Completing shapes of point clouds from partial scans is a fundamental problem for 3-D vision and remote sensing. However, recent methods mainly relied on K-nearest neighbors (KNN) operations to extract local features of point clouds, which are susceptible to outliers and have limited ability to capture features from long-range context information. In this letter, we propose a new framework with an encoder–decoder architecture, named adaptive global feature augmentation network (AGFA-Net) for point cloud completion. The network mainly consists of spatial and channel attention blocks. Spatial attention blocks are used to replace KNN operations and aggregate global features adaptively by calculating per-point attention values, and channel attention blocks are used to augment useful features of geometric details. Meanwhile, several skip connections are added between different attention blocks to selectively convey geometric features from local regions of partial point clouds to the completion process. Experimental results and analyses demonstrate that our method can generate finer shapes of point clouds and outperforms other state-of-the-art methods under widely used benchmark point completion network (PCN) dataset and several terrestrial laser scanning (TLS) data.

中文翻译：

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AGFA-Net：用于点云补全的自适应全局特征增强网络

从部分扫描完成点云的形状是 3D 视觉和遥感的一个基本问题。然而，最近的方法主要依靠 K 近邻（KNN）操作来提取点云的局部特征，这些特征容易受到异常值的影响，并且从远程上下文信息中捕获特征的能力有限。在这封信中，我们提出了一个具有编码器-解码器架构的新框架，称为自适应全局特征增强网络 (AGFA-Net)，用于点云补全。该网络主要由空间和通道注意块组成。空间注意力块用于替换 KNN 操作并通过计算每个点的注意力值自适应地聚合全局特征，通道注意力块用于增强几何细节的有用特征。同时，在不同的注意力块之间添加了几个跳跃连接，以选择性地将几何特征从部分点云的局部区域传递到完成过程。实验结果和分析表明，我们的方法可以生成更精细的点云形状，并且在广泛使用的基准点完成网络（PCN）数据集和多个地面激光扫描（TLS）数据下优于其他最先进的方法。