通过分割改进了基于视频的点云压缩
Faranak Tohidi1, Manoranjan Paul1, Anwaar Ulhaq2
1School of Computing Mathematics and Engineering, Charles Sturt University, Bathurst, NSW 2795, Australia.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
本研究引入了一种压缩动态3D点云的新方法,提高数据效率和视觉质量. 该方法通过专注于形状相似性和遮来增强点云压缩,以更好地生成补丁.
科学领域:
- 计算机视觉 计算机视觉
- 3D数据处理 3D数据处理
- 数据压缩数据压缩
背景情况:
- 点云对于虚拟现实和增强现实等领域的3D表示是必不可少的.
- 压缩大型动态点云数据对于高效的传输和存储至关重要.
- 目前基于视频的点云压缩 (V-PCC) 方法涉及计算密集的步骤,可能导致信息丢失和文物.
研究的目的:
- 开发一种用于动态点云压缩的改进方法.
- 为了减少数据量,同时保持几何和纹理信息.
- 与现有标准相比,最大限度地减少文物并增强用户的感知.
主要方法:
- 基于形状相似性和封闭性的动态点云的新型细分策略.
- 在细分后生成补丁,以更好地保存点近距离和原始数据.
- 在补丁生成之前利用密度和阻塞特性进行增强的细分.
主要成果:
- 拟议的方法在速率扭曲性能方面明显优于V-PCC标准.
- 实验结果显示,对几何和纹理数据的主观质量测试优越.
- 与V-PCC相比,新方法保留了更多的原始点和近距离信息.
结论:
- 提出的基于细分的方法提供了一种更有效的方式来压缩动态点云.
- 该方法通过维护数据完整性来解决当前V-PCC标准的局限性.
- 这些发现表明,3D点云压缩技术取得了重大进展.
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