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Ultron: Enabling Temporal Geometry Compression of 3D Mesh Sequences using Temporal Correspondence and Mesh Deformation

Haichao Zhu

TL;DR

This research proposes a method to compress mesh sequences with arbitrary topology using temporal correspondence and mesh deformation, which can achieve state-of-the-art performance in terms of compression performance.

Abstract

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.

Ultron: Enabling Temporal Geometry Compression of 3D Mesh Sequences using Temporal Correspondence and Mesh Deformation

TL;DR

This research proposes a method to compress mesh sequences with arbitrary topology using temporal correspondence and mesh deformation, which can achieve state-of-the-art performance in terms of compression performance.

Abstract

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.
Paper Structure (16 sections, 8 equations, 2 figures, 3 tables)

This paper contains 16 sections, 8 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Mesh Matching
  4. Non-rigid 3D registration
  5. Mesh Compression
  6. Overview
  7. Method
  8. Mesh tracking
  9. Non-rigid 3D registration
  10. Key frame selection
  11. Mesh compression
  12. Experiments and Results
  13. Dataset and evaluation metrics
  14. Compression performance evaluation
  15. Compression quality evaluation
  16. ...and 1 more sections

Figures (2)

  • Figure 1: These two frames of meshes are from VOCA VOCA2019. We can see the two mesh are from a same person and look similar; however their topology is different because the number of vertices are different and thus the connectives are also different.
  • Figure 2: System Overview