  In this work, we propose Dilated Point Convolutions (DPC). In a thorough
ablation study, we show that the receptive field size is directly related to
the performance of 3D point cloud processing tasks, including semantic
segmentation and object classification. Point convolutions are widely used to
efficiently process 3D data representations such as point clouds or graphs.
However, we observe that the receptive field size of recent point convolutional
networks is inherently limited. Our dilated point convolutions alleviate this
issue, they significantly increase the receptive field size of point
convolutions. Importantly, our dilation mechanism can easily be integrated into
most existing point convolutional networks. To evaluate the resulting network
architectures, we visualize the receptive field and report competitive scores
on popular point cloud benchmarks.
