Gmd: Gaussian mixture descriptor for pair matching of 3D fragments
Abstract
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 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.
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- Gmd: Gaussian mixture descriptor for pair matching of 3D fragments
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Publication History
Published: 28 October 2024
Accepted: 26 September 2024
Received: 13 June 2024
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- Shaanxi Science and Technology Association Youth Talent Support Program
- National Natural Science Foundation of China
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