Iterative Differential Entropy Minimization (IDEM) method for fine rigid pairwise 3D Point Cloud Registration: A Focus on the Metric
This work addresses robustness challenges in point cloud registration for computer vision applications, offering a solution that is less sensitive to common data imperfections compared to existing methods.
The authors tackled the problem of fine rigid pairwise 3D point cloud registration by introducing a novel differential entropy-based metric, which effectively handles issues like density differences, noise, holes, and partial overlap, outperforming traditional metrics such as RMSE, Chamfer distance, and Hausdorff distance in multiple case studies.
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.