A large-scale point cloud semantic segmentation neural network based on long-range contextual dependencies enhancement

Abstract

ABSTRACT This letter presents a long-range contextual dependency enhanced network (LCDE-Net) for semantic segmentation of large-scale point cloud, which employs a U-shaped framework. Firstly, point clouds are subsampled with grid sampling and fed into convolutional layers to learn more representative local features of points. Then global and local encoders (GLE) are designed to exploit long-range contextual dependencies and local features simultaneously. The core of GLE consists of two parts: global feature enhancement (GFE) module and feature channel modulation (FCM) module. Secondly, through decoder layers, the encoded features are upsampled through the nearest-neighbour interpolation and aggregated with the intermediate encoded features by skip connections to capture multi-scale discriminative features for semantic segmentation of point cloud. Finally, via Fully Connection layer and Softmax classifier, each point’s label is assigned. Two different benchmark datasets are conducted to evaluate the performance of the proposed method, Experimental results report that the proposed LCDE-Net achieves 78.6% in terms of mean intersection over union (mIoU) on Semantic3D, and 68.2% on S3DIS, which is the highest among the comparison methods. The code of LCDE-Net is available at https://github.com/xrzmyz/LCDE-Net.