub-object diagram according to the selection information.
In some embodiments, the executing entity may determine the target historical building sub-object diagram according to the selection information in response to receiving the selection information corresponding to the respective historical building sub-object diagrams sent by the user terminal. The selection information may represent a user's selection of a certain historic building sub-object graph. That is, the execution subject may determine the history building sub-object diagram indicated by the selection information as the target history building sub-object diagram.
And 104, rendering the adjusted building object model according to the determined target historical building sub-object diagrams to obtain a rendered adjusted building object model serving as a target building model.
In some embodiments, the execution subject may render the adjusted building object model according to the determined target historical building sub-object diagrams, to obtain a rendered adjusted building object model as the target building model. For example, for each building sub-object in the adjusted building object model, the building sub-object may be color rendered according to the color in the corresponding target historical building sub-object graph.
In an actual application scenario, the execution body may render the adjusted building object model by:
And a first step of rendering each building sub-object in the adjusted building object model according to each target historical building sub-object graph to obtain an initial rendering building object model. For example, for each building sub-object in the adjusted building object model, the building sub-object may be color rendered according to the color in the corresponding target historical building sub-object graph.
And secondly, inputting the initial rendering building object model into a pre-trained building object model detection model to obtain a building object model detection result. The building object model detection model may be a neural network model pre-trained with the rendered building object model as input and the building object model detection result as output. For example, the building object model detection result may represent whether a certain building sub-object in the rendered adjusted building object model is completely rendered (i.e., color rendering is complete). For example, the building object model detection model may be a machine learning model structure using various object detection, such as YOLOv or YOLOv, and the like.
And thirdly, determining the initial rendering building object model as a target building model in response to determining that the building object model detection result represents no abnormality.
Continuing, when the building model construction method of the present application is adopted to construct a building model, the following problems are often associated: the constructed building model was not examined. For these problems, the conventional solutions are: the building model is inspected by a technician.
However, the above solution generally has the following technical problem two: the manual detection efficiency is low, and detection of partial building sub-objects in the building model is easy to miss.
For the second technical problem, the following solution is decided to be adopted.
The building object model detection model can be obtained through training by the following steps:
first, a training sample set of a rendered building object model is obtained. Wherein the rendered building object model training samples in the rendered building object model training sample set comprise sample rendered building object models.
Continuing, when the building model construction method of the present application is used for building model detection, the following problems are often associated with the building model detection: a large number of building object model images need to be acquired to train the detection model. For these problems, the conventional solutions are: each historical building object model is marked manually by a technician, and then model training is performed by using the marked images.
However, the above solution generally has the following technical problem three: the manual marking efficiency is lower, the marking is inaccurate, and detection errors are easy to cause.
Aiming at the third technical problem, the following solution is adopted.
Wherein, the first step can comprise the following substeps:
A first sub-step of obtaining a set of marked rendered building object models and a set of unmarked rendered building object models. The markup rendering building object model tags a model image of an anomaly region in the building object model. Unlabeled rendered building object models may refer to model images of anomaly regions in unlabeled building object models. That is, the markup renders the building object model as a model image with the tags present. The unlabeled rendered building object model may be a model image of an unlabeled label.
A second sub-step of executing the following labeling step based on the labeled-up building object model set and the unlabeled-up building object model set:
1. And extracting the feature vector of the mark rendering building object model of each mark rendering building object model in the mark rendering building object model set to obtain a feature vector set of the mark rendering building object model. And extracting the feature vector of the mark rendering building object model of each mark rendering building object model in the mark rendering building object model set through a feature extraction network to obtain a feature vector set of the mark rendering building object model. The feature extraction network may be a feature extraction model, e.g., a Bert model, a VGG model, etc.
2. And extracting the feature vector of the unlabeled building object model of each unlabeled building object model in the unlabeled building object model set to obtain a feature vector set of the unlabeled building object model. And extracting the feature vector of the unlabeled rendering building object model of each unlabeled rendering building object model in the unlabeled rendering building object model set through a feature extraction network to obtain a feature vector set of the unlabeled rendering building object model.
3. And constructing a feature structure tree of the rendering building object model according to the feature vector set of the marking rendering building object model and the feature vector set of the unmarked rendering building object model. Wherein, the rendering building object model feature structure tree comprises: each labeled rendered building object model node and each unlabeled rendered building object model node.
