SOAR: Stochastic Optimization for Affine global point set Registration
SOAR: Stochastic Optimization for Affine global point set Registration
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DOI：
10.2312/vmv.20141282
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发表时间：
2014-10
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通讯作者：
Marco Agus;E. Gobbetti;A. Villanueva;Claudio Mura;R. Pajarola
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作者：
Marco Agus;E. Gobbetti;A. Villanueva;Claudio Mura;R. Pajarola
我们介绍了一个随机算法的成对仿射配准的部分重叠的三维点云未知点对应。该算法恢复全局最优的尺度，旋转和平移对齐参数，并适用于各种困难的设置，包括非常稀疏，嘈杂，和outlierrided数据集，不允许本地描述符的计算。该技术是基于一个随机的方法的全局优化的对准误差函数鲁棒噪声和抗离群值。在每个优化步骤中，它在随机访问当前解决方案景观的广义BSP树表示以选择有希望的转换，使用GPU加速技术找到点对点对应关系以及在BSP树中加入新的错误值之间交替。与以前的工作相比，而不是简单地构建树的引导随机抽样，我们利用问题的结构，通过低成本的本地最小化过程的基础上分析解决绝对方向问题，使用当前的对应关系。我们展示了我们的方法在各种不同尺度，分辨率和噪声特性的大型点集上的质量和性能。
We introduce a stochastic algorithm for pairwise affine registration of partially overlapping 3D point clouds with unknown point correspondences. The algorithm recovers the globally optimal scale, rotation, and translation alignment parameters and is applicable in a variety of difficult settings, including very sparse, noisy, and outlierridden datasets that do not permit the computation of local descriptors. The technique is based on a stochastic approach for the global optimization of an alignment error function robust to noise and resistant to outliers. At each optimization step, it alternates between stochastically visiting a generalized BSP-tree representation of the current solution landscape to select a promising transformation, finding point-to-point correspondences using a GPU-accelerated technique, and incorporating new error values in the BSP tree. In contrast to previous work, instead of simply constructing the tree by guided random sampling, we exploit the problem structure through a low-cost local minimization process based on analytically solving absolute orientation problems using the current correspondences. We demonstrate the quality and performance of our method on a variety of large point sets with different scales, resolutions, and noise characteristics.