使用通用多级条件编码的多功能点云压缩机 - - 第二部分:属性
IEEE transactions on pattern analysis and machine intelligence
|September 17, 2024
概括
一个名为"独角兽"的新框架有效地压缩了点云几何和使用多尺度稀疏张量器的属性. 这种基于学习的解决方案为静态和动态点云提供了优越的压缩效率,无论是在无损和有损模式下.
科学领域:
- 计算机视觉 计算机视觉
- 数据压缩数据压缩
- 机器学习 机器学习
背景情况:
- 点云数据需要高效的压缩来进行存储和传输.
- 像MPEG G-PCC和V-PCC这样的现有方法在压缩效率方面存在局限性.
- 在点云中的属性压缩由于数据特征的变化而带来了独特的挑战.
研究的目的:
- 为点云几何学和属性压缩提出一个名为"独角兽"的通用多级条件编码框架.
- 为静态和动态点云开发一种多功能,基于学习的解决方案.
- 在无损和有损模式中实现最先进的压缩效率.
主要方法:
- 为voxelized点云属性框架构建多尺度稀疏张量.
- 在较低规模的重建和当前规模数据之间处理属性残留.
- 利用较低规模的空间先验和时间参考框架进行条件剩余预测和改进.
主要成果:
- 独角兽显著优于符合标准的方法 (MPEG G-PCC,V-PCC) 和其他基于学习的解决方案.
- 为各种点云类型实现了最先进的压缩效率.
- 证明了可负担得起的编码和解码运行时间.
结论:
- 独角兽为点云压缩提供了一种多功能且高效的解决方案.
- 该框架的基于学习的方法很好地适应了多样化的点云数据.
- 独角兽为压缩效率在现场设定了新的基准.
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