Wherein constructing a rendered building object model feature structure tree comprises:
First, the set of labeled-up building object model feature vectors and the set of unlabeled-up building object model feature vectors are determined as a set of up building object model feature vectors.
Then, for each of the above-described set of rendered building object model feature vectors, the following processing steps are performed:
first, each of the rendering building object model feature vectors having a similarity equal to or greater than a preset similarity to the rendering building object model feature vector is selected from the rendering building object model feature vector set.
And secondly, determining the selected characteristic vectors of each rendering building object model as a similar rendering building object model characteristic vector group of the characteristic vectors of the rendering building object model.
And finally, constructing a rendering building object model feature structure tree according to the similar rendering building object model feature vector groups of the rendering building object model feature vectors in the rendering building object model feature vector set. The feature vectors of the corresponding marked rendering building object models in the feature structure tree of the rendering building object models are marked rendering building object model nodes, and the feature vectors of the corresponding unmarked rendering building object models are unmarked rendering building object model nodes.
4. And selecting a set of rendering building object model nodes to be marked from the rendering building object model feature structure tree according to each marking rendering building object model node included in the rendering building object model feature structure tree. For each of the respective unlabeled rendered building object model nodes, the execution body may perform the steps of: and determining the distance between the feature vector of the untagged rendering building object model corresponding to the untagged rendering building object model node and each tagged rendering building object model to obtain each feature distance. And secondly, determining the minimum feature distance from the minimum value of each feature distance. Then, the determined minimum feature distances may be arranged in a descending order to obtain a minimum feature distance sequence. Next, each minimum feature distance of the number of preceding targets in the minimum feature distance sequence described above may be determined as each target minimum feature distance. And finally, determining each unlabeled rendering building object model node corresponding to each target minimum feature distance as a rendering building object model node set to be labeled.
5. And marking each unmarked rendering building object model corresponding to the node set of the building object model to be marked to obtain a marked rendering building object model group. The execution body may display each unlabeled rendering building object model corresponding to the node set of the to-be-labeled rendering building object model, so as to receive label information corresponding to the unlabeled rendering building object model, which is input by a technician, and perform label processing on the unlabeled rendering building object model. Here, the markup information may include a tag of the untagged rendered building object model input by the user.
And a third sub-step of merging the mark rendering building object model set with the mark rendering building object model set to obtain a merged mark rendering building object model set as a rendering building object model training sample set.
The related content is used as an invention point of the present disclosure, which solves the technical problem that the manual labeling efficiency is low, the labeling is inaccurate, and the detection error is easy to cause. ". The manual labeling efficiency is low, the labeling is inaccurate, and the factors which easily cause detection errors are often as follows: the manual marking efficiency is lower, the marking is inaccurate, and detection errors are easy to cause. If the above factors are solved, the effects of improving the marking efficiency and reducing the detection error can be achieved. To achieve this effect, first, a set of markup-rendered building object models and a set of non-markup-rendered building object models are acquired. Next, the following labeling steps are performed based on the labeled rendered building object model set and the unlabeled rendered building object model set: extracting feature vectors of the mark rendering building object model of each mark rendering building object model in the mark rendering building object model set to obtain a feature vector set of the mark rendering building object model; extracting feature vectors of the unlabeled building object model of each unlabeled building object model in the unlabeled building object model set to obtain a feature vector set of the unlabeled building object model; constructing a feature structure tree of the rendering building object model according to the feature vector set of the marking rendering building object model and the feature vector set of the unmarked rendering building object model, wherein the feature structure tree of the rendering building object model comprises: each marked rendering building object model node and each unmarked rendering building object model node; selecting a to-be-marked rendering building object model node set from the rendering building object model feature structure tree according to each marking rendering building object model node included in the rendering building object model feature structure tree; and marking each unmarked rendering building object model corresponding to the node set of the building object model to be marked to obtain a marked rendering building object model group. And combining the marked rendering building object model set with the marked rendering building object model set to obtain a combined marked rendering building object model set, wherein the combined marked rendering building object model set is used as a rendering building object model training sample set, and therefore, unmarked building object models can be marked by using a small number of marked images. Therefore, the marking efficiency is improved, and the marking time is shortened. Also, because the automatic marking is performed through the building object model characteristics, the marking accuracy is greatly improved, and the detection error of the subsequent model is reduced.
