Handling deformation is one of the biggest challenges associated with point cloud registration. When deformation happens due to the motion of an animated object which actively changes its location and general shape, registration of two instances of the same object turns out to be a challenging task. The focus of this work is to address the problem by leveraging the complementary attributes of local and global geometric structures of the point clouds. We define an energy function which consists of local and global terms, as well as a semi-local term to model the intermediate level geometry of the point cloud. The local energy estimates the transformation parameters at the lowest level by assuming a reduced deformation model. The parameters are estimated in a closed form solution, which are then used to assign the initial probability of a stochastic model working at the intermediate level. The global energy term estimates the overall transformation parameters by minimizing a nonlinear least square function via Gauss-Newton optimization framework. The total energy is optimized in a block coordinate descent fashion, updating one term at a time while keeping others constant. Experiments on three publicly available datasets show that the method performs significantly better than several state-of-the-art algorithms in registering pairwise point cloud data.

中文翻译：

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可变形点云配准的多级优化


处理变形是与点云配准相关的最大挑战之一。当由于动画对象的运动主动改变其位置和总体形状而发生变形时，注册同一对象的两个实例是一项具有挑战性的任务。这项工作的重点是通过利用点云的局部和全局几何结构的互补属性来解决该问题。我们定义了一个由局部项和全局项组成的能量函数，以及一个半局部项来对点云的中间层几何进行建模。局部能量通过假设减少的变形模型来估计最低级别的变换参数。参数以封闭形式解进行估计，然后用于分配在中间级别工作的随机模型的初始概率。全局能量项通过高斯-牛顿优化框架最小化非线性最小二乘函数来估计总体变换参数。总能量以块坐标下降的方式进行优化，一次更新一项，同时保持其他项不变。对三个公开可用数据集的实验表明，该方法在配准成对点云数据方面的性能明显优于几种最先进的算法。