论文标题
MG-SAGC：一个多尺度图及其3D点云的自适应图卷积网络
MG-SAGC: A multiscale graph and its self-adaptive graph convolution network for 3D point clouds
论文作者
论文摘要
在本文中，为了增强神经网络提取本地点云特征并提高其质量的能力，我们提出了一种多尺度图生成方法和一种自适应图形卷积方法。首先，我们为点云提出了一种多尺度图生成方法。这种方法将点云转换为结构化的多尺度图表，该图支持刻度空间中点云的多尺度分析，并可以在不同尺度上获得点云数据的维数特征，从而使获得最佳的点云特征变得更加容易。由于传统的卷积神经网络不适用于具有不规则顶点邻域的图形数据，因此本文提出了一种SEF自适应图卷积内核，该核心使用Chebyshev多项式使用基于最佳近似理论来适合不规则卷积过滤器。在本文中，我们采用最大池来综合不同比例地图的特征并生成点云特征。在在三个广泛使用的公共数据集上进行的实验中，该提出的方法显着优于其他最先进的模型，证明了其有效性和可推广性。
To enhance the ability of neural networks to extract local point cloud features and improve their quality, in this paper, we propose a multiscale graph generation method and a self-adaptive graph convolution method. First, we propose a multiscale graph generation method for point clouds. This approach transforms point clouds into a structured multiscale graph form that supports multiscale analysis of point clouds in the scale space and can obtain the dimensional features of point cloud data at different scales, thus making it easier to obtain the best point cloud features. Because traditional convolutional neural networks are not applicable to graph data with irregular vertex neighborhoods, this paper presents an sef-adaptive graph convolution kernel that uses the Chebyshev polynomial to fit an irregular convolution filter based on the theory of optimal approximation. In this paper, we adopt max pooling to synthesize the features of different scale maps and generate the point cloud features. In experiments conducted on three widely used public datasets, the proposed method significantly outperforms other state-of-the-art models, demonstrating its effectiveness and generalizability.
