MFFAE-Net: semantic segmentation of point clouds using multi-scale feature fusion and attention enhancement networks

Abstract

Point cloud data can reflect more information about the real 3D space, which has gained increasing attention in computer vision field. But the unstructured and unordered nature of point clouds poses many challenges in their study. How to learn the global features of the point cloud in the original point cloud is a problem that has been accompanied by the research. In the research based on the structure of the encoder and decoder, many researchers focus on designing the encoder to better extract features, and do not further explore more globally representative features according to the features of the encoder and decoder. To solve this problem, we propose the MFFAE-Net method, which aims to obtain more globally representative point cloud features by using the feature learning of encoder decoder stage.Our method first enhances the feature information of the input point cloud by merging the information of its neighboring points, which is helpful for the following point cloud feature extraction work. Secondly, the channel attention module is used to further process the extracted features, so as to highlight the role of important channels in the features. Finally, we fuse features of different scales from encoding features and decoding features as well as features of the same scale, so as to obtain more global point cloud features, which will help improve the segmentation results of point clouds. Experimental results show that the method performs well on some objects in S3DIS dataset and Toronto3d dataset.