@inproceedings{e6d39ff99ac34060b913bee5cfaeced7,
title = "Training PointNet for human point cloud segmentation with 3D meshes",
abstract = "PointNet, which enables end-to-end learning for scattered/unordered point data, is a popular neural network architecture. However, in many applications, large amounts of complete point clouds are hardly available for non-rigid objects such as the human body. To generate the training data of PointNet, in this study, we propose to generate human body point clouds of various postures by uniformly sampling point clouds from meshes with respect to multiple human mesh model datasets. Experiments show that the model trained with the point clouds generated from mesh data is effective in the task of human body segmentation.",
keywords = "Deep learning, Human body segmentation, Point cloud sampling",
author = "Takuma Ueshima and Katsuya Hotta and Shogo Tokai and Chao Zhang",
note = "Publisher Copyright: {\textcopyright} 2021 SPIE.; 15th International Conference on Quality Control by Artificial Vision ; Conference date: 12-05-2021 Through 14-05-2021",
year = "2021",
doi = "10.1117/12.2589075",
language = "英語",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Kenji Terada and Akio Nakamura and Takashi Komuro and Tsuyoshi Shimizu",
booktitle = "Fifteenth International Conference on Quality Control by Artificial Vision",
}