  The point cloud is gaining prominence as a method for representing 3D shapes,
but its irregular format poses a challenge for deep learning methods. The
common solution of transforming the data into a 3D voxel grid introduces its
own challenges, mainly large memory size. In this paper we propose a novel 3D
point cloud representation called 3D Modified Fisher Vectors (3DmFV). Our
representation is hybrid as it combines the discrete structure of a grid with
continuous generalization of Fisher vectors, in a compact and computationally
efficient way. Using the grid enables us to design a new CNN architecture for
point cloud classification and part segmentation. In a series of experiments we
demonstrate competitive performance or even better than state-of-the-art on
challenging benchmark datasets.
