Abstract
We present a method to compute the descriptor of components of point clouds, therefore, a novel component-oriented partial matching of point clouds is achieved based on the component descriptor. We observe that 3D components can be constructed by stacking 2D shapes using certain criteria so that the centers of the 2D shapes form a curve called a skeletal curve that is the trajectory of the 2D shapes. In addition, the scaling factors of the 2D shapes also impact the shape of the 3D components. Motivated by these observations, the computation of the component descriptor that is termed 2to3SSC (from 2D to 3D: 2D Shape and Skeletal Curve) is formulated as a 2D shape and skeletal curve extraction problem, and the component-oriented partial matching of the point clouds is based on the dissimilarity measure of 2to3SSCs of the components. Furthermore, for the 2D shape matching, which is crucial to the matching of the components, we present a novel 2D shape descriptor called VDTL (Vertical Distances to the Tangent Line). The proposed method outperforms previously proposed methods because it simultaneously encodes the local and global features of the components as opposed to only encoding the local or partial features as in previous studies. Finally, the effectiveness and performance of 2to3SSCs are compared with those of state-of-the-art feature description and matching methods for different point cloud datasets. Further, the benefits and the applicability of the proposed method are demonstrated; favorable results are obtained for real-world point clouds of Terracotta fragments.
Highlights
Feature extraction, description, and matching are the core and prerequisite of most point cloud processing techniques, such as point cloud registration [1], line drawings generated from point clouds [2], 3D object retrieval [3], 3D object partitioning [4], and 3D object reconstruction [5].It is not surprising that numerous studies have reported on techniques for addressing the problem of feature extraction, description, and matching of point clouds [3], [6]
Since 2D shape matching is a part of component matching, the details of the similarity errors are discussed in the component matching results
Are given in Table 6, where ω1 measures the Dist(Si ∈ Component #1, Sj ∈ Component #2) for 2D shape matching, ω2 measures the ratio for non-matching characters to the total characters in the OLAPs [5] for skeletal curve matching, ω3 measures the Dist(lC1, lC2 ) for scaling factor matching, and ω4 measures the percentage ratio for matching the 2D shapes to the total number of 2D shapes because the presence of holes may increase the similarity errors between the 2D shapes to greater than ω1 and ω3 respectively
Summary
Description, and matching are the core and prerequisite of most point cloud processing techniques, such as point cloud registration [1], line drawings generated from point clouds [2], 3D object retrieval [3], 3D object partitioning [4], and 3D object reconstruction [5]. Geometric operators at a fine scale (referred to as micro-features in this paper) concentrate on extracting small structures and capturing details that describe the gradients of the point clouds, as shown in Fig. (b). Geometric operators at the meso-scale (referred to as meso-features in this paper) describe the partial shape of a 3D model from a specific viewing angle The goal of these approaches is to extract local representations of point clouds rather than describing the local gradients of the point clouds [6]. The approaches for the meso-feature description and their matching via feature descriptors to find true point correspondences typically consist of continuously rotating the point clouds and projecting the local surfaces onto a 2D plane; subsequently, the feature description is performed by generating feature descriptors from a series of 2D image patches. The computation of the descriptor is conceptually simple and easy to implement; (ii) we propose a new 2D shape descriptor VDTL (Vertical Distances to the Tangent Line) which plays an important role in component matching; (iii) from a practical aspect, this method represents a novel component-oriented partial matching of point clouds with wide applicability
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