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In this paper we present an efficient GPU-based framework to dynamically perform the voxelization of polygonal models for training 3D convolutional neural networks. It is designed to manage the dataset augmentation by using efficient geometric transformations and random vertex displacements in GPU. With the proposed system, every voxelization is ca...
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... faces of the tetrahedron T i follow a counterclockwise ordering, VOLUME 4, 2016 −1 when they follow a clockwise ordering, and 0 when the tetrahedron is degenerated. This way, a point P inside G is covered by an odd number of tetrahedra from S. By using the formulation of equation 1, the voxelization algorithm for a given polyhedra is as follows (Fig. ...
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Machine learning has considerably improved medical image analysis in the past years. Although data-driven approaches are intrinsically adaptive and thus, generic, they often do not perform the same way on data from different imaging modalities. In particular computed tomography (CT) data poses many challenges to medical image segmentation based on...
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... The training of the CNN often needs a lot of image data [30][31][32]. Even with an indoor accelerated corrosion test, a large amount of data for training cannot be obtained in a short time. ...
For the maintenance of weathering steel structure facilities, it is necessary to evaluate the corrosion grade of the rust layer on the surface regularly. At present, the corrosion grade classification of weathering steel is mainly based on the human-eye inspection. In this paper, a deep learning method using a convolutional neural network for evaluating the corrosion grade of weathering steel is proposed to save time and manpower. Firstly, the image dataset of the corrosion steel plate was established using salt spray tests. Then, a CNN architecture named VGG-Corrosion was designed to evaluate the corrosion grade of the corroded steel plate. The effect of the learning rate, transfer learning, and batch size was also investigated to clarify the best hyperparameter configurations to train a powerful corrosion grade classification model. Under the best combination of considered hyperparameters, the mean average accuracy for the corrosion grade evaluation of the test results is 90.96%. The testing results indicated that the CNN based corrosion grade recognition for weath-ering steel plate is prospective, which would be helpful for safety evaluation of steel structures.
... Users can observe the spatial relationship of objects from a full perspective, which can give users a real-world experience and improve the level of scientific management of urban underground pipelines. Compared with the two-dimensional flat map representation, the three-dimensional visualization expression has the characteristics and advantages of strong expressiveness, realistic effects, and clear spatial relationships [4]. e change from two-dimensional to three-dimensional means a change in the way of expressing spatial objects and a deepening of spatial cognition. ...
In the processing of panoramic video, projection mapping is a very critical step. The selection of the projection mapping format will affect the performance, transmission mode, and rendering mode of the panoramic video codec. Therefore, this article starts from the projection mapping format, analyzes the mapping process of the standard mapping format, and then proposes a method of rendering panoramic video in the projection mapping format. By analyzing the parallel design schemes of swarm intelligence algorithms under different granularities, this paper proposes a parallel swarm intelligence optimization algorithm design method and then designs and implements a parallel artificial bee colony algorithm. With the help of the ArcGIS Engine development platform, this paper defines the interface for data exchange. With the support of Multipatch format data in ArcGIS software, through secondary development, the three-dimensional pipeline automatic modeling module is established, and the pipeline model is automatically generated. The digital construction and visualization of the company play a driving role. Based on the understanding of the characteristics of the pipeline image itself, combined with the analysis of the shortcomings of the existing methods, this paper proposes a new deep learning-based high-definition rendering solution for the pipeline image. In this paper, the pipeline image is preprocessed, and then the processed pipeline image is converted into a style pipeline image through the pipeline image style transfer technology, and the obtained style pipeline image is postprocessed to enhance the effect. The preprocessing of pipeline images mainly includes pipeline image enhancement and pipeline image filtering operations. Its purpose is to change the distribution of pipeline images to improve the quality of pipeline images and make them more suitable for subsequent style conversion. In the part of pipeline image style conversion, this paper proposes a new deep learning-based pipeline image high-definition rendering network, which consists of three subnetworks: pipeline image feature modeling module, feature model alignment module, and pipeline image re-rendering module. This article has conducted sufficient experiments to fully compare the processing results of the method proposed in this article and other existing methods and at the same time shows the high-quality high-definition rendering results. The experimental results verify the excellent performance of the method proposed in this paper.