And secondly, training the initial building object model detection model according to the rendered building object model training sample set to obtain a trained building object model detection model.
Wherein, the second step can comprise the following substeps:
a first sub-step of selecting a target rendered building object model training sample from the set of rendered building object model training samples. One rendered building object model training sample may be randomly selected from the set of rendered building object model training samples as a target rendered building object model training sample.
And a second sub-step of inputting a sample rendering building object model included in the target rendering building object model training sample into a feature extraction network included in the initial building object model detection model so as to perform layered feature extraction processing on the sample rendering building object model to obtain features of each layer of sample rendering building object model. As an example, the feature extraction network may be a backbone network that incorporates an attention mechanism for improvement based on the backbone feature extraction network used by the object detection algorithm. For example, on the basis of a backbone network (CSPDARKNET), the SE-CSPDARKNET backbone network with improved attention mechanisms of SE (Squeeze and Excitation) is added. The feature extraction network may comprise a plurality of convolution modules, a plurality of attention mechanism convolution modules, a pooling module. The method can be used for extracting the image characteristics of the appearance effect sample of each layer of building. For example, the backbone feature extraction network includes 6 convolution modules, 4 CSP-SE convolution modules (i.e., attention mechanism convolution modules), and 1 SPPF module (i.e., pooling module). It should be noted that specific parameters (such as input feature diagram size, output feature diagram size, network parameters, etc.) and structures of each module may be set according to actual situations. The attention mechanism convolution module may include: the first convolution sub-module is used for carrying out two-dimensional convolution operation on the feature map of the input attention mechanism convolution module to obtain a first convolution feature map (building object model feature map); the splitting module is used for splitting the first convolution feature diagram according to the second channel to obtain a split feature diagram; the residual convolution submodules are sequentially connected and are used for carrying out convolution and residual connection processing on the input feature images to obtain residual convolution feature images; the residual connection submodule is used for superposing the split characteristic diagram and a plurality of residual convolution characteristic diagrams according to the channels to obtain a residual characteristic diagram; the second convolution sub-module is used for carrying out two-dimensional convolution operation on the residual feature map to obtain a second convolution feature map; and the attention machine submodule is used for processing the second convolution feature diagram to obtain an output feature diagram of the attention mechanism convolution module. For example, the residual convolution sub-module may include two convolution modules (convolution kernel 3×3, step size 1, padding 1), and a residual connection module. The residual error connection module can add the input characteristic diagram and the characteristic diagram output by the second convolution module according to the channels, so as to obtain the output characteristic diagram of the residual error convolution sub-module. The attention machine submodule may include a global averaging pooling operation, two-dimensional convolution operations (the convolution kernel is 1×1, the step size is 1, and the padding is 0), and two activation functions (such as Relu and Sigmod), and further may multiply the input feature map with the corresponding feature map weight according to the feature channel, so as to obtain an output feature map of the attention machine submodule. The SPPF module may include a two-dimensional convolution operation (Conv 2d, 1x1 convolution kernel, 1 step size, 0 padding), 3 maximum pooling operations (Max Pooling, 5 pooling kernel), a residual connection module, a channel addition module, and another two-dimensional convolution operation (Conv 2d, 1x1 convolution kernel, 1 step size, 0 padding).
As an example, the backbone feature extraction network in this embodiment may include ten feature extraction stages, that is, ten feature extraction modules connected in sequence, the first stage: convolution module-input feature map size 320x320x64; and a second stage: CSP-SE convolution module-input feature map size 160x160x128; and a third stage: convolution module-input feature map size 160x160x128; fourth stage: CSP-SE convolution module-input feature map size 80x80x256; fifth stage: convolution module-input feature map size 80x80x256; sixth stage: CSP-SE convolution module-input feature map size 40x40x512; seventh stage: convolution module-input feature map size 40x40x512; eighth stage: CSP-SE convolution module-input feature map size 20x20x512; ninth stage: SPPF pooling module-input feature map size 20x20x512.
