A Systematic Approach for Cross-Source Point Cloud Registration by Preserving Macro and Micro Structures

Abstract

Abstract-We propose a systematic approach for registering cross-source point clouds. The compelling need for cross-source point cloud registration is motivated by the rapid development of a variety of 3D sensing techniques, but many existing registration methods face critical challenges as a result of the large variations in cross-source point clouds. This paper therefore illustrates a novel registration method which successfully aligns two crosssource point clouds in the presence of significant missing data, large variations in point density, scale difference and so on. The robustness of the method is attributed to the extraction of macro and micro structures. Our work has three main contributions: (1) a systematic pipeline to deal with cross-source point cloud registration; (2) a graph construction method to maintain macro and micro structures; (3) a new graph matching method is proposed which considers the global geometric constraint to robustly register these variable graphs. Compared to most of the related methods, the experiments show that the proposed method successfully registers in cross-source datasets, while other methods have difficulty achieving satisfactory results. The proposed method also shows great ability in same-source datasets.

Correlated Multimodal Imaging in Life Sciences: Expanding the Biomedical Horizon

“…The method to find the matching and compute the transformation can be done with two main paradigms: transforming the image data in localizations with potential additional features using point-based registration ( [89,91] for the AutoFINDER part of ec-clem) or shape-based registration, potentially with intensity-based machine learning approaches [99]. Note that a plethora of variants exists for point-cloud registration, some of them sounding particularly promising for feature-based multimodal registration [100]. Interesting approaches mixed both feature-based and full registration by restraining the learning data set to registered features [101].…”

Fine-Grained Action Recognition by Motion Saliency and Mid-Level Patches

“…In order to effectively capture the spatial context information during the action process, a graph structure is used to represent the relationship between mid-level patches [53,54]. Firstly, all mid-level patches are constructed into an undirected action graph G = (V, E), where the node i ∈ V represents a mid-level patch, and the edge (i, j) ∈ E represents the relationship between two patches.…”

Fast Registration for Cross-Source Point Clouds by using Weak Regional Affinity and Pixel-Wise Refinement

“…Initially, we introduce line 1 of Algorithm 1. Following [1], we use supervoxel segmentation method [8] to segment the point clouds and use the central points of these segments as the salient structures. Triplet point selections: Inspired by [9], we randomly select triangles satisfying wide baseline strategy and use the three nodes of these triangles as triplet points.…”

“…All the comparison experiments are executed on an I5 CPU, 8GB memory computer. We select ICP [10], GO-ICP [11], Super-4PCS [9], CPD [12], JR-MPC [13] and CSGM [1] as the comparison methods. Most of the existed state-of-the-art registration methods are focus on same-source data and designed to solve SE(3) transformation.…”

“…However, fusion of cross-source point clouds are very challenging because they contain mixture of various differences, such as different density, noise, outliers, viewpoint changing and missing data. The detail of the differences is explained in [1] [2]. In this paper, a registration algorithm is proposed to deal with cross-source point cloud fusion problem by considering weak There are several attempts to solve the cross-source point cloud registration.…”