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

• URL: http://arxiv.org/abs/2601.09601v1
• Date: Wed, 14 Jan 2026 16:16:51 GMT
• Title: Iterative Differential Entropy Minimization (IDEM) method for fine rigid pairwise 3D Point Cloud Registration: A Focus on the Metric
• Authors: Emmanuele Barberi, Felice Sfravara, Filippo Cucinotta,
• Abstract summary: Authors introduce a novel differential entropy-based metric for fine rigid pairwise 3D point cloud registration.<n>The proposed metric proves effective even with density differences, noise, holes, and partial overlap, where RMSE does not always yield optimal alignment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Fully-Geometric Cross-Attention for Point Cloud Registration [51.865371511201765]
  Point cloud registration approaches often fail when the overlap between point clouds is low due to noisy point correspondences.<n>This work introduces a novel cross-attention mechanism tailored for Transformer-based architectures that tackles this problem.<n>We integrate the Gromov-Wasserstein distance into the cross-attention formulation to jointly compute distances between points across different point clouds.<n>At the point level, we also devise a self-attention mechanism that aggregates the local geometric structure information into point features for fine matching.
  arXiv  Detail & Related papers  (2025-02-12T10:44:36Z)
• Efficient Point Clouds Upsampling via Flow Matching [16.948354780275388]
  Existing diffusion models struggle with inefficiencies as they map Gaussian noise to real point clouds.<n>We propose PUFM, a flow matching approach to directly map sparse point clouds to their high-fidelity dense counterparts.<n>Our method delivers superior upsampling quality but with fewer sampling steps.
  arXiv  Detail & Related papers  (2025-01-25T17:50:53Z)
• A Consistency-Aware Spot-Guided Transformer for Versatile and Hierarchical Point Cloud Registration [9.609585217048664]
  We develop a consistency-aware spot-guided Transformer (CAST) CAST incorporates a spot-guided cross-attention module to avoid interfering with irrelevant areas. A lightweight fine matching module for both sparse keypoints and dense features can estimate the transformation accurately.
  arXiv  Detail & Related papers  (2024-10-14T08:48:25Z)
• BiEquiFormer: Bi-Equivariant Representations for Global Point Cloud Registration [28.75341781515012]
  The goal of this paper is to address the problem of global point cloud registration (PCR) i.e., finding the optimal alignment between point clouds. We show that state-of-the-art deep learning methods suffer from huge performance degradation when the point clouds are arbitrarily placed in space.
  arXiv  Detail & Related papers  (2024-07-11T17:58:10Z)
• SPARE: Symmetrized Point-to-Plane Distance for Robust Non-Rigid 3D Registration [77.13381026159111]
  We propose SPARE, a novel formulation that utilizes a symmetrized point-to-plane distance for robust non-rigid registration.<n>The proposed method greatly improves the accuracy of non-rigid registration problems and maintains relatively high solution efficiency.
  arXiv  Detail & Related papers  (2024-05-30T15:55:04Z)
• Multiway Point Cloud Mosaicking with Diffusion and Global Optimization [74.3802812773891]
  We introduce a novel framework for multiway point cloud mosaicking (named Wednesday) At the core of our approach is ODIN, a learned pairwise registration algorithm that identifies overlaps and refines attention scores. Tested on four diverse, large-scale datasets, our method state-of-the-art pairwise and rotation registration results by a large margin on all benchmarks.
  arXiv  Detail & Related papers  (2024-03-30T17:29:13Z)
• Unleash the Potential of 3D Point Cloud Modeling with A Calibrated Local Geometry-driven Distance Metric [62.365983810610985]
  We propose a novel distance metric called Calibrated Local Geometry Distance (CLGD) CLGD computes the difference between the underlying 3D surfaces calibrated and induced by a set of reference points. As a generic metric, CLGD has the potential to advance 3D point cloud modeling.
  arXiv  Detail & Related papers  (2023-06-01T11:16:20Z)
• Learning to Register Unbalanced Point Pairs [10.369750912567714]
  Recent 3D registration methods can effectively handle large-scale or partially overlapping point pairs. We present a novel 3D registration method, called UPPNet, for the unbalanced point pairs.
  arXiv  Detail & Related papers  (2022-07-09T08:03:59Z)
• Shape-invariant 3D Adversarial Point Clouds [111.72163188681807]
  Adversary and invisibility are two fundamental but conflict characters of adversarial perturbations. Previous adversarial attacks on 3D point cloud recognition have often been criticized for their noticeable point outliers. We propose a novel Point-Cloud Sensitivity Map to boost both the efficiency and imperceptibility of point perturbations.
  arXiv  Detail & Related papers  (2022-03-08T12:21:35Z)
• RPM-Net: Robust Point Matching using Learned Features [79.52112840465558]
  RPM-Net is a less sensitive and more robust deep learning-based approach for rigid point cloud registration. Unlike some existing methods, our RPM-Net handles missing correspondences and point clouds with partial visibility.
  arXiv  Detail & Related papers  (2020-03-30T13:45:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.