The key to effective point cloud compression is to obtain a robust context model consistent with complex 3D data structures. Recently, the advancement of large language models (LLMs) has highlighted their capabilities not only as powerful generators for in-context learning and generation but also as effective compressors. These dual attributes of LLMs make them particularly well-suited to meet the demands of data compression. Therefore, this paper explores the potential of using LLM for compression tasks, focusing on lossless point cloud geometry compression (PCGC) experiments. However, applying LLM directly to PCGC tasks presents some significant challenges, i.e., LLM does not understand the structure of the point cloud well, and it is a difficult task to fill the gap between text and point cloud through text description, especially for large complicated and small shapeless point clouds. To address these problems, we introduce a novel architecture, namely the Large Language Model-based Point Cloud Geometry Compression (LLM-PCGC) method, using LLM to compress point cloud geometry information without any text description or aligning operation. By utilizing different adaptation techniques for cross-modality representation alignment and semantic consistency, including clustering, K-tree, token mapping invariance, and Low Rank Adaptation (LoRA), the proposed method can translate LLM to a compressor/generator for point cloud. To the best of our knowledge, this is the first structure to employ LLM as a compressor for point cloud data. Experiments demonstrate that the LLM-PCGC outperforms the other existing methods significantly, by achieving -40.213% bit rate reduction compared to the reference software of MPEG Geometry-based Point Cloud Compression (G-PCC) standard, and by achieving -2.267% bit rate reduction compared to the state-of-the-art learning-based method.
Denoising Diffusion Probabilistic Model for Point Cloud Compression at Low Bit-Rates
LTCI, Télécom Paris, Institut Polytechnique de Paris（巴黎理工大学LTCI研究所）；University of Turin（都灵大学）；Wangxuan Institute of Computer Technology, Peking University（北京大学王轩计算机技术研究所）；Université Paris-Saclay, CNRS, CentraleSupélec, Laboratoire des Signaux et Systèmes（巴黎萨克雷大学、CNRS、CentraleSupélec信号与系统实验室）
Deep-JGAC: End-to-End Deep Joint Geometry and Attribute Compression for Dense Colored Point Clouds
School of Electronics and Communication Engineering, Shenzhen Campus, Sun Yat-Sen University（中山大学深圳校区电子与通信工程学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Department of Electrical and Computer Engineering, University of Southern California（南加州大学电子与计算机工程系）
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）
3DAC: Learning Attribute Compression for Point Clouds
Sun Yat-sen University（中山大学）；The Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；University of Oxford（牛津大学）；Information Engineering University（信息工程大学）；Shanghai Jiaotong University（上海交通大学）；National University of Defense Technology（国防科技大学）
ECM-OPCC: Efficient Context Model for Octree-based Point Cloud Compression
Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；Department of Automation, Tsinghua University（清华大学自动化系）；Department of Computer Science, Tsinghua University（清华大学计算机科学系）
DALD-PCAC: Density-Adaptive Learning Descriptor for Point Cloud Lossless Attribute Compression
City University of Hong Kong（香港城市大学）；Peking University Shenzhen Graduate School（北京大学深圳研究生院）；University of Missouri-Kansas City（密苏里大学-堪萨斯城分校）；Tencent Media Laboratory（腾讯媒体实验室）
Range Image-Based Implicit Neural Compression for LiDAR Point Clouds
Graduate School of Information Science and Technology（信息科学与技术研究生学校）；The University of Osaka（大阪大学）；Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究实验室（MERL））
Point Cloud Compression and Objective Quality Assessment: A Survey
Cooperative Medianet Innovation Center, Shanghai Jiao Tong University（上海交通大学合作中位网创新中心）；University of Missouri–Kansas City（密苏里大学-堪萨斯城分校）；Department of Electrical and Computer Engineering, University of Canterbury（坎特伯雷大学电子与计算机工程系）
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电子与电气工程系）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）
Rate-distortion Optimized Point Cloud Preprocessing for Geometry-based Point Cloud Compression
School of Telecommunications Engineering, Xidian University（电子科技大学电信工程学院）；School of Electronics and Information, Northwestern Polytechnical University（西北工业大学电子信息技术学院）；School of Engineering, Royal Melbourne Institute of Technology（皇家墨尔本理工学院工程学院）
Rendering-Oriented 3D Point Cloud Attribute Compression using Sparse Tensor-based Transformer
School of Telecommunication Engineering, Xidian University（西安电子科技大学通信工程学院）；City University of Hong Kong（香港城市大学）；School of Electronics and Information, Northwestern Polytechnical University（西北工业大学电子信息学院）；School of Engineering, Royal Melbourne Institute of Technology（皇家墨尔本理工大学工程学院）
