Coherent Point Drift Networks：非刚性点集配准的无监督学习
Coherent Point Drift Networks: Unsupervised Learning of Non-Rigid Point Set Registration
摘要：
给定新的源点集和目标点集对，标准点集配准方法通常重复地进行所需几何变换的独立迭代搜索以将源点集与目标点集对准。这限制了它们在处理具有大体积数据集的实时点集配准的应用中的使用。本文提出了一种新的方法，称为相干点漂移网络（CPD-Net），无监督学习的几何变换实时非刚性点集配准。与以前的努力（例如相干点漂移）相比，CPD-Net可以学习位移场函数来估计训练数据集的几何变换，从而预测先前未见过的对的对齐所需的几何变换，而无需任何额外的迭代优化过程。此外，CPD网络利用深度神经网络的能力来拟合任意函数，自适应地适应所需几何变换的不同复杂程度。特别地，CPD网络被证明具有学习连续位移向量函数的理论保证，该连续位移向量函数可以进一步避免像以前的作品那样施加额外的参数平滑约束。我们的实验验证了CPD-Net在各种2D/3D数据集上进行非刚性点集配准的令人印象深刻的性能，即使存在显著的位移噪声，离群值和缺失点。我们的代码将在https://github.com/nyummvc/CPD-Net上提供。
英文摘要：
Given new pairs of source and target point sets, standard point set registration methods often repeatedly conduct the independent iterative search of desired geometric transformation to align the source point set with the target one. This limits their use in applications to handle the real-time point set registration with large volume dataset. This paper presents a novel method, named coherent point drift networks (CPD-Net), for the unsupervised learning of geometric transformation towards real-time non-rigid point set registration. In contrast to previous efforts (e.g. coherent point drift), CPD-Net can learn displacement field function to estimate geometric transformation from a training dataset, consequently, to predict the desired geometric transformation for the alignment of previously unseen pairs without any additional iterative optimization process. Furthermore, CPD-Net leverages the power of deep neural networks to fit an arbitrary function, that adaptively accommodates different levels of complexity of the desired geometric transformation. Particularly, CPD-Net is proved with a theoretical guarantee to learn a continuous displacement vector function that could further avoid imposing additional parametric smoothness constraint as in previous works. Our experiments verify the impressive performance of CPD-Net for non-rigid point set registration on various 2D/3D datasets, even in the presence of significant displacement noise, outliers, and missing points. Our code will be available at https://github.com/nyummvc/CPD-Net.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
