Mr. Uenishi received the IPSJ Outstanding Paper Award / 硕士课程工学专业计算机理工课程（当时）的上西和树等人的论文荣获信息处理学会2019年度论文奖

　A paper authored by Mr. Kazuki Uenishi (Completed a Master’s degree in Computer Science and Engineering, now at IVIS inc.) and his colleagues has been awarded the IPSJ Outstanding Paper Award. This award is given to the best author of a paper published in the Journal of the Information Processing Society of Japan. In 2019, six papers were selected out of 563 papers eligible for selection.
　The title of his paper was “Unsupervised Representation Learning for 3D Point Set by using Generative Adversarial Neural Network”. This paper proposed and experimentally evaluated shape features for indexing, retrieval, and classification a large number of 3D shape data without categorical labels. They also showed that the application of Generative Adversarial Neural Network(GAN) , a type of deep learning technology, to 3D point sets enables them to obtain shape features with higher accuracy than previous research.
　Dr. Takahiko Furuya, an Assistant Professor in the Faculty of Engineering, and Dr. Ryutaro Ohbuchi, a doctor in the Faculty of Engineering, were jointly awarded.
　“I very honored to receive this honorable award for our paper,” said Mr. Uenishi, who received the award. “I would like to express my heartfelt gratitude to Dr. Obuchi and Dr. Furuya, as well as to all the people who guided us and helped us in my research and writing of the paper.”

〇Awarded Papers
KAZUKI UENISHI,? TAKAHIKO FURUYA,? RYUTAROU OHBUCHI,
“Unsupervised Representation Learning for 3D Point Set by using Generative Adversarial Neural Network”
Journal of the Information Processing Society of Japan, Vol.60, No.7, pp.1315-1324, 2019.

〇IPSJ Outstanding Paper Award

　上西和树（硕士课程工学专业计算机理工课程毕业，现工作于株式会社IVIS）等人撰写的论文荣获信息处理学会2019年度论文奖。该奖项授予在信息处理学会机构发表的论文中特别优秀的论文，2019年度从563篇论文中遴选出了6篇。
　获奖论文的题目是《利用敌对生成网络的三维点群形状特征量的无监督学习》。论文提取了大量的无标签三维形状数据的形状特征量，用于按形状进行分类、比较、检索，并通过实验对其进行评价。另外，论文还将深层学习（深度学习）的技术之一敌对生成神经网络应用于三维点群，显示了比以往研究更高精度的形状特征量。
　此次共同获奖的还有古屋贵彦 工学部助教和大渕竜太郎 工学部教授。
　获奖的上西同学说：“这次我们的论文能够获得如此殊荣，我感到非常荣幸。向大渕老师、古屋老师，以及其他在研究和论文写作方面给予指导和协助的各位，表示衷心的感谢。”
　另外，关于获奖论文的介绍报道将刊登在信息处理学会杂志61卷8号上。敬请关注阅览。

〇 获奖论
上西 和树，古屋 贵彦，大渕 竜太郎，
《利用敌对生成网络的三维点群形状特征量的无监督学习》
信息处理学会杂志，Vol.60, No.7, pp.1315-1324, 2019.

〇信息处理学会论文奖