Automatic Labeled LiDAR Data Generation based on Precise Human Model

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.Comment: Accepted at ICRA201

Realization of a collective decoding of codeword states

This was also extended from the previous article quant-ph/9705043, especially in a realization of the decoding process.Comment: 6 pages, RevTeX, 4 figures(EPS