Storage and transmission challenges in dynamic 3D scene representation based on the i3DV platform, With increasing scene complexity, the explosive growth of 3D Gaussian data volume causes excessive storage space occupancy. To address this issue, we propose adopting the AVS PCRM reference software for efficient compression of Gaussian point cloud geometry data. The strategy deeply integrates the advanced encoding capabilities of AVS PCRM into the i3DV platform, forming technical complementarity with the original rate-distortion optimization mechanism based on binary hash tables. On one hand, the hash table efficiently caches inter-frame Gaussian point transformation relationships, which allows for high-fidelity transmission within a 40 Mbps bandwidth constraint. On the other hand, AVS PCRM performs precise compression on geometry data. Experimental results demonstrate that the joint framework maintains the advantages of fast rendering and high-quality synthesis in 3D Gaussian technology while achieving significant 10\%-25\% bitrate savings on universal test sets. It provides a superior rate-distortion tradeoff solution for the storage, transmission, and interaction of 3D volumetric video.
SizeGS: Size-aware Compression of 3D Gaussian Splatting via Mixed Integer Programming
SIGS, Tsinghua University（清华大学SIGS实验室）；Harbin Institute of Technology（哈尔滨工业大学）；MMLab, The Chinese University of Hong Kong（香港中文大学MMLab）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Jiangxing Intelligence Inc.（江行智能有限公司）
MesonGS++: Post-training Compression of 3D Gaussian Splatting with Hyperparameter Searching
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；The Hong Kong University of Science and Technology（香港科技大学）；Harbin Institute of Technology（哈尔滨工业大学）；The Chinese University of Hong Kong（香港中文大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Shenzhen Technology University（深圳技术大学）；Xiamen University（厦门大学）；Simon Fraser University（西蒙弗雷泽大学）；Jiangxing Intelligence Inc.（江兴智能有限公司）
GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splatting
The Hong Kong University of Science and Technology（香港科技大学）；SenseTime Research（商汤科技研究院）；Beijing Institute of Technology（北京理工大学）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；The Chinese University of Hong Kong（香港中文大学）；Shanghai Jiaotong University（上海交通大学）
School of Electrical Engineering and Automation, Hefei University of Technology, and Engineering Technology Research Center of Industrial Automation Anhui Province, Hefei, Anhui 230009, China（合肥工业大学电气与自动化学院，安徽省工业自动化工程技术研究中心）；The University of Electro-Communications, Tokyo, Japan（东京电讯大学）；School of Computer Science and Technology, University of Science and Technology of China, Hefei, China（中国科学技术大学计算机科学与技术学院）；Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark（哥本哈根大学电子与计算机工程系）；Department of Computer Science and Electrical Engineering, University of Missouri–Kansas City, Kansas, USA（密苏里大学- Kansas 城市分校计算机科学与电气工程系）；School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China（合肥工业大学计算机科学与信息工程学院）
Deep AutoEncoder-based Lossy Geometry Compression for Point Clouds
Peking University Shenzhen Graduate School（北京大学深圳研究生院）；Tencent America（腾讯美国）；Advanced Institute of Information Technology, Peking University（北京大学先进信息研究院）
CAGS: Color-Adaptive Volumetric Video Streaming with Dynamic 3D Gaussian Splatting
Simon Fraser University（西蒙弗雷泽大学）；Jiangxing Intelligence Inc.（江行智能有限公司）；McGill University（麦吉尔大学）；Nankai University（南开大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Fuzhou University（福州大学）；Southeast University（东南大学）