And a third sub-step of inputting the characteristics of the sample rendering building object model of each layer into a characteristic fusion network included in the initial building object model detection model to obtain the fusion characteristics of the sample rendering building object model. The feature fusion network can be set according to actual requirements, for example, an up-sampling feature fusion structure from bottom to top can be adopted, or a down-sampling feature fusion structure from top to bottom can also be adopted. Here, the upsampling feature fusion structure may first upsample a feature map (building object model feature map) output at the ninth stage by a specified multiple to match the feature map output at the sixth stage and perform channel superimposition. And then, the superimposed feature images can be processed by an attention mechanism convolution module and output to obtain a first building object model fusion feature image. And then, up-sampling the fusion feature map of the first building object model by a designated multiple so as to match the feature map output in the fourth stage and perform channel superposition. And finally, processing the superimposed feature images by a attention mechanism convolution module, and outputting to obtain a second building object model fusion feature image.
And a fourth sub-step of inputting the fusion characteristics of the rendering building object models of the samples into a positioning detection network included in the initial building object model detection model to obtain a positioning detection result. The location detection network may include a classification structure and a location structure. It will be appreciated that the specific structure of the location detection network may be set according to actual requirements. As an example, the classification structure in the classification positioning network may comprise a convolution module (convolution kernel 3, step size 1, padding 0) and a two-dimensional convolution operation (Conv 2d, convolution kernel 1x 1, step size 1, padding 0). And the positioning structure may comprise a convolution module (convolution kernel 3, step 1, padding 0) and a two-dimensional convolution operation (Conv 2d, convolution kernel 1x 1, step 1, padding 0). The execution body can input the fusion characteristics of the various sample rendering building object models into a positioning detection network in the initial building object model detection model, so that classification data and positioning data of the fusion characteristics of the various sample rendering building object models are obtained. And then, carrying out dimension superposition on the classification data of the fusion characteristics of the sample rendering building object models to obtain the classification prediction total data of the sample rendering building object models. The classification prediction dimension may include the number of predicted detection frames, and a predicted value of each detection frame belonging to each labeling category. In addition, the positioning data of fusion characteristics of the sample rendering building object models can be subjected to dimension superposition, so that the positioning prediction total data of the sample rendering building object models are obtained. Wherein the localization prediction dimension may include a predicted number of detection frames and a predicted position of each detection frame in the sample rendered building object model.
And a fifth sub-step of generating an output result of the initial building object model detection model based on the positioning detection result. For the positioning detection results of fusion features of the building object model of each sample rendering building object model of the same sample, the execution subject can calculate an average value to serve as an output result of the model.
And a sixth sub-step of determining a loss value between the sample label corresponding to the training sample of the target rendering building object model and the output result. In practice, the execution subject may determine a loss value between the sample label corresponding to the training sample of the target rendered building object model and the output result through a hinge loss function or a cross entropy loss function.
And a seventh sub-step of determining the initial building object model detection model as a trained building object model detection model in response to determining that the loss value is equal to or less than a preset loss value.
The above related matters are taken as an invention point of the disclosure, and solve the second technical problem that the manual detection efficiency is low, and detection of partial building sub-objects in the building model is easy to miss. ". Detection of a part of the building sub-objects in the building model is easily missed. The factors of (a) are often as follows: the manual detection efficiency is low, and detection of partial building sub-objects in the building model is easy to miss. If the above factors are solved, the effects of improving the detection efficiency and avoiding missing detection can be achieved. To achieve this, first, a target rendered building object model training sample is selected from the set of rendered building object model training samples. Secondly, inputting a sample rendering building object model included in the target rendering building object model training sample into a feature extraction network included in an initial building object model detection model, so as to perform layered feature extraction processing on the sample rendering building object model, and obtaining features of each layer of sample rendering building object model. And then, inputting the characteristics of the sample rendering building object model of each layer into a characteristic fusion network included in the initial building object model detection model to obtain the fusion characteristics of each sample rendering building object model. And then, inputting the fusion characteristics of the rendering building object models of the samples into a positioning detection network included in the initial building object model detection model to obtain a positioning detection result. Then, based on the positioning detection result, generating an output result of the initial building object model detection model; and determining a loss value between a sample label corresponding to the target rendering building object model training sample and the output result. And finally, determining the initial building object model detection model as a building object model detection model after training in response to determining that the loss value is smaller than or equal to a preset loss value. Thus, the collected images of the respective historic building object models can be utilized as training samples. And training the constructed building object model detection model. The building object model detection model comprises a feature extraction network, a feature fusion network and a positioning detection network. And further, the building object model detection model can be utilized to realize automatic detection of the building object model. Thus, the problem of omission caused by manual detection is solved, and the detection efficiency is improved.
