对点云几何压缩的自回归和残余指数卷积模型
Gerald Baulig1,2, Jiun-In Guo1,2,3
1College of Electrical & Computer Engineering, National Yang Ming Chiao Tung University, No. 1001, University Road, East District, Hsinchu City 300, Taiwan.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
这项研究使用自行回归方法来提高点云压缩,以估计voxel占用率. 这种新的方法提高了压缩性能和效率,超过了现有的深度学习模型.
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:
- 由于缺乏自回归方法,现有的点云压缩方法难以实现最先进的性能.
- 以前的压缩模型显示出有希望的结果,但需要进一步优化以获得具有竞争力的结果.
研究的目的:
- 引入混合点云压缩方法,采用自动回归策略.
- 通过提高 voxel 占用率估计来提高压缩效率和性能.
- 为点云数据压缩建立一个新的下界比特率.
主要方法:
- 一种混合方法,结合了八个节点和voxel占用率估计.
- 实现二进制算术范围编码器用于比特率计算.
- 自动回归分组方法的适应用于代的voxel候选估计.
- 通过蒸层和轻量级的1D卷积重构骨架构.
- 优化交叉以分析因果关系,并取代传统的卷积技术.
主要成果:
- 拟议的模型在时间和内存消耗方面都取得了显著的改善.
- 与最近基于多个数据集的深度学习方法相比,实现了优越的压缩性能.
- 成功地取代了计算上昂贵的声音卷积和注意力机制.
结论:
- 开发的自回归点云压缩方法提供了最先进的性能.
- 混合方法为点云数据压缩提供了更高效和有效的解决方案.
- 这项工作为基于深度学习的点云压缩设定了新的基准.
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