论文标题
DPDIST：使用深点云距离比较点云
DPDist : Comparing Point Clouds Using Deep Point Cloud Distance
论文作者
论文摘要
我们引入了一种新的深度学习方法，以进行点云比较。我们的方法称为深点云距离（DPDIST），测量了一个云中的点与对另一个点云被采样的估计表面之间的距离。使用3D修改的Fisher矢量表示，局部有效地估算了表面。局部表示降低了表面的复杂性，从而实现了有效的学习，从而在对象类别之间很好地概括了。我们在具有挑战性的任务中测试了提议的距离，例如类似的对象比较和注册，并表明它比常用距离，例如倒角距离，地球搬运工的距离等提供了重大改进。
We introduce a new deep learning method for point cloud comparison. Our approach, named Deep Point Cloud Distance (DPDist), measures the distance between the points in one cloud and the estimated surface from which the other point cloud is sampled. The surface is estimated locally and efficiently using the 3D modified Fisher vector representation. The local representation reduces the complexity of the surface, enabling efficient and effective learning, which generalizes well between object categories. We test the proposed distance in challenging tasks, such as similar object comparison and registration, and show that it provides significant improvements over commonly used distances such as Chamfer distance, Earth mover's distance, and others.
