论文标题
GASCN：图形注意形状完成网络
GASCN: Graph Attention Shape Completion Network
论文作者
论文摘要
形状完成是在机器人和计算机视觉中推断给定对象云的对象的完整几何形状的问题。本文提出了图形注意形状完成网络（GASCN），这是一个解决此问题的新型神经网络模型。该模型结合了一个基于图的模型，用于编码本地点云信息以及用于编码全局信息的基于MLP的体系结构。对于每个完整的点，我们的模型渗透了局部表面贴片的正常和范围，该斑块用于产生致密而精确的形状完成。我们报告了实验，这些实验表明，在Shapenet数据集绘制的标准基准上，GASCN优于标准形状完成方法。
Shape completion, the problem of inferring the complete geometry of an object given a partial point cloud, is an important problem in robotics and computer vision. This paper proposes the Graph Attention Shape Completion Network (GASCN), a novel neural network model that solves this problem. This model combines a graph-based model for encoding local point cloud information with an MLP-based architecture for encoding global information. For each completed point, our model infers the normal and extent of the local surface patch which is used to produce dense yet precise shape completions. We report experiments that demonstrate that GASCN outperforms standard shape completion methods on a standard benchmark drawn from the Shapenet dataset.
