Joint boundary-aware and multi-feature fusion for point cloud semantic segmentation

Abstract

Current point cloud semantic segmentation methods based on deep learning tend to overlook the boundary of objects in transition area, resulting in the problem of ambiguous features at the boundary. This article proposed a point cloud semantic segmentation method with boundary-aware and multi-feature fusion (BA-MFF). Firstly, the backbone network was optimized to make the extracted features more robust. Secondly, boundary-aware module (BAM) was designed to focus on the boundaries of objects in transition area. This module consisted of a boundary point prediction module (BPPM) and a feature aggregation module (FAM). The boundary point prediction module predicted the points belonging to boundaries by learning the features of neighboring points, and the feature aggregation module performed discriminative aggregation of point cloud features within the neighborhood. Finally, for more discriminative features, a multi-feature fusion module (MFFM) was introduced, which fused features between different channels. The experimental results show that the mean Intersection over Union (mIoU) of this method reaches 63.7% on the ScanNetV2 dataset. The Overall Accuracy (OA) and mIoU on the S3DIS are 88.2% and 62.3%, respectively. The method in this paper effectively focuses on the transition area and has some segmentation superiority.