亚洲图形学学会 (Asiagraphics) 是亚洲缱绻机图形和交互工夫规模的专科学术组织，2016年10月12日在日本冲绳召开的Pacific Grahics会议上雅致成立。2017年，亚洲图形学诞生毕生建立奖，每两年评审一次。2018年，增设隆起工夫孝顺奖和后生学者奖寂寞少妇，每年评审一次。亚洲图形学学会后生学者奖旨在赏赐处于管事生存早期（获博士学位后不向上6年）的年青学者在缱绻机图形学与交互工夫规模作念出的卓越孝顺，该奖项每年在亚太地区颁发给又名获奖者。2023年该奖项授予北京大学王选缱绻机究诘所的助理陶冶王鹏帅博士，获奖者由Ming Lin陶冶（the University of Maryland，好意思国）和Leif Kobbelt 陶冶（RWTH Aachen University，德国）所担任主席的评审团选出。

亚洲图形学学会的官网评价王鹏帅博士的孝顺如下（英文原版）：

肛交 小说

Dr. Peng-Shuai Wang is a tenure-track Assistant Professor at Peking University. Before joining Peking University in 2022， he was a senior researcher in Microsoft Research Asia. He got Ph.D. degree from the Institute for Advanced Study at Tsinghua University in 2018， under the supervision of Dr. Baining Guo. Dr. Wang has done a series of remarkable research works on fundamental network structures and algorithms for 3D shape analysis and generation， which significantly advance the state-of-the-art of 3D geometric deep learning and make impactful contributions to both computer graphics and computer vision.

Dr. Wang’s research on Octree-based Sparse Convolutional Networks (O-CNN， SIGGRAPH 2017) lays a solid foundation for learning-based 3D shape analysis and generation and attracts considerable attention in the research field. O-CNN significantly reduces the computational and memory complexity of 3D deep learning from O(N^3) to O(N^2) and has been widely used in various 3D learning tasks， including 3D classification， segmentation， and detection. His work on Adaptive O-CNN (SIGGRAPH Asia 2018) also greatly improves the state-of-the-art for shape representation and generation.

To generate continuous surfaces and further improve the reconstruction of geometric details， Dr. Wang proposed Dual Octree Graph Networks (SIGGRAPH 2022) that offers an adaptive deep representation of 3D volumetric fields and associated graph neural networks， which greatly improves the efficiency and performance for shape generation and reconstruction. As transformer-based backbone networks have been widely used in 2D vision and NLP fields， Dr. Wang recently proposed OctFormer (SIGGRAPH 2023) that is not only significantly faster than previous point cloud transformers寂寞少妇， but also achieves state-of-the-art performances in various 3D understanding tasks.

Additionally， Dr. Wang is also well known by his outstanding works on traditional and learning-based digital geometry processing， including his early work on learning-based mesh denosing (SIGGRAPH Asia 2016)， and interactive geometric feature editing (SIGGRAPH Asia 2015)， as well as his recent works on geodesic distance computation with graph neural networks (GeGNN in SIGGRAPH Asia 2023).

Dr. Wang also actively serves the graphics communities as the PC members of graphics conferences (e.g. Eurographics 2024， CVM 2023 & 2024.)， and the paper reviewers of graphics and vision conferences and journals， such as ACM SIGGRAPH/TOG， IEEE TVCG， CVPR and CVMJ.

亚洲图形学学会的官网评价王鹏帅博士的孝顺如下（译文）：

王鹏帅博士于2022年9月加入北京大学王选缱绻机究诘所，此前他曾担任微软亚洲究诘院的高档究诘员。王鹏帅博士在2018年于清华大学高等究诘院取得博士学位，导师为郭百宁陶冶。王鹏帅博士在三维情势融合和生成标的作念了一系列出色的究诘责任，权贵鼓动了三维几何深度学习的发展，在缱绻机图形学和缱绻机视觉规模作念出了有影响力的孝顺。

王鹏帅博士提议的基于八叉树的稀薄卷积聚集（O-CNN，SIGGRAPH 2017）将三维卷积神经网的运算和存储法例在稀薄的三维体素里，将原始的三维体素卷积神经网的运算和存储效力普及了上百倍，该次序被鄙俚利用于多样三维深度学习任务中，如三维情势的分类、分割和检测等。 而后，王鹏帅博士又提议Adaptive O-CNN（SIGGRAPH Asia 2018），权贵提高了三维情势示意和生成的质地。为了生成连合曲面并进一步普及神经聚集重建几何细节的才略，王鹏帅博士提议了对偶八叉树的图神经聚集（SIGGRAPH 2022），极大提高了三维情势生成的效力和质地。跟着 Transformer 被鄙俚利用于缱绻机视觉和 NLP 规模，王鹏帅博士最近提议了基于八叉树的点云Transformer (OctFormer， SIGGRAPH 2023)，比较于畴前的点云 Transformer ，其在速率和后果方面齐取得了刻放学术界的最好性能。

此外，王鹏帅博士还在数字几那边置方面的作念出了优秀的究诘责任，包括基于学习的三角网格网格去噪（SIGGRAPH Asia 2016），交互式几何特征裁剪（SIGGRAPH Asia 2015）， 以及基于图神经聚集进行测地距离缱绻的责任（GeGNN，SIGGRAPH Asia 2023）。

王鹏帅博士还积极办事于图形学规模，担任著名的图形学国外会议（如Eurographics 2024、CVM 2023 & 2024等）的会议形势委员，以及缱绻机图形学和缱绻机视觉会议、期刊的审稿东谈主（如ACM SIGGRAPH/TOG、IEEE TVCG、 CVPR 和 CVMJ等）。

更多信息参见亚洲图形学学会（Asiagraphics）官网的新闻：

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