一个使用通用多级条件编码的多功能点云压缩机 - - 第一部分:几何学
IEEE transactions on pattern analysis and machine intelligence
|September 17, 2024
概括
独角兽是一个新的点云压缩框架,使用多尺度稀疏张量器高效地压缩几何和属性. 这种基于学习的解决方案为各种点云提供了最先进的压缩.
科学领域:
- 计算机视觉 计算机视觉
- 数据压缩数据压缩
- 3D数据处理 3D数据处理
背景情况:
- 点云数据在3D应用中越来越普遍.
- 现有的压缩方法面临着不同点云特性和实时性能的挑战.
- 有效的压缩对于3D数据的存储,传输和染至关重要.
研究的目的:
- 引入"独角兽",一个通用的多尺度条件编码框架,用于点云压缩.
- 在统一的方法中解决几何和属性压缩.
- 与现有方法相比,实现更高的压缩效率.
主要方法:
- 为voxelized点云框架构建多尺度稀疏张量.
- 从当前和参考框架中利用较低规模的先验来进行预测性重建.
- 采用基于学习的方法来实现多功能压缩 (无损/无损).
主要成果:
- 独角兽显著优于符合标准 (MPEG G-PCC,V-PCC) 和基于学习的解决方案.
- 在各种点云类型中实现了最先进的压缩效率.
- 证明了适合实际应用的负担得起的复杂性.
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.
- 该框架的多级条件编码方法可以实现卓越的性能.
- 独角兽代表了3D数据压缩技术的重大进步.
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