  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.
