Automatic Labeled LiDAR Data Generation and Distance-Based Ensemble Learning for Human Segmentation

Abstract

Following the improvements in deep neural networks, state-of-the-art networks have been proposed for human segmentation using point clouds captured by light detection and ranging. However, the performance of these networks depends significantly on the training data. An issue with collecting training data is labeling. Labeling by humans is necessary to obtain ground-truth labels; however, labeling involves high costs. Therefore, we propose an automatically labeled data generation pipeline, for which we can change any parameters or data generation environments. Our approach uses a human model named Dhaiba and the background of Miraikan to generate realistic artificial data. We present 1M data generated by the proposed pipeline. Furthermore, we propose an ensemble learning based on generated data for utilizing our data generation pipeline. This paper proposes the specifications of the pipeline, data details, and explanation of ensemble learning with evaluations of various approaches.