Gmd: Gaussian mixture descriptor for pair matching of 3D fragments

TL;DR

This paper introduces a Gaussian Mixture Descriptor (GMD) for matching fractured surfaces in 3D fragment reassembly, improving accuracy through regional GMM fitting and similarity measurement.

Contribution

The novel GMD method uses regional GMM fitting and similarity metrics to enhance 3D surface matching in fragment reassembly tasks.

Findings

GMD outperforms existing methods on public datasets.

The regional GMM approach improves surface description accuracy.

Experiments validate the effectiveness of GMD in real-world scenarios.

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 L2 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.