KPConv: Flexible and Deformable Convolution for Point Clouds

@inproceedings{thomas2019kpconv,
  title={Kpconv: Flexible and deformable convolution for point clouds},
  author={Thomas, Hugues and Qi, Charles R and Deschaud, Jean-Emmanuel and
          Marcotegui, Beatriz and Goulette, Fran{\c{c}}ois and Guibas,
          Leonidas J},
  booktitle={Proceedings of the IEEE/CVF international conference on computer
             vision},
  pages={6411--6420},
  year={2019}
}

This paper introduces a new point convolution operator named Kernel Point Convolution (KPConv). KPConv also consists of a set of local 3D filters, but overcomes previous point convolution limitations as shown in related work. KPConv is inspired by image-based convolution, but in place of kernel pixels, we use a set of kernel points to define the area where each kernel weight is applied, like shown in Figure 1. The kernel weights are thus carried by points, like the input features, and their area of influence is defined by a correlation function. The number of kernel points is not constrained, making our design very flexible.

• the network generates a set of shifts for every convolution location