# An Effective Approach for Point Clouds Registration Based on the Hard and Soft Assignments

## TL;DR

This paper introduces a novel point cloud registration method that combines hard and soft assignment strategies to improve accuracy and robustness, especially with low overlap data.

## Contribution

It proposes a new registration approach integrating hard and soft assignments into an ICP variant for better handling partial overlaps.

## Key findings

- Achieves superior accuracy over previous methods.
- Demonstrates robustness with low-overlap point clouds.
- Validated on public datasets with positive results.

## Abstract

For the registration of partially overlapping point clouds, this paper proposes an effective approach based on both the hard and soft assignments. Given two initially posed clouds, it firstly establishes the forward correspondence for each point in the data shape and calculates the value of binary variable, which can indicate whether this point correspondence is located in the overlapping areas or not. Then, it establishes the bilateral correspondence and computes bidirectional distances for each point in the overlapping areas. Based on the ratio of bidirectional distances, the exponential function is selected and utilized to calculate the probability value, which can indicate the reliability of the point correspondence. Subsequently, both the values of hard and soft assignments are embedded into the proposed objective function for registration of partially overlapping point clouds and a novel variant of ICP algorithm is proposed to obtain the optimal rigid transformation. The proposed approach can achieve good registration of point clouds, even when their overlap percentage is low. Experimental results tested on public data sets illustrate its superiority over previous approaches on accuracy and robustness.