3D点云属性压缩与 -Laplacian嵌入图表学习字典学习字典
IEEE transactions on pattern analysis and machine intelligence
|October 30, 2023
概括
本研究介绍了一种新的图形字典学习方法,用于3D点云压缩,其性能优于现有的技术. 新的框架通过利用几何结构有效地压缩了详细的3D数据.
科学领域:
- 计算机视觉 计算机视觉
- 数据压缩数据压缩
- 几何深度学习 几何深度学习
背景情况:
- 三维点云提供了丰富的场景信息,但也带来了重大的压缩挑战.
- 现有的压缩方法很难有效地利用不规则的信号统计和高阶几何结构.
研究的目的:
- 开发一个先进的3D点云属性压缩框架.
- 解决利用复杂的几何数据结构的当前方法的局限性.
主要方法:
- 提出了一个新的p-Laplacian嵌入图形字典学习框架.
- 制定了一个非凸的最小化问题与p-Laplacian嵌入规范化.
- 采用了使用ADMM的交替优化范式,以获得高效的解决方案.
主要成果:
- 实现了卓越的M术语近似和点云属性压缩性能.
- 超越了最先进的基于转换的方法和MPEG G-PCC参考软件.
- 证明有效地利用可变信号统计和高阶几何结构.
结论:
- 拟议的p-Laplacian嵌入图形字典学习框架是第一个用于点云压缩的类型.
- 集成的分层压缩方案有效地利用3D点云相关性.
- 这种方法为高效的3D点云数据处理提供了显著的进步.
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