Automatic Labeled LiDAR Data Generation based on Precise Human Model
This reduces labeling costs for LiDAR training data in human recognition tasks, but it is incremental as it builds on existing data generation methods.
The authors tackled the high cost of manual labeling for LiDAR-based human recognition by proposing an automatic labeled data generation pipeline using a human model and background, resulting in over 500k realistic artificial data points.
Following improvements in deep neural networks, state-of-the-art networks have been proposed for human recognition using point clouds captured by LiDAR. However, the performance of these networks strongly depends on the training data. An issue with collecting training data is labeling. Labeling by humans is necessary to obtain the ground truth label; however, labeling requires huge costs. Therefore, we propose an automatic labeled data generation pipeline, for which we can change any parameters or data generation environments. Our approach uses a human model named Dhaiba and a background of Miraikan and consequently generated realistic artificial data. We present 500k+ data generated by the proposed pipeline. This paper also describes the specification of the pipeline and data details with evaluations of various approaches.