姜蔚蔚，清华大学电子工程系学士和博士，加州大学伯克利分校访问学者，北京邮电大学讲师，主要研究领域为人工智能与无线通信、空天地一体化网络、车联网等。在IEEE Trans等高水平期刊中发表了30多篇SCI和EI索引论文，包括3篇ESI高被引论文和2篇ESI热点论文；谷歌学术引用超过2300次，入选斯坦福大学2023年度“全球前2%顶尖科学家榜单”；作为项目骨干参与国家重大研究计划项目1项，面上和重点项目2项，国家电网公司总部科技项目1项等。担任Future Generation Computer Systems、Big Data Mining and Analytics、计算机工程等中英文期刊的青年编委，Information Fusion、Neural Computing and Applications等英文期刊的客座编辑。获得中国发明协会2023年度发明创业奖创新奖获奖项目一等奖，获得SCI期刊清华大学学报（英文版）2020优秀论文奖等学术荣誉。

报告二

题目：SPU-PMD: Self-Supervised Point Cloud Upsampling via Progressive Mesh Deformation

报告人：Yushi Li

时间：2024年8月8日(周四)，上午10：30-11：00

会议地点：双创5楼会议室

报告摘要：

Despite the success of recent upsampling approaches, generating high-resolution point sets with uniform distribution and meticulous structures is still challenging. Unlike existing methods that only take spatial information of the raw data into account, we regard point cloud upsampling as generating dense point clouds from deformable topology. Motivated by this, we present SPU-PMD, a self-supervised topological mesh deformation network, for 3D densification. As a cascaded framework, our architecture is formulated by a series of coarse mesh interpolator and mesh deformers. At each stage, the mesh interpolator first produces the initial dense point clouds via mesh interpolation, which allows the model to perceive the primitive topology better. Meanwhile, the deformer infers the morphing by estimating the movements of mesh nodes and reconstructs the descriptive topology structure. By associating mesh deformation with feature expansion, this module progressively refines point clouds' surface uniformity and structural details. To demonstrate the effectiveness of the proposed method, extensive quantitative and qualitative experiments are conducted on synthetic and real-scanned 3D data. This talk gives the introduction about this innovative point cloud upsampling framework.

报告人简介：

Yushi Li received the Ph.D. from The Hong Kong Polytechnic University, in 2021. He is currently an Assistant Professor at the Department of Intelligent Science, Xi'an Jiaotong-Liverpool University. His main research interests include computer vision, computer graphics, graph learning, and multimodal-based learning. He has published over 30 papers in several prestigious journals and conferences such as IEEE TIP, IEEE TVCG, IEEE TNNLS, IEEE TCE, CVPR, and WACV. He serves as the reviewer of TVCG, TNNLS, CAD/CG, Computers & Graphics, WWW, ECAI, ICME, ICPR, CSCWD and ChinaVis. He also serves as the co-chair or PC member of several conferences like WWW 2024 (Industry track), ECAI 2024, UIC 2024, and ICVR 2023.

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