  The semantic segmentation of point clouds is an important part of the
environment perception for robots. However, it is difficult to directly adopt
the traditional 3D convolution kernel to extract features from raw 3D point
clouds because of the unstructured property of point clouds. In this paper, a
spherical interpolated convolution operator is proposed to replace the
traditional grid-shaped 3D convolution operator. This newly proposed feature
extraction operator improves the accuracy of the network and reduces the
parameters of the network. In addition, this paper analyzes the defect of point
cloud interpolation methods based on the distance as the interpolation weight
and proposes the self-learned distance-feature density by combining the
distance and the feature correlation. The proposed method makes the feature
extraction of spherical interpolated convolution network more rational and
effective. The effectiveness of the proposed network is demonstrated on the 3D
semantic segmentation task of point clouds. Experiments show that the proposed
method achieves good performance on the ScanNet dataset and Paris-Lille-3D
dataset.
