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使用3D骨架基变形进行压缩的非刚性动态人类点云序列的时间估计
Jin-Kyum Kim1, Ye-Won Jang1, Sol Lee1
1Electronic Materials Engineering, Kwangwoon University, Kwangwoon-ro 20, Seoul 01897, Republic of Korea.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究介绍了一种新的算法,通过估计非刚性运动来压缩动态3D点云序列 (PCS). 该方法通过利用时间相关性,能够高效地重建3D点云 (PCF),类似于2D视频压缩.
科学领域:
- 计算机视觉 计算机视觉
- 3D 图形 3D 图形
- 数据压缩数据压缩
背景情况:
- 动态3D点云序列 (PCS) 对高效传输和重建提出了挑战.
- 现有的方法经常与人类或可变形物体点云固有的非刚性运动作斗争.
- 3D数据的压缩技术落后于2D图像的压缩技术.
研究的目的:
- 开发一种算法,通过时间运动估计来传输和重建动态3D点云序列 (PCS).
- 通过将2D视频压缩原理适应3D非刚性运动,使3D点云 (PCF) 的有效压缩成为可能.
- 提供一种方法来使用关键PCF,运动向量和剩余点云重建目标PCF.
主要方法:
- 该算法将PCS分为组,选择一个关键PCF,并通过3D姿势估计估计3D骨运动.
- 非刚性点云被转换为网状模型,与3D骨架准,并变形以匹配目标PCF.
- 生成剩余点云,并使用关键PCF,运动向量和剩余PC实现重建.
主要成果:
- 实验结果表明,3D点云序列的成功压缩.
- 拟议的方法有效地估计并利用非刚性3D运动进行压缩.
- 该算法显示了高压缩效率的潜力,当与现有的点云压缩 (PCC) 技术相结合时.
结论:
- 拟议的算法将2D运动估计扩展到3D非刚性物体运动,以有效地压缩点云.
- 它为压缩动态3D点云数据提供了一种可行的方法,特别是对于人类运动.
- 未来与MPEG PCC等标准的整合可能会导致高效的3D数据传输系统.
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