Survey of Inter‐Prediction Methods for Time‐Varying Mesh Compression

Time‐varying meshes (TVMs), that is mesh sequences with varying connectivity, are a greatly versatile representation of shapes evolving in time, as they allow a surface topology to change or details to appear or disappear at any time during the sequence. This, however, comes at the cost of large sto...

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Bibliographic Details
Published in:Computer graphics forum Vol. 44; no. 1
Main Authors: Dvořák, Jan, Hácha, Filip, Arvanitis, Gerasimos, Podgorelec, David, Moustakas, Konstantinos, Váša, Libor
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.02.2025
Subjects:
compression algorithms
data compression
modelling
polygonal mesh reduction
Topology
ISSN:0167-7055, 1467-8659
Online Access:Get full text
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Summary:Time‐varying meshes (TVMs), that is mesh sequences with varying connectivity, are a greatly versatile representation of shapes evolving in time, as they allow a surface topology to change or details to appear or disappear at any time during the sequence. This, however, comes at the cost of large storage size. Since 2003, there have been attempts to compress such data efficiently. While the problem may seem trivial at first sight, considering the strong temporal coherence of shapes represented by the individual frames, it turns out that the varying connectivity and the absence of implicit correspondence information that stems from it makes it rather difficult to exploit the redundancies present in the data. Therefore, efficient and general TVM compression is still considered an open problem. We describe and categorize existing approaches while pointing out the current challenges in the field and hint at some related techniques that might be helpful in addressing them. We also provide an overview of the reported performance of the discussed methods and a list of datasets that are publicly available for experiments. Finally, we also discuss potential future trends in the field. This paper surveys existing methods for compressing time‐varying meshes (TVMs), focusing on the challenge of exploiting data redundancies due to varying connectivity and the absence of implicit correspondence information. We review current performance, available datasets, and potential future trends in the field.
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.15278