DPCN++:用于多功能姿势注册的可差分相位相关联网络
IEEE transactions on pattern analysis and machine intelligence
|September 20, 2023
概括
我们介绍了DPCN++,这是一种使用光谱域分析进行无初始化姿势注册的新方法. 这种方法有效地处理同质和异质的测量,优于计算机视觉和机器人的现有方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 三维重建的3D重建
背景情况:
- 姿势注册对于视觉和机器人的任务至关重要.
- 现有的方法通常需要初始化或预定义的对应.
- 处理异质测量是一个重大挑战.
研究的目的:
- 开发一个无初始化姿势注册方法,可达到7个自由度 (7DoF).
- 为了应对记录同质和异质测量的挑战.
- 改进现有的基于学习和经典姿势记录技术.
主要方法:
- 提议DPCN++,一个与特征提取网络相结合的可分化解决器.
- 在光谱域中利用相位相关性进行无对应和无初始化注册.
- 采用富里埃变换和球形辐射聚合用于翻译和尺度不变频谱表示.
- 独立估计频谱中的旋转,尺度和转换.
- 训练整个管道端到端.
主要成果:
- DPCN++在看不见的对象上表现出强大的概括性.
- 该方法成功地对均质和异质输入进行了注册.
- 在各种任务和模式中表现优于经典和基于学习的基线.
- 在部分观测和异质测量上实现卓越的性能.
结论:
- DPCN++提供了一个强大而高效的解决方案,用于无初始化姿势注册.
- 谱域方法有效地解开了旋转,尺度和转换.
- 该方法显示了各种应用的巨大潜力,包括2D图像,3D数据和医学成像.
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