Recent work and challenges behind 3D Gaussian Splatting compression

讲座名称：Recent work and challenges behind 3D Gaussian Splatting compression

讲座人：Enzo Tartaglione 副教授

讲座时间：9月27日9:00-10:15

讲座地点：黄大年茶思屋 （裕隆创新大厦）

讲座人介绍：

Enzo Tartaglione is an Associate Professor at Télécom Paris, where he is responsible for the equipe Multimedia and he is a Hi!Paris chair holder. He is also a Member of the ELLIS Society, Senior IEEE Member, and Associate Editor of IEEE Transactions on Neural Networks and Learning Systems and of the EURASIP journal on image and video processing. He received the MS degree in Electronic Engineering at Politecnico di Torino in 2015, cum laude. The same year, he also received a magna cum laude MS in electrical and computer engineering at University of Illinois at Chicago. In 2016 he was also awarded the MS in Electronics by Politecnico di Milano, cum laude. In 2019 he obtained a PhD in Physics at Politecnico di Torino, cum laude, with the thesis "From Statistical Physics to Algorithms in Deep Neural Systems". In 2024 he received his Habilitation à Diriger des Recherches from the Institut Polytechnique de Paris, and in 2025 the National Qualification for Full Professor in both Computer Science and Computer Science Engineering. His principal interests include compression and responsible (frugal) AI, privacy-aware learning, data debiasing, and regularization for deep learning.

Enzo Tartaglione 现任法国电信巴黎高科（Télécom Paris）副教授，负责多媒体团队（équipe Multimedia），并担任 Hi!Paris 教席。他同时是 ELLIS 学会成员、IEEE 高级会员，以及《IEEE Transactions on Neural Networks and Learning Systems》与《EURASIP Journal on Image and Video Processing》期刊的副主编。他于 2015 年获都灵理工大学（Politecnico di Torino）电子工程硕士学位（优等，cum laude），同年获伊利诺伊大学芝加哥分校（University of Illinois at Chicago）电气与计算机工程硕士学位（最高荣誉，magna cum laude）。2016 年获米兰理工大学（Politecnico di Milano）电子学硕士学位（优等，cum laude）。2019 年在都灵理工大学获物理学博士学位（优等，cum laude），论文题为“From Statistical Physics to Algorithms in Deep Neural Systems”。2024 年获巴黎综合理工学院（Institut Polytechnique de Paris）博士生导师资格（HDR），并于 2025 年获得计算机科学与计算机工程两个领域的法国国家正教授资格（National Qualification for Full Professor）。其主要研究兴趣包括压缩与负责型（节俭型）人工智能、隐私感知学习、数据去偏与深度学习的正则化等。

讲座内容：

3D Gaussian Splatting 能把“高画质 + 实时交互”带到复杂场景，但模型太大；因此核心是如何有效压缩以便规模化、可传输、可部署。

主办单位：通信工程学院