Iterative Differential Entropy Minimization (IDEM) method for fine rigid pairwise 3D Point Cloud Registration: A Focus on the Metric

TL;DR

The paper proposes a novel differential entropy-based metric for fine rigid pairwise 3D point cloud registration that is robust to common issues like noise, density differences, and partial overlaps, outperforming traditional metrics.

Contribution

Introduction of IDEM, a differential entropy-based objective function that does not require a fixed point cloud and improves registration robustness under challenging conditions.

Findings

IDEM effectively handles noise, density variations, and partial overlaps.

Compared to RMSE, Chamfer, and Hausdorff distances, IDEM provides more accurate alignments.

Experimental results demonstrate IDEM's superior performance in diverse scenarios.

Abstract

Point cloud registration is a central theme in computer vision, with alignment algorithms continuously improving for greater robustness. Commonly used methods evaluate Euclidean distances between point clouds and minimize an objective function, such as Root Mean Square Error (RMSE). However, these approaches are most effective when the point clouds are well-prealigned and issues such as differences in density, noise, holes, and limited overlap can compromise the results. Traditional methods, such as Iterative Closest Point (ICP), require choosing one point cloud as fixed, since Euclidean distances lack commutativity. When only one point cloud has issues, adjustments can be made, but in real scenarios, both point clouds may be affected, often necessitating preprocessing. The authors introduce a novel differential entropy-based metric, designed to serve as the objective function within an optimization framework for fine rigid pairwise 3D point cloud registration, denoted as Iterative Differential Entropy Minimization (IDEM). This metric does not depend on the choice of a fixed point cloud and, during transformations, reveals a clear minimum corresponding to the best alignment. Multiple case studies are conducted, and the results are compared with those obtained using RMSE, Chamfer distance, and Hausdorff distance. The proposed metric proves effective even with density differences, noise, holes, and partial overlap, where RMSE does not always yield optimal alignment.