PointVGG: Graph convolutional network with progressive aggregating features on point clouds

Li, Rongkang; Zhang, Yumeng; Niu, Dongmei; Yang, Guangchao; Zafar, Numan; Zhang, Caiming; Zhao, Xiuyang

doi:10.1016/j.neucom.2020.10.086

Cited by 18 publications

(4 citation statements)

References 14 publications

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“…Experimental results show that PointNGCNN achieves good performance in the 3D recognition and segmentation tasks. Li et al [82] proposed point convolution (P conv ) and point pooling (P pool ) for 3D points based on the graph structure and designed a novel point cloud feature learning network, PointVGG. Among them, P conv learns the geometric information between the center point and its neighboring points.…”

Section: Gcn-based Methodsmentioning

confidence: 99%

Deep-Learning-Based Point Cloud Semantic Segmentation: A Survey

Zhang,

Wu,

Jin

et al. 2023

Electronics

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With the rapid development of sensor technologies and the widespread use of laser scanning equipment, point clouds, as the main data form and an important information carrier for 3D scene analysis and understanding, play an essential role in the realization of national strategic needs, such as traffic scene perception, natural resource management, and forest biomass carbon stock estimation. As an important research direction in 3D computer vision, point cloud semantic segmentation has attracted more and more researchers’ attention. In this paper, we systematically outline the main research problems and related research methods in point cloud semantic segmentation and summarize the mainstream public datasets and common performance evaluation metrics. Point cloud semantic segmentation methods are classified into rule-based methods and point-based methods according to the representation of the input data. On this basis, the core ideas of each type of segmentation method are introduced, the representative and innovative algorithms of each type of method are elaborated, and the experimental results on the datasets are compared and analyzed. Finally, some promising research directions and potential tendencies are proposed.

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Section: Gcn-based Methodsmentioning

confidence: 99%

Deep-Learning-Based Point Cloud Semantic Segmentation: A Survey

Zhang,

Wu,

Jin

et al. 2023

Electronics

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“…To ensure representativeness in the analysis, it is assumed that the number of channels in the input and output feature maps remains consistent. In traditional convolution operations, as illustrated in Figure 4a, assume that the dimensions of the input feature map are height h, width w, and number of channels c. FLOPs can be calculated using the following formula to quantify the computational complexity of the convolution operation [23]. This approach provides a basis for further analysis and optimization of the convolutional neural network structure, thereby reducing the consumption of computational resources while ensuring network performance.…”

Section: Pconvmentioning

confidence: 99%

Advancements in Electric Vehicle PCB Inspection: Application of Multi-Scale CBAM, Partial Convolution, and NWD Loss in YOLOv5

Xu,

Wang,

Chen

2024

WEVJ

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In the rapidly evolving electric vehicle industry, the reliability of electronic systems is critical to ensuring vehicle safety and performance. Printed circuit boards (PCBs), serving as a cornerstone in these systems, necessitate efficient and accurate surface defect detection. Traditional PCB surface defect detection methods, like basic image processing and manual inspection, are inefficient and error-prone, especially for complex, minute, or irregular defects. Addressing this issue, this study introduces a technology based on the YOLOv5 network structure. By integrating the Convolutional Block Attention Module (CBAM), the model’s capability in recognizing intricate and small defects is enhanced. Further, partial convolution (PConv) replaces traditional convolution for more effective spatial feature extraction and reduced redundant computation. In the network’s final stage, multi-scale defect detection is implemented. Additionally, the normalized Wasserstein distance (NWD) loss function is introduced, considering relationships between different categories, thereby effectively solving class imbalance and multi-scale defect detection issues. Training and validation on a public PCB dataset showed the model’s superior detection accuracy and reduced false detection rate compared to traditional methods. Real-time monitoring results confirm the model’s ability to accurately detect various types and sizes of PCB surface defects, satisfying the real-time detection needs of electric vehicle production lines and providing crucial technical support for electric vehicle reliability.

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“…Aggregating the neighborhood features of points onto a point inspired us to construct local neighborhood graphs and gather the graph features to the central point. To further capture additional local geometric information, PointVGG [21] defines operations of convolution and pooling, i.e., Pconv and Ppool, respectively. The Pconv could pay attention to the relations between neighbors, and the Ppool makes the network abstract the underlying shape information.…”

Section: Direct Representation Of Original Point Cloudsmentioning

confidence: 99%

MFNet: Multi-Level Feature Extraction and Fusion Network for Large-Scale Point Cloud Classification

Zhang

et al. 2022

Remote Sensing

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The accuracy with which a neural network interprets a point cloud depends on the quality of the features expressed by the network. Addressing this issue, we propose a multi-level feature extraction layer (MFEL) which collects local contextual feature and global information by modeling point clouds at different levels. The MFEL is mainly composed of three independent modules, including the aggregated GAPLayer, the spatial position perceptron, and the RBFLayer, which learn point cloud features from three different scales. The aggregated GAPLayer aggregates the geometry features of neighboring points in a local coordinate system to centroid by graph convolution. Then, the spatial position perceptron independently learns the position features of each point in the world coordinate system. Finally, the RBFLayer aggregates points into pointsets according to the correlation between features, and extracts features from the pointset scale through the quantization layer. Based on the MFEL, an end-to-end classification and segmentation network, namely the MFNet and MFNet-S, is proposed. In the proposed network, the channel-attention mechanism is employed to better aggregate multi-level features. We conduct classification and semantic segmentation experiments on four standard datasets. The results show that the proposed method outperforms the compared methods on the multiple datasets, resulting in 93.1% classification accuracy in ModelNet40. Furthermore, the mIoU of part semantic segmentation in ShapeNet is 85.4%, and the mIoU for semantic segmentation in S3DIS and Semantic3D is 62.9% and 71.9%, respectively.

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PointVGG: Graph convolutional network with progressive aggregating features on point clouds

Cited by 18 publications

References 14 publications

Deep-Learning-Based Point Cloud Semantic Segmentation: A Survey

Deep-Learning-Based Point Cloud Semantic Segmentation: A Survey

Advancements in Electric Vehicle PCB Inspection: Application of Multi-Scale CBAM, Partial Convolution, and NWD Loss in YOLOv5

MFNet: Multi-Level Feature Extraction and Fusion Network for Large-Scale Point Cloud Classification

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