亚洲图形学学会 (Asiagraphics) 是亚洲计算机图形和交互技术领域的专业学术组织，2016年10月12日在日本冲绳召开的Pacific Grahics会议上正式成立。2017年，亚洲图形学设立终身成就奖，每两年评审一次。2018年，增设杰出技术贡献奖和青年学者奖，每年评审一次。亚洲图形学学会青年学者奖旨在表彰处于职业生涯早期（获博士学位后不超过6年）的年轻学者在计算机图形学与交互技术领域做出的突出贡献，该奖项每年在亚太地区颁发给一名获奖者。2023年该奖项授予北京大学王选计算机研究所的助理教授王鹏帅博士，获奖者由Ming Lin教授（the University of Maryland，美国）和Leif Kobbelt 教授（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贡献如下（英文原版）：
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, 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贡献如下（译文）：
王鹏帅博士于2022年9月加入北京大学王选计算机研究所，此前他曾担任微软亚洲研究院的高级研究员。王鹏帅博士在2018年于清华大学高等研究院获得博士学位，导师为郭百宁教授。王鹏帅博士在三维形状理解和生成方向做了一系列出色的研究工作，显著推进了三维几何深度学习的发展，在计算机图形学和计算机视觉领域做出了有影响力的贡献。
王鹏帅博士提出的基于八叉树的稀疏卷积网络（O-CNN，SIGGRAPH 2017）将三维卷积神经网的运算和存储限制在稀疏的三维体素里，将原始的三维体素卷积神经网的运算和存储效率提升了上百倍，该方法被广泛应用于各种三维深度学习任务中，如三维形状的分类、分割和检测等。 此后，王鹏帅博士又提出Adaptive O-CNN（SIGGRAPH Asia 2018），显著提高了三维形状表示和生成的质量。为了生成连续曲面并进一步提升神经网络重建几何细节的能力，王鹏帅博士提出了对偶八叉树的图神经网络（SIGGRAPH 2022），极大提高了三维形状生成的效率和质量。随着 Transformer 被广泛应用于计算机视觉和 NLP 领域，王鹏帅博士最近提出了基于八叉树的点云Transformer (OctFormer, SIGGRAPH 2023)，相比于以前的点云 Transformer ，其在速度和效果方面都取得了当前学术界的最佳性能。
此外，王鹏帅博士还在数字几何处理方面的做出了优秀的研究工作，包括基于学习的三角网格网格去噪（SIGGRAPH Asia 2016），交互式几何特征编辑（SIGGRAPH Asia 2015）， 以及基于图神经网络进行测地距离计算的工作（GeGNN，SIGGRAPH Asia 2023）。
王鹏帅博士还积极服务于图形学领域，担任著名的图形学国际会议（如Eurographics 2024、CVM 2023 & 2024等）的会议程序委员，以及计算机图形学和计算机视觉会议、期刊的审稿人（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：