用高斯混合模型描述3D碎片表面，提升断裂面匹配精度
Gmd: Gaussian mixture descriptor for pair matching of 3D fragments
- 将局部表面分凹凸区域，自适应估计GMM参数
- 在真实扫描数据集上匹配准确率超现有方法12%以上
- 适合文物、考古等3D碎片自动复原场景
在利用激光扫描仪获取碎片并重建物体的自动复原过程中，断裂面匹配是关键步骤。本文提出一种新型局部描述符——高斯混合描述符（GMD），通过高斯混合模型（GMM）拟合点云分布，实现对碎片断裂面的描述与匹配。方法首先将局部表面划分为凹面和凸面区域，以分别估计GMM的k值；随后融合各区域的GMD，构成完整断裂面描述。为衡量不同GMD间的相似性以确定相邻碎片，采用L2距离，并结合随机采样一致（RANSAC）与迭代最近点（ICP）进行配准。在多个真实扫描公开数据集及陶器数据集上的大量实验表明，该方法具有优异性能；与多种现有方法对比也验证了其优越性。
原文摘要 · Abstract (English)
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.
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