问题描述
我有两个点云，以3d坐标表示.一个是另一个的子集，包含更少的点.它们的规模相同.
I have two point clouds, in 3d coordinates. One is a subset of the other, containing many less points. They are in the same scale.
我需要做的是找到两者之间的平移和旋转.我看过点云库，迭代最近点" 和相干点漂移，但是这些匹配方法似乎都期望这两个点集包含的点基本相同，而没有一个是另一个较小的子集.
What i need to do is find the translation and rotation between the two. I have looked at Point cloud Library, "Iterative closest point", and Coherent Point Drift, but these matching approaches both seem to expect the two point sets to contain mostly the same points, not have one be a smaller, subset of the other.
我可以使用其中任何一项进行调整吗?还是有另一种算法可以将子集点云与集合匹配?
Can i use either of these, with adjustments? Or is there another algorithm to match a subset point cloud to a set?
谢谢.
推荐答案
在无法访问示例数据的情况下，很难为您推荐一种特定的注册算法.
Without having access to sample data, is kind of hard to recommend you a specific registration algorithm.
但是，如今，我对所有新的数据驱动"注册方法感到非常兴奋.
However, I'm pretty exicted nowdays about all the new "data-driven" registration approaches.
根据我的亲身经历，使用最近的这篇论文，我得到了很棒的注册结果:
From my personal experience, I'm having awesome registration results using the approach of this recent paper:
https://arxiv.org/abs/1603.08182
其中提供了源代码:
https://github.com/andyzeng/3dmatch-toolbox
如论文中所述，它优于基于pcl-descriptor的注册方法，我认为它可能适合您的需求.
As reported in the paper, it outperforms pcl-descriptor based registration approaches and I think that it may be suitable for your needs.
这篇关于集对子集点云匹配的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！