[Registration] "Add new rigid & non-rigid registration algorithms"

Is your feature request related to a problem? Please describe.

Hello, I am a researcher working on point cloud registration algorithms.

I have noticed that PCL includes some rigid point cloud registration algorithms, such as ICP and GICP. However, these algorithms are not very recent, and the speed and accuracy of them can be further improved. Additionally, it appears that there is no implementation of non-rigid registration algorithms available in the library, which handle point clouds with dynamic deformations.

Context

I am wondering if you have considered adding recent rigid & non-rigid registration algorithms to PCL?

Recently, we have published two works:
[1] Juyong Zhang, Yuxin Yao and Bailin Deng. "Fast and robust iterative closest point." IEEE Transactions on Pattern Analysis and Machine Intelligence 44(7), 2021: 3450-3466.
[2] Yuxin Yao, Bailin Deng, Weiwei Xu and Juyong Zhang. "Fast and robust non-rigid registration using accelerated majorization-minimization." IEEE Transactions on Pattern Analysis and Machine Intelligence 45(8), 2023: 9681-9698.

**Expected behavior **

FRICP[1] can speed up ICP and improve its robustness. In most of the experiments conducted in [1], our accelerated version can reduce the computation time of the ICP algorithm by half, and our robust version can improve the accuracy of the ICP algorithm by approximately an order of magnitude. The algorithm has gained popularity among users of our open-source code.

AMM-NRR[2] is our recent publication on non-rigid registration, and its performance surpasses state-of-the-art methods in terms of both speed and accuracy when we publish our paper. (source code)

Describe the solution you'd like

I would like to know if you are interested in these two algorithms, and whether it would be possible for us to contribute them to the PCL library.

Looking forward to your reply.

Start with CONTRIBUTING.md and PCL's existing ICP and GICP registration implementations. Review the linked FRICP and AMM-NRR source repositories, then clarify whether either algorithm is accepted and define concrete integration and validation criteria; the issue does not currently specify files, tests, or a measurable definition of done.