Urban vegetation segmentation using terrestrial LiDAR point clouds based on point non-local means network

Abstract

Urban vegetation inventory at city-scale using terrestrial light detection and ranging (LiDAR) point clouds is very challenging due to the large quantity of points, varying local density, and occlusion effects, leading to missing features and incompleteness of data. This paper proposes a novel method, named Point Non-Local Means (PointNLM) network, which incorporates the supervoxel-based and point-wise for automatic semantic segmentation of vegetation from large scale complex scene point clouds. PointNLM captures the long-range relationship between groups of points via a non-local branch cascaded three times to describe sharp geometric features. Simultaneously, a local branch processes the position of scattered feature points and captures the low and high level features. Finally, we proposed a fusion layer of neighborhood max-pooling method to concatenate the long-range features, low level features and high level features for segmenting the trees. The proposed architecture was evaluated on three datasets, including two open access datasets of Semantic3D and Paris-Lille-3D, and an in-house dataset acquired by a commercial mobile LiDAR system. Experimental results indicated that the proposed method provides an efficient and robust result for vegetation segmentation, achieving an Intersection over Union (IoU) of 94.4%, F1-score of 92.7% and overall accuracy of 96.3%, respectively.

Keywords

• Non-local means
• Vegetation inventory
• LiDAR point clouds
• PointNLM