... While surveying for related works, we noticed that (Ogayar-Anguita et al., 2020) has recently presented a work with similar scope and impressive outcomes. It integrated with the GPU through GLSL, making the work independent from GPU vendors. ...
... The most related studies from our work includes (Gascon et al., 2013) and (Ogayar-Anguita et al., 2020), using tetrahedral mesh rasterization which successfully produced better outcomes than that of other methods. (Ogayar-Anguita et al., 2020), proposed a parallelized method that takes no assumption of the GPU vendor running on the target machine. ...
... The most related studies from our work includes (Gascon et al., 2013) and (Ogayar-Anguita et al., 2020), using tetrahedral mesh rasterization which successfully produced better outcomes than that of other methods. (Ogayar-Anguita et al., 2020), proposed a parallelized method that takes no assumption of the GPU vendor running on the target machine. ...
When obtaining interior 3D voxel data from triangular meshes, most existing methods fail to handle low quality meshes which happens to take up a big portion on the internet. In this work we present a robust voxelization method that is based on tetrahedral mesh generation within a user defined error bound. Comparing to other tetrahedral mesh generation methods, our method produces much higher quality tetrahedral meshes as the intermediate outcome, which allows us to utilize a faster voxelization algorithm that is based on a stronger assumption. We show the results comparing to various methods including the state-of-the-art. Our contribution includes a framework which takes triangular mesh as an input and produces voxelized data, a proof to an unproved algorithm that performs better than the state-of-the-art, and various experiments including parallelization built on the GPU and CPU. We further tested our method on various dataset including Princeton ModelNet and Thingi10k to show the robustness of the framework, where near 100% availability is achieved, while others can only achieve around 50%.
... Architecture framework and strategy [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] Scheduling and communication [26][27][28][29] Image processing and computer vision [30][31][32][33][34][35][36][37][38][39][40] Medical or health [41][42][43][44] Modeling or prediction [45][46][47][48][49][50][51] Convolution or performance analysis [6,[52][53][54] VLSI placement [55] GPU-based Machine Learning Technologies for EI Architecture platform [56][57][58][59][60][61][62][63][64][65] Applications [66][67][68][69][70][71][72][73][74][75][76][77] GPU: Graphic Process Unit; EI: Embedded Intelligence. Table 1 gives an overview and classification of EI research on GPU-based architecture technologies and applications. ...
... The authors in [30] proposed a GPU-based framework for training 3D CNNs using the voxelization of polygonal models. Their approach uses geometric transformations and vertex displacement computations for data augmentation in the GPU. ...
This paper present contributions to the state-of-the art for graphics processing unit (GPU-based) embedded intelligence (EI) research for architectures and applications. This paper gives a comprehensive review and representative studies of the emerging and current paradigms for GPU-based EI with the focus on the architecture, technologies and applications: (1) First, the overview and classifications of GPU-based EI research are presented to give the full spectrum in this area that also serves as a concise summary of the scope of the paper; (2) Second, various architecture technologies for GPU-based deep learning techniques and applications are discussed in detail; and (3) Third, various architecture technologies for machine learning techniques and applications are discussed. This paper aims to give useful insights for the research area and motivate researchers towards the development of GPU-based EI for practical deployment and applications.
Typically, before constructing an object with an additive manufacturing system, the 3D object must be sent through a process called slicing. Slicing converts a 3D object commonly in the form of an STL file into a set of layers by horizontally intersecting a plane with the object at various heights. At each height, called a layer, multiple 2D polygons can be generated. Each polygon represents a boundary for solid geometry and is called an island. Each island is then comprised of multiple path types in an attempt to optimally fill the polygon. To move between each island and each islands’ paths, travels are inserted. Travels are simply motion by the system to move from one area of construction to another. Travels do not contribute to the construction of the object, and so, are considered wasted motion. In large-scale additive manufacturing, objects can be quite large and the distance between islands can be large as well. As a result, these travels can waste a significant amount of time. Ideally, travels would be as short as possible, however, computing global minimal travel paths is computationally expensive. To combat this problem, researchers at Oak Ridge National Lab developed a GPU-based approach to travel insertion based on a unique factoradic representation. This representation was then utilized by the GPU to solve the Traveling Salesman Problem (TSP). This algorithm was able to compute global minimal travel paths quickly resulting in faster object construction. A general investigation was also carried out to determine when a GPU vs CPU implementation would be beneficial.
