隐式神经压缩点云的点云
概括
本研究介绍了NeRC3,一种使用神经网络压缩3D点云数据的新方法. 它实现了比当前标准更好的静态和动态点云的压缩.
科学领域:
- 计算机视觉 计算机视觉
- 3D数据处理 3D数据处理
- 机器学习 机器学习
背景情况:
- 点云对于3D表示至关重要,但难以压缩.
- 现有的压缩方法在高精度,非结构化的点云数据方面扎.
研究的目的:
- 开发一种使用隐式神经表示 (INR) 的新型点云压缩框架.
- 为了高效地编码密集点云的几何和属性.
- 为了扩展动态点云压缩的方法.
主要方法:
- 利用两个基于坐标的神经网络来实现隐式几何和属性表示.
- 用于voxel化点云的voxel占用和属性映射.
- 为动态点云开发了一个4D时空表示 (4D-NeRC3).
主要成果:
- 对于静态点云,NeRC3的性能优于基于八树的G-PCC和其他INR方法.
- 4D-NeRC3显示了与G-PCC和V-PCC相比,对于动态点云的优越几何压缩.
- 该方法证明了竞争性的关节几何和属性压缩性能.
结论:
- NeRC3为静态点云压缩提供了一种有效的方法.
- 4D-NeRC3为动态点云压缩提供了最先进的解决方案.
- 隐式神经表示显示了高级点云数据处理的巨大潜力.
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