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WO2021179593A1 - Procédé de reconstruction de pipeline tridimensionnel basé sur un apprentissage profond, système, support et appareil - Google Patents

Procédé de reconstruction de pipeline tridimensionnel basé sur un apprentissage profond, système, support et appareil Download PDF

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Publication number
WO2021179593A1
WO2021179593A1 PCT/CN2020/120385 CN2020120385W WO2021179593A1 WO 2021179593 A1 WO2021179593 A1 WO 2021179593A1 CN 2020120385 W CN2020120385 W CN 2020120385W WO 2021179593 A1 WO2021179593 A1 WO 2021179593A1
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component
point
pipeline
instances
instance
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PCT/CN2020/120385
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English (en)
Chinese (zh)
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屠长河
程莉莉
魏卓
孙铭超
辛士庆
安德劳
李扬彦
陈宝权
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山东大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

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  • the present disclosure relates to the technical field of pipeline three-dimensional reconstruction, and in particular to a pipeline three-dimensional reconstruction method, system, medium and equipment based on deep learning.
  • High-quality 3D models of power plants, petrochemical plants, and other factories are critical in many applications, including disaster simulation, monitoring, and execution training.
  • Industrial bases are built according to specific plans, usually combined with 3D CAD models.
  • building a complete and accurate three-dimensional model is a difficult task.
  • these models may not exist in older facilities or may not reflect the current appearance of the site.
  • laser scanners can capture three-dimensional surfaces and geometric figures with high precision, generating dense point cloud samples.
  • capturing surface geometry is particularly challenging.
  • the inventors of the present disclosure found that due to the importance and universality of the function of the pipeline, it is the main structure of many industrial sites. They are thin structures defined by long cylinders organized in dense and complex structures. Although pipes are only cylindrical in basic shape, which can be easily defined as their axis and radius, they often contain additional components such as flanges, valves, air inlets, elbows, tees, etc. Therefore, the small surface of the pipeline, the serious self-occlusion caused by the complex structure, partial missing, insufficient sampling and other problems can easily cause errors in the three-dimensional scanning and reconstruction of the pipeline.
  • a common method of point cloud 3D pipeline reconstruction is based on geometric processing and fitting.
  • the key to this type of method is to find the position of the radius and axis of the cylinder.
  • the commonly used method is to use RanSac, Hough transform and other methods to perform fitting to detect the cylinder, mainly to restore the cylindrical pipe in the industrial plant.
  • the present disclosure provides a pipeline 3D reconstruction method, system, medium and equipment based on deep learning, which reduces the complexity of common pipeline reconstruction problems to a combination of component detection and model fitting problems.
  • the accurate three-dimensional reconstruction of the pipeline is realized.
  • the first aspect of the present disclosure provides a pipeline 3D reconstruction method based on deep learning.
  • a pipeline 3D reconstruction method based on deep learning including the following steps:
  • Obtain the point cloud data of the pipeline use the deep learning method to learn the characteristics of the point cloud, at least get the category of the component to which the point belongs, the radius of the component to which the point belongs, and the direction vector of the point;
  • the second aspect of the present disclosure provides a pipeline 3D reconstruction system based on deep learning, including:
  • the point cloud learning module is configured to: obtain the point cloud data of the pipeline, use the deep learning method to learn the characteristics of the point cloud, and at least obtain the category of the component to which the point belongs, the radius of the component to which the point belongs, and the direction vector of the point;
  • the candidate instance acquisition module is configured to calculate the axis point by using the radius of the component to which the point belongs and the direction vector of the point, and combine the category label of the component to which the point belongs to cluster the axis point to obtain the candidate instance;
  • the graph structure component module is configured to: use graph-based methods to obtain the connection relationship between different candidate instances, and use components as nodes to form the structure of the graph;
  • the pipeline reconstruction module is configured to replace the nodes in the figure with the actual three-dimensional component model to complete the entire pipeline reconstruction.
  • the third aspect of the present disclosure provides a medium on which a program is stored, and when the program is executed by a processor, the steps in the pipeline 3D reconstruction method based on deep learning as described in the first aspect of the present disclosure are realized.
  • the fourth aspect of the present disclosure provides an electronic device, including a memory, a processor, and a program stored in the memory and capable of running on the processor, and the processor executes the program as described in the first aspect of the present disclosure.
  • the steps in the deep learning-based pipeline 3D reconstruction method are to reconstruct the pipeline 3D model.
  • the method, system, medium, and electronic equipment described in the present disclosure reduce the complexity of common pipeline reconstruction problems to a combination of component detection and model fitting problems, and are highly robust and realize accurate three-dimensional reconstruction of pipelines.
  • the methods, systems, media, and electronic devices described in the present disclosure use a combination of clustering and graphs to filter detection results and generate a class diagram global pipeline model, which effectively prevents the generation of training sets and the design of training networks Wait for the error caused by a priori detection.
  • the methods, systems, media, and electronic devices described in the present disclosure embed the initial unreliable local prior detection into a processing framework that considers global attributes and semantic structure, so as to better understand the industrial structure.
  • the cloud reconstructs the complete pipeline structure to achieve more accurate pipeline 3D reconstruction.
  • the radius and direction vector adopt a weight-sharing framework for regression calculation, which can have better accuracy and convergence.
  • Fig. 1 is a schematic diagram of an existing pipeline structure provided in the background art.
