With the advancement of computer vision, dynamic 3D reconstruction techniques have seen significant progress and found applications in various fields. However, these techniques generate large amounts of 3D data sequences, necessitating efficient storage and transmission methods. Existing 3D model compression methods primarily focus on static models and do not consider inter-frame information, limiting their ability to reduce data size. Temporal mesh compression, which has received less attention, often requires all input meshes to have the same topology, a condition rarely met in real-world applications. This research proposes a method to compress mesh sequences with arbitrary topology using temporal correspondence and mesh deformation. The method establishes temporal correspondence between consecutive frames, applies a deformation model to transform the mesh from one frame to subsequent frames, and replaces the original meshes with deformed ones if the quality meets a tolerance threshold. Extensive experiments demonstrate that this method can achieve state-of-the-art performance in terms of compression performance. The contributions of this paper include a geometry and motion-based model for establishing temporal correspondence between meshes, a mesh quality assessment for temporal mesh sequences, an entropy-based encoding and corner table-based method for compressing mesh sequences, and extensive experiments showing the effectiveness of the proposed method. All the code will be open-sourced at https://github.com/lszhuhaichao/ultron.
Chair for Computer Aided Medical Procedures (CAMP) Technical University of Munich（计算机辅助医疗程序研究所（CAMP）慕尼黑技术大学）；Robotics Institute Carnegie Mellon University（卡内基梅隆大学机器人研究所）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
HexaShrink, an exact scalable framework for hexahedral meshes with attributes and discontinuities: multiresolution rendering and storage of geoscience models
University Paris-Est（巴黎东大学）；ESIEE Paris（巴黎高等电子工程师学院）；INRIA（法国国家信息与自动化研究所）；ENS（巴黎高等师范学院）；PSL Research University（巴黎文理研究大学）；HoloMake；IFP Energies nouvelles（法国新能源与原子能委员会石油与新能源研究所）；Université Côte d’Azur（蔚蓝海岸大学）；CNRS（法国国家科学研究中心）
University of Liverpool（利物浦大学）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Universidad Antonio de Nebrija（安东尼奥·德·内布里哈大学）；ARIES Research Centre（ARIES研究中心）
Neural Geometry Image-Based Representations with Optimal Transport (OT)
Futurewei Technologies（未来智科）；Stony Brook University（石溪大学）；University of Southern California（南加州大学）；Massachusetts Institute of Technology（麻省理工学院）；George Mason University（乔治·玛莎大学）
