论文标题
Kepoint AutoCoders：学习语义的兴趣点
Keypoint Autoencoders: Learning Interest Points of Semantics
论文作者
论文摘要
了解点云至关重要。许多以前的方法着重于检测到点云的身份结构的显着关键。但是，现有方法忽略了所选点的语义，导致下游任务的性能差。在本文中，我们提出了KePoint AutoCoder，这是一种用于检测关键点的无监督学习方法。我们鼓励通过从关键点到原始点云的重建来选择稀疏的语义关键。为了使稀疏关键点选择可区分，通过计算输入点之间的加权平均值来采用软关键点。将形状用稀疏关键点进行分类的下游任务是为了证明我们选定的关键点的独特性。提出了语义的准确性和语义丰富性，我们的方法比这两个指标的艺术状态具有竞争性甚至更好的性能。
Understanding point clouds is of great importance. Many previous methods focus on detecting salient keypoints to identity structures of point clouds. However, existing methods neglect the semantics of points selected, leading to poor performance on downstream tasks. In this paper, we propose Keypoint Autoencoder, an unsupervised learning method for detecting keypoints. We encourage selecting sparse semantic keypoints by enforcing the reconstruction from keypoints to the original point cloud. To make sparse keypoint selection differentiable, Soft Keypoint Proposal is adopted by calculating weighted averages among input points. A downstream task of classifying shape with sparse keypoints is conducted to demonstrate the distinctiveness of our selected keypoints. Semantic Accuracy and Semantic Richness are proposed and our method gives competitive or even better performance than state of the arts on these two metrics.
