# An Elastic Fine-Tuning Dual Recurrent Framework for Non-Rigid Point Cloud Registration

## TL;DR

This paper introduces a new unsupervised method for aligning 3D point clouds using a dual recurrent framework that achieves high accuracy without needing labeled data.

## Contribution

The novel elastic fine-tuning dual recurrent framework enables unsupervised non-rigid registration with minimal labeling.

## Key findings

- The method achieves state-of-the-art performance with an EMD of 0.01219 in non-rigid scenes.
- It also shows strong results with an RMSE of 0.0153 in rigid scenes.
- The framework uses loss functions to maintain rigidity while allowing controlled deformations.

## Abstract

Non-rigid transformation is based on rigid transformation by adding distortions to form a more complex but more consistent common scene. Many advanced non-rigid alignment models are implemented using supervised learning; however, the large number of labels required for the training process makes their application difficult. Here, an elastic fine-tuning dual recurrent computation for unsupervised non-rigid registration is proposed. At first, we transform a non-rigid transformation into a series of combinations of rigid transformations using an outer recurrent computational network. Then, the inner loop layer computes elastic-controlled rigid incremental transformations by controlling the threshold to obtain a finely coherent rigid transformation. Finally, we design and implement loss functions that constrain deformations and keep transformations as rigid as possible. Extensive experiments validate that the proposed method achieves state-of-the-art performance with 0.01219 earth mover’s distances (EMDs) and 0.0153 root mean square error (RMSE) in non-rigid and rigid scenes, respectively.