Improved deep learning segmentation of outdoor point clouds with different sampling strategies and using intensities

Abstract

The rapid growth of outdoor digital twin data sets and advancements in 3D data acquisition technology have sparked interest in improving segmentation performance using deep learning. This research aims to analyze and evaluate different sampling strategies and optimization techniques while exploring the intensity information of outdoor point cloud data. Two sampling strategies, random and stratified sampling, are employed to divide a limited data set. Additionally, the data set is divided into point cloud data with and without intensity. The PointNet++ model is used to segment the point cloud data into two classes, vegetation and structure. The results indicate that stratified sampling outperforms random sampling, yielding a considerable improvement in mean intersection over union scores of up to 10%. Interestingly, the inclusion of intensity information in the data set does not universally enhance performance. Although the use of intensity improves the performance of random sampling, it does not benefit stratified sampling. This research provides insights into the effectiveness of different sampling strategies for outdoor point cloud data segmentation. The findings can contribute to the development of optimized approaches to improving segmentation accuracy in outdoor digital twin applications using deep learning techniques.