In this paper, we propose a novel and effective approach, namely GridNet, to hierarchically learn deep representation of 3D point clouds. It incorporates the ability of regular holistic description and fast data processing in a single framework, which is able to abstract powerful features progressively in an efficient way. Moreover, to capture more accurate internal geometry attributes, anchors are inferred within local neighborhoods, in contrast to the fixed or the sampled ones used in existing methods, and the learned features are thus more representative and discriminative to local point distribution. GridNet delivers very competitive results compared with the state of the art methods in both the object classification and segmentation tasks.

在本文中，我们提出了一种新颖有效的方法，即 GridNet，以分层学习 3D 点云的深度表示。它将规则整体描述和快速数据处理的能力结合在一个框架中，能够以高效的方式逐步抽象出强大的特征。此外，为了捕获更准确的内部几何属性，与现有方法中使用的固定或采样的锚点相比，在局部邻域内推断锚点，因此学习到的特征对局部点分布更具代表性和辨别力。与对象分类和分割任务中的最先进方法相比，GridNet 提供了非常有竞争力的结果。