PFCN: A fully convolutional network for point cloud semantic segmentation

Abstract

It is a challenging task to use deep learning methods to understand point cloud data and assign semantics due to the complexity of point cloud data structure. In this Letter, a fully convolutional network is designed by the authors to perform point cloud semantic segmentation. The proposed network based on PointNet++ takes point cloud data as input and predicts a semantic label for each point. Their network consists of three parts. In the first, different scale features are extracted, the second part reduces the extracted features and then fuses them, and in the third part, more structural information of the point cloud is preserved by up-sampling with deconvolution to reconstruct the point cloud. They have carried out part segmentation and semantic segmentation of ShapeNet and S3DIS datasets, respectively, and the validity of the network has been verified.