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This paper introduces a novel Gaussian Mixture Descriptor (GMD) for matching fractured surfaces of 3D fragments in object reconstruction. The method fits a GMM to the point distribution of local surface patches, dividing them into concave and convex regions to estimate the GMM's k value. Experiments on real-scanned and Terracotta datasets demonstrate the effectiveness and advantages of GMD compared to existing methods, using L2 distance for similarity measurement and RANSAC/ICP for alignment.
Achieve more accurate 3D fragment matching for object reconstruction by modeling fractured surface geometry with Gaussian Mixture Models.
In the automatic reassembly of fragments acquired using laser scanners to reconstruct objects, a crucial step is the matching of fractured surfaces. In this paper, we propose a novel local descriptor that uses the Gaussian Mixture Model (GMM) to fit the distribution of points, allowing for the description and matching of fractured surfaces of fragments. Our method involves dividing a local surface patch into concave and convex regions for estimating the k value of GMM. Then the final Gaussian Mixture Descriptor (GMD) of the fractured surface is formed by merging the regional GMDs. To measure the similarities between GMDs for determining adjacent fragments, we employ the L2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_2$$\end{document} distance and align the fragments using Random Sample Consensus (RANSAC) and Iterative Closest Point (ICP). The extensive experiments on real-scanned public datasets and Terracotta datasets demonstrate the effectiveness of our approach; furthermore, the comparisons with several existing methods also validate the advantage of the proposed method.