Optionally, the target building model is sent to the user terminal for display.
In some embodiments, the executing entity may send the target building model to the user terminal for presentation.
Optionally, exporting the target building model into a building model file in a target file format, and storing the building model file in a target database.
In some embodiments, the executing entity may export the target building model into a building model file in a target file format, and store the building model file in a target database. The target file format may be a preset file format. For example, the target file format may be a GIF format, an OBJ format, or an STL format.
With further reference to fig. 2, as an implementation of the method shown in the above figures, the present disclosure provides embodiments of a building model construction apparatus corresponding to those shown in fig. 1, which may be applied in particular to various electronic devices.
As shown in fig. 2, the building model constructing apparatus 200 of some embodiments includes: a construction unit 201, an adjustment unit 202, a determination unit 203, and a rendering unit 204. Wherein the construction unit 201 is configured to construct a building object model based on building design information of the target building object; an adjusting unit 202 configured to adjust the building object model according to the building adjustment requirement information in response to receiving the building adjustment requirement information sent by the user terminal, so as to obtain an adjusted building object model, where the adjusted building object model includes a building sub-object group, and each building sub-object corresponds to one building sub-object parameter information; a determining unit 203 configured to perform, for each building sub-object parameter information, the following processing steps: determining the type of the building sub-object corresponding to the building sub-object parameter information; acquiring a historical building sub-object diagram set corresponding to the building sub-object type, wherein each historical building sub-object diagram corresponds to historical building sub-object parameter information; determining the parameter similarity of the building sub-object parameter information and each historical building sub-object parameter information to obtain a parameter similarity set; determining the parameter similarity meeting the preset condition in the parameter similarity set as an alternative parameter similarity to obtain an alternative parameter similarity group; transmitting each historical building sub-object diagram corresponding to the alternative parameter similarity group to the user terminal; responding to receiving the selection information corresponding to each history building sub-object diagram sent by the user terminal, and determining a target history building sub-object diagram according to the selection information; and a rendering unit 204 configured to render the adjusted building object model according to the determined target historic building sub-object diagrams, to obtain a rendered adjusted building object model as a target building model.
It will be appreciated that the elements described in the construction model construction device 200 correspond to the respective steps in the method described with reference to fig. 1. Thus, the operations, features and resulting benefits described above for the method are equally applicable to the building model construction device 200 and the units contained therein, and are not described in detail herein.
Referring now to FIG. 3, a schematic diagram of an electronic device (e.g., computing device) 300 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), car terminals (e.g., car navigation terminals), and the like, as well as stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 3 is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of the present disclosure.
As shown in fig. 3, the electronic device 300 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 301 that may perform various suitable actions and processes in accordance with a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage means 308 into a Random Access Memory (RAM) 303. In the RAM303, various programs and data required for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM302, and the RAM303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
In general, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 308 including, for example, magnetic tape, hard disk, etc.; and communication means 309. The communication means 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. While fig. 3 shows an electronic device 300 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 3 may represent one device or a plurality of devices as needed.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 309, or from storage device 308, or from ROM 302. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing means 301.
It should be noted that, the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: building a building object model based on building design information of the target building object; responding to the received building adjustment demand information sent by the user terminal, and adjusting the building object model according to the building adjustment demand information to obtain an adjusted building object model, wherein the adjusted building object model comprises a building sub-object group, and each building sub-object corresponds to one building sub-object parameter information; for each building sub-object parameter information, the following processing steps are performed: determining the type of the building sub-object corresponding to the building sub-object parameter information; acquiring a historical building sub-object diagram set corresponding to the building sub-object type, wherein each historical building sub-object diagram corresponds to historical building sub-object parameter information; determining the parameter similarity of the building sub-object parameter information and each historical building sub-object parameter information to obtain a parameter similarity set; determining the parameter similarity meeting the preset condition in the parameter similarity set as an alternative parameter similarity to obtain an alternative parameter similarity group; transmitting each historical building sub-object diagram corresponding to the alternative parameter similarity group to the user terminal; responding to receiving the selection information corresponding to each history building sub-object diagram sent by the user terminal, and determining a target history building sub-object diagram according to the selection information; and rendering the adjusted building object model according to the determined target historical building sub-object diagrams to obtain a rendered adjusted building object model serving as a target building model.
Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example, described as: a processor comprising: the device comprises a construction unit, an adjustment unit, a determination unit and a rendering unit. The names of these units do not constitute limitations on the unit itself in some cases, and for example, a construction unit may also be described as "a unit that constructs a building object model based on building design information of a target building object".
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.
Claims (8)
1. A building model construction method comprising:
building a building object model based on building design information of the target building object;
responding to received building adjustment demand information sent by a user terminal, and adjusting the building object model according to the building adjustment demand information to obtain an adjusted building object model, wherein the adjusted building object model comprises a building sub-object group, and each building sub-object corresponds to one building sub-object parameter information;
For each building sub-object parameter information, the following processing steps are performed:
Determining the type of the building sub-object corresponding to the building sub-object parameter information;
acquiring a historical building sub-object diagram set corresponding to the building sub-object type, wherein each historical building sub-object diagram corresponds to historical building sub-object parameter information;
determining the parameter similarity of the building sub-object parameter information and each historical building sub-object parameter information to obtain a parameter similarity set;
Determining the parameter similarity which meets the preset condition in the parameter similarity set as an alternative parameter similarity, and obtaining an alternative parameter similarity group;
sending each historical building sub-object diagram corresponding to the alternative parameter similarity group to the user terminal;
Responding to receiving selection information corresponding to each historical building sub-object diagram sent by the user terminal, and determining a target historical building sub-object diagram according to the selection information;
And rendering the adjusted building object model according to the determined target historical building sub-object diagrams to obtain a rendered adjusted building object model serving as a target building model.
2. The method of claim 1, wherein said rendering the adjusted building object model according to the determined respective target historical building sub-object diagrams, resulting in a rendered adjusted building object model, comprises:
rendering each building sub-object in the adjusted building object model according to each target historical building sub-object graph to obtain an initial rendering building object model;
inputting the initial rendering building object model into a pre-trained building object model detection model to obtain a building object model detection result;
in response to determining that the building object model detection result characterizes no anomaly, the initial rendered building object model is determined to be a target building model.
3. The method of claim 2, wherein prior to said inputting the initial rendered building object model into a pre-trained building object model detection model, the method further comprises:
Obtaining a rendered building object model training sample set, wherein the rendered building object model training sample in the rendered building object model training sample set comprises a sample rendered building object model;
and training the initial building object model detection model according to the rendered building object model training sample set to obtain a trained building object model detection model.
4. The method of claim 1, wherein the method further comprises:
and sending the target building model to the user terminal for display.
5. The method of claim 1, wherein the method further comprises:
and exporting the target building model into a building model file in a target file format, and storing the building model file into a target database.
6. A building model construction apparatus comprising:
a construction unit configured to construct a building object model based on building design information of the target building object;
The adjusting unit is configured to respond to receiving building adjustment demand information sent by the user terminal, adjust the building object model according to the building adjustment demand information to obtain an adjusted building object model, wherein the adjusted building object model comprises a building sub-object group, and each building sub-object corresponds to one building sub-object parameter information;
A determining unit configured to perform, for each building sub-object parameter information, the following processing steps: determining the type of the building sub-object corresponding to the building sub-object parameter information; acquiring a historical building sub-object diagram set corresponding to the building sub-object type, wherein each historical building sub-object diagram corresponds to historical building sub-object parameter information; determining the parameter similarity of the building sub-object parameter information and each historical building sub-object parameter information to obtain a parameter similarity set; determining the parameter similarity which meets the preset condition in the parameter similarity set as an alternative parameter similarity, and obtaining an alternative parameter similarity group; sending each historical building sub-object diagram corresponding to the alternative parameter similarity group to the user terminal; responding to receiving selection information corresponding to each historical building sub-object diagram sent by the user terminal, and determining a target historical building sub-object diagram according to the selection information;
And the rendering unit is configured to render the adjustment building object model according to the determined target historical building sub-object diagrams to obtain a rendered adjustment building object model serving as a target building model.
7. An electronic device, comprising:
One or more processors;
a storage device having one or more programs stored thereon;
When executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-5.
8. A computer readable medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of any of claims 1-5.
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