面向点云补全的鲁棒图关注网络
Journal of China Jiliang University（2023）
Abstract
目的:利用图神经网络与关注机制,构建具有鲁棒性的图关注网络模型并用于点云补全任务.方法:首先,用自关注机制构造局部图的邻接矩阵,不仅考虑中心点与邻域点的关系,而且还考虑邻域点之间的内在相关性,从而有效提取点云的局部几何结构信息.其次,利用邻域中邻域点的特征信息,自适应地更新局部中心点的坐标与特征.此时,以每个点新的坐标组成的点云更能准确描述物体的几何结构细节,增强抗噪能力.最后,为了增强特征提取的鲁棒性,利用残差连接分别融合经过多次微调的点特征作为全局特征,以此来生成点云.结果:与其他点云补全方法在多个常用数据集实验相比,本文构建模型具有最优的补全效果.结论:利用具有鲁棒性的图关注网络模型在点云补全任务中具有先进性.
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