  • FIG. 2 is a schematic flowchart of a pipeline 3D reconstruction method based on deep learning provided in Embodiment 1 of the disclosure.
  • FIG. 3 is a schematic diagram of the radius and point direction vector of the component provided in Embodiment 1 of the disclosure.
  • FIG. 4 is a schematic diagram of the structure of the network framework provided by Embodiment 1 of the disclosure.
  • FIG. 5 is a schematic diagram of the result of the noisy point cloud and the predicted component category provided by Embodiment 1 of the disclosure.
  • FIG. 6 is a schematic diagram of the original point cloud provided by Embodiment 1 of the disclosure and the corresponding axis points calculated through prediction features.
  • FIG. 7 is a schematic diagram of skeletons of different types of components provided in Embodiment 1 of the disclosure.
  • FIG. 8 is a schematic diagram of reconstruction of the synthetic scene provided in Embodiment 1 of the disclosure.
  • FIG. 9 is a schematic diagram of comparison of reconstruction results provided by Embodiment 1 of the present disclosure and other methods.
  • FIG. 10 is a schematic diagram of reconstruction results under point clouds with different missing degrees provided in Embodiment 1 of the disclosure.
  • FIG. 11 is a schematic diagram of the reconstruction result under the real point cloud data provided in Embodiment 1 of the disclosure.
  • the pipeline scene is assembled by pipeline components and pipeline supports.
  • pipe components are mainly considered, and supporting parts such as floors and fences are ignored.
  • a priori-based learning method is adopted, and a deep learning network is trained to learn candidate features of 3D point clouds.
  • the prior detection of generating training set and designing training network usually has errors, so a combination of clustering and graph technology is used to filter the detection results and generate a class diagram global pipeline model. Embed the initial unreliable local prior detection into a processing framework that considers global attributes and semantic structure.
  • Embodiment 1 of the present disclosure provides a pipeline 3D reconstruction method based on deep learning. Given a scanned point cloud of a pipeline, the method completes the reconstruction in four steps:
  • the category and radius of the component can uniquely determine the shape of the component. Since pipeline reconstruction pays more attention to the geometric characteristics of the pipeline, the shape information of the component is obtained by detecting the category and radius of the component. According to the pipeline design standard, the radius of the component is a predetermined discrete value. As mentioned above, six types of components are selected as basic parts: pipes, flanges, reducers, elbows, tees and crosses, and an additional type label is added to distinguish non-component points.
  • the characteristics of each point are predicted by learning methods: the type c of the component to which the point belongs, the radius r of the component to which the point belongs, and the direction vector o of the point. Since the type and radius of the component to which a point belongs are discrete, classification is used to predict, and the discrete category prediction has better accuracy than the regression of continuous values; the direction vector of the point is continuous, so regression is used for prediction. Using the direction vector and radius of the point, the position of the axis point corresponding to the scanning point can be calculated.
  • This embodiment implements a pipeline generator to simulate a similar real scene model, and trains the network on the synthesized pipeline model.
  • a synthetic pipeline is generated by assembling components.
  • a random skeleton diagram is first generated in the set scene range. For each diagram node, the type, radius and orientation of the components are randomly assembled to obtain the entire pipeline scene. After that, the virtual scanning library is used to sample the surface of the pipeline to simulate the scanning point cloud. Combining the labels required for network learning, for each scan point, get the type of component it belongs to, the radius of the component and the direction vector of the point, and generate the groundtruth about the scan point.
  • the type label of the component is 0, 1, 2, 3, 4, 5, 6, 0-straight pipe, 1-flange, 2-elbow, 3-tee, 4-four-way, 5-reducing pipe , 6-noise point;
  • the radius range of the component is 0.365-4.6, and there are 23 different sizes in total, that is, there are 23 radius labels, respectively 0.365,...,4.6.
  • this embodiment uses network learning to predict the category c of the component to which the point belongs, the radius r of the component to which the point belongs, and the direction vector o of the point.
  • the input of the network is the scanned point cloud, each point contains (x, y, z) coordinate information, and the output is the above three tags.
  • r represents the radius of the component to which the point belongs
  • o represents the direction vector of the point.
  • the specific network framework is shown in Figure 4.
  • the input of the network is a point cloud P containing location information.
  • the network contains four layers of convolution, four layers of deconvolution, and two layers of MLP to obtain 7 channel feature maps. And followed by a softmax activation layer. Then use the classifier to filter out noise, that is, points that do not belong to the six types of components.
  • a multi-task network is defined to handle classification and regression.
  • the network in the upper right corner is used to predict the radius of the component to which each point belongs, and the network in the lower right corner is used to predict the orientation.
  • the range of the radius is 0.365-4.6 meters, with a total of 23 sizes. Therefore, the branch classified in the upper right corner is subjected to four-layer convolution, four-layer deconvolution and two-layer MLP output 23 channel feature vectors, followed by a softmax layer, and the regression branch is four-layer convolution, four-layer deconvolution and two-layer MLP
  • the feature vectors of the 3 channels are output, that is, the corresponding direction vectors, in which the convolutional layer and the deconvolutional layer of the two branches are shared.
  • multi-task training is performed to train the entire network at the same time.
  • the cross-entropy loss function is used; in the regression task, the L2loss is used, because the radius and direction vector are related to the offset vector from the scanning point to the component axis.
  • the test result is shown in Figure 5.
  • the left side is the input point cloud with noise, and the right side is the result of the predicted component category.
  • different color depths represent different types of components.
  • the component category of each point, the radius of the component and the direction vector of the point are obtained according to the network prediction, that is, the specific expression of the point is:
