International Journal of Applied Earth Observations and Geoinformation (Aug 2022)

LEARD-Net: Semantic segmentation for large-scale point cloud scene

  • Ziyin Zeng,
  • Yongyang Xu,
  • Zhong Xie,
  • Wei Tang,
  • Jie Wan,
  • Weichao Wu

Journal volume & issue
Vol. 112
p. 102953

Abstract

Read online

Given the prominence of 3D sensors in recent years, 3D point cloud scene data are worthy to be further investigated. Point cloud scene understanding is a challenging task because of its characteristics of large-scale and discrete. In this study, we propose a network called LEARD-Net, focuses on semantic segmentation for the large-scale point cloud scene data with color information. The proposed network contains three main components: (1) To fully utilize color information of point clouds rather than just as initial input features, we propose a robust local feature extraction module (LFE) to benefit the network focus on both spatial geometric structure, color information and semantic features. (2) We propose a local feature aggregation module (LFA) to benefit the network to focus on the local significant features while also focus on the entire local neighbor. (3) To allow the network to focus on both local and comprehensive features, we use residual and dense connections (ResiDense) to connect different-level LFE and LFA modules. Comparing with state-of-the-art networks on several large-scale benchmark datasets, including S3DIS, Toronto3D and Semantic3D, we demonstrate the effectiveness of our LEARD-Net.

Keywords