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PU-DZMS:通过密集变焦编码器和多尺度补充回归进行点云采样
Shucong Li1, Zhenyu Liu1, Tianlei Wang2
1School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China.
Journal of imaging
|August 27, 2025
概括
这项研究引入了PU-DZMS,一种新的点云采样方法. 它有效地增强了几何细节,并通过集成密集变焦编码器和多尺度补充回归来减少稀疏区域.
科学领域:
- 计算机视觉
- 三维几何处理
- 机器学习
背景情况:
- 在成像中点云稀疏导致关键几何细节的丢失.
- 现有的点云采样网络难以理解局部-全球特征,导致轮扭曲和稀疏区域.
研究的目的:
- 解决当前点云采样技术的局限性.
- 提出一种新的方法,即PU-DZMS,用于增强点云密度和细节恢复.
主要方法:
- 拟议的PU-DZMS方法包括两个关键组件:密集变焦编码器 (DENZE) 和多尺度补充回归 (MSCR) 模块.
- DENZE使用具有密集连接的ZOOM块和变压器机制来捕获局部-全球几何特征,澄清点云边缘.
- 通过交叉尺度剩余学习,MSCR扩展特征并回归密集点云,确保几何连续性并减少局部稀疏性.
主要成果:
- 对PU-GAN和PU-Net数据集的实验结果证明了PU-DZMS的有效性.
- 这种方法成功地增强了几何细节,并减少了点云中的稀疏区域.
- 在点云采样任务中,PU-DZMS表现出强的表现.
结论:
- PU-DZMS有效地克服了现有方法的局部 - 全球关系理解点云采样的局限性.
- 拟议的架构澄清了几何边缘,并减少了局部稀疏区域,从而提高了点云质量.
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