  • L ⁇ (p 1, c 1, r 1, o 1), (p 2, c 2, r 2, o 2), ... (p n, c n, r n, o n) ⁇ ;
  • L 2 ⁇ (a 1 ,c 1 ,r 1 ),(a 2 ,c 2 ,r 2 ),...(a n ,c n ,r n ) ⁇ ;
  • the left image is the original point cloud
  • the right image is the corresponding axis point calculated by the prediction feature.
  • points of different color depths represent different component types.
  • the corresponding axis points and candidate component instance sets are obtained through the second step of processing.
  • This step mainly obtains the connection relationship between different components.
  • the overall skeleton of the pipeline is obtained, and then the connection relationship between the components is initially obtained. Due to a certain error in network prediction, this embodiment uses rules to optimize the graph structure to obtain a reasonable connection relationship between instances. Finally, the overall skeleton is obtained according to the connection relationship between the examples.
  • the axis points of the template skeleton and the instance are matched using the ICP algorithm to obtain the candidate skeleton of the instance.
  • the skeleton of the template is represented by endpoints and lines.
  • L 2 ⁇ (a 1 ,c 1 ,r 1 ),(a 2 ,c 2 ,r 2 ),...(a n ,c n ,r n ) ⁇ ;
  • the overall framework is obtained by iteratively extracting the longest path of the minimum spanning tree. And use this connection relationship to guide the connection relationship between the instances.
  • connection relationship between the axis points obtained above and the instance information obtained by clustering are used to obtain the connection relationship between the instances;
  • the pipeline frame represented by D is composed of multiple acyclic paths d, and d is a path composed of axis points. Traverse each path d and combine the clustering results to obtain the connection relationship between the instances on the path d.
  • This step combines the template information of each component instance obtained in step (3-1), and uses these connection rules to optimize the graph architecture to obtain accurate connection relationships between instances.
  • the rules are as follows:
  • Tee There are three neighbors, and the three neighboring endpoints adjacent to this component need to form a three-way layout;
  • the detection updates the connection relationship between the instances according to the mark. If the direction vectors of the adjacent end points are in a vertical state, an elbow instance is added between the two instances.
  • connection relationship between the instances is obtained above, and the template skeleton corresponding to the instance point cloud is known.
  • connection relationship is used to optimize the position of the template skeleton again; now the final instance and the connection relationship between the instances can be obtained, taking the instance as Nodes represent the connection relationship of instances in the form of graphs, and there are edges between adjacent instances.
  • the technology described in this embodiment reconstructs the local structure that follows the connection rules and the semantic relationship in the pipeline.
  • the results show that the method can better reconstruct the complete pipeline structure from the industrial structure point cloud.
  • Each axis point has a predicted radius. According to the radius of these points, the radius with the most votes is taken as the radius of the component instance; the type, radius, and position of the template corresponding to the instance are known. Now use the template instead of the figure.
  • the nodes complete the reconstruction of the entire pipeline.
  • Figure 8 shows the reconstruction of the composite scene, from left to right are the input point cloud, the axis points marked with the color of the component category, the skeleton of the pipeline, and the reconstructed pipeline.
  • the reconstruction result of the method (Cylinder detection in large-scale point cloud of pipeline plant) (simplifying the 3D factory reconstruction problem to the detection problem of the 2D circle after the pipeline is projected to the plane), the reconstruction result and groundtruth obtained by the EdgeWise software (Correct reconstruction result).
  • the comparison includes four scenes of different scales and complexity, from simple small scenes to complex large scenes.
  • the methods of Huang et al. and Liu et al. lose components, and the method of Liu et al. cannot reconstruct some joints such as elbows, but can only reconstruct straight pipes.
  • the commercial software EdgeWise requires a lot of manual interaction to complete the reconstruction. The longer the interaction time, the better the results can be obtained, but it is a very time-consuming task.
  • Fig. 11 is the reconstruction result under real point cloud data, from left to right are the input point cloud, the reconstruction result of the method in this embodiment, the reconstruction result of Liu et al., and the reconstruction result of the EdgeWise software.
  • Embodiment 2 of the present disclosure provides a pipeline 3D reconstruction system based on deep learning, including:
  • the point cloud learning module is configured to learn the characteristics of the point cloud by using a deep learning method to at least obtain the category of the component to which the point belongs, the radius of the component to which the point belongs, and the direction vector of the point;
  • the candidate instance acquisition module is configured to calculate the axis point by using the radius of the component to which the point belongs and the direction vector of the point, and combine the category label of the component to which the point belongs to cluster the axis point to obtain the candidate instance;
  • the graph structure component module is configured to: use graph-based methods to obtain the connection relationship between different component instances, and use the components as nodes to form the structure of the graph;
  • the pipeline reconstruction module is configured to replace the nodes in the figure with the actual three-dimensional component model to complete the entire pipeline reconstruction.
  • the specific working method of the three-dimensional reconstruction system is the same as the three-dimensional reconstruction method described in Embodiment 1, and will not be repeated here.
  • Embodiment 3 of the present disclosure provides a medium on which a program is stored, and when the program is executed by a processor, the steps in the method for 3D reconstruction of a pipeline based on deep learning as described in Embodiment 1 of the present disclosure are realized.
  • Embodiment 4 of the present disclosure provides an electronic device, including a memory, a processor, and a program stored in the memory and capable of running on the processor.
  • the processor executes the program as described in Embodiment 1 of the present disclosure.
  • the steps in the deep learning-based pipeline 3D reconstruction method are to reconstruct the pipeline 3D model.

