灵活加权的Chamfer距离:用于点云完成的增强目标函数
概括
灵活加权的Chamfer距离 (FCD) 通过平衡本地细节和全球结构来改善点云完成. 这种新的方法提高了点云质量和空间完整性,优于标准方法.
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:
- 在点云完成中,标准孔距离 (CD) 受到对称权重的影响,导致像点聚合这样的结构缺陷.
- 这种限制阻碍了生成高质量,结构健全的点云.
研究的目的:
- 引入灵活加权的Chamfer距离 (FCD) 作为点云完成的改进的目标函数.
- 将CD解为本地精度和全球完整性,优先考虑全球结构完整性.
主要方法:
- 开发了具有不对称权重策略的FCD,以解决CD的局限性.
- 在最先进的网络中集成FCD作为插电模块.
- 在各种数据集上进行了广泛的实验,包括ShapeNet55,PCN,KITTI,ABC和PU-GAN.
主要成果:
- FCD显著提高了全球分布指标,同时保持了本地精度.
- 在ShapeNet55上减少了12.4%,在PCN数据集上减少了密度感知孔距离 (DCD) 和地球移动器距离 (EMD).
- 与标准CD相比,表现出优越的全球统一性和结构完整性.
结论:
- FCD有效地减轻了点聚类,并改善了点云生成中的空间结构.
- 拟议的方法在各种任务和数据集中显示出优异的概括性.
- FCD提供了一种多功能和有效的目标功能,用于推进点云生成.
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