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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| Published in: | Computer graphics forum Vol. 44; no. 1 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Oxford
Blackwell Publishing Ltd
01.02.2025
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| ISSN: | 0167-7055, 1467-8659 |
| Online Access: | Get full text |
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| Abstract | 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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| AbstractList | 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. 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. 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. |
| Author | Moustakas, Konstantinos Dvořák, Jan Arvanitis, Gerasimos Podgorelec, David Váša, Libor Hácha, Filip |
| Author_xml | – sequence: 1 givenname: Jan orcidid: 0000-0003-4569-1151 surname: Dvořák fullname: Dvořák, Jan email: jdvorak@kiv.zcu.cz organization: University of West Bohemia in Pilsen, Faculty of Applied Sciences – sequence: 2 givenname: Filip orcidid: 0000-0001-8956-6411 surname: Hácha fullname: Hácha, Filip email: hachaf@kiv.zcu.cz organization: University of West Bohemia in Pilsen, Faculty of Applied Sciences – sequence: 3 givenname: Gerasimos orcidid: 0000-0001-8149-5188 surname: Arvanitis fullname: Arvanitis, Gerasimos email: arvanitis@ece.upatras.gr organization: University of Patras – sequence: 4 givenname: David orcidid: 0000-0002-0701-9201 surname: Podgorelec fullname: Podgorelec, David email: david.podgorelec@um.si organization: University of Maribor – sequence: 5 givenname: Konstantinos orcidid: 0000-0001-7617-227X surname: Moustakas fullname: Moustakas, Konstantinos email: moustakas@upatras.gr organization: University of Patras – sequence: 6 givenname: Libor orcidid: 0000-0002-0213-3769 surname: Váša fullname: Váša, Libor email: lvasa@kiv.zcu.cz organization: University of West Bohemia in Pilsen, Faculty of Applied Sciences |
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| Title | Survey of Inter‐Prediction Methods for Time‐Varying Mesh Compression |
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