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Abstract

La présente invention concerne un procédé de reconstruction de pipeline tridimensionnel basé sur un apprentissage profond, un système, un support et un appareil, se rapportant au domaine technique de la reconstruction de pipeline tridimensionnel. Le procédé comprend les étapes consistant à : apprendre des caractéristiques d'un nuage de points au moyen d'un procédé d'apprentissage profond et au moins obtenir une catégorie d'un composant associé à des points, un rayon du composant associé aux points et un vecteur de direction des points ; calculer des points d'axe au moyen du rayon du composant associé aux points et du vecteur de direction des points et réaliser, en incorporant une étiquette de catégorie du composant associé aux points, le regroupement sur les points d'axe pour obtenir des instances candidates ; obtenir, au moyen d'un procédé basé sur un graphique, une relation de connexion entre les différentes instances candidates et constituer, au moyen du composant en tant que nœud, une structure d'un graphique ; et remplacer le nœud dans le graphique par un modèle de composant tridimensionnel réel pour achever la reconstruction d'un pipeline entier. La présente invention aborde le problème associé à la faible précision de la reconstruction de pipeline tridimensionnel existante et réduit les problèmes de complexité de la reconstruction de pipeline générale à celle d'une combinaison de problèmes de détection de composant et d'ajustement de modèle, ce qui permet d'obtenir une reconstruction de pipeline tridimensionnel précise.
PCT/CN2020/120385 2020-03-10 2020-10-12 Procédé de reconstruction de pipeline tridimensionnel basé sur un apprentissage profond, système, support et appareil WO2021179593A1 (fr)

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WO2024077084A1 (fr) * 2022-10-04 2024-04-11 Nutech Ventures Réseau de caméras de profondeur à double fonction pour reconstruction 3d en ligne de pipelines complexes
CN116258835A (zh) * 2023-05-04 2023-06-13 武汉大学 基于深度学习的点云数据三维重建方法和系统
CN117408007A (zh) * 2023-12-15 2024-01-16 华东交通大学 三维管网初始流量分配方法、系统、存储介质及电子设备

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