【摘要】 为解决多视角配准中带有低频非刚性形变的深度数据容易产生累积误差、重叠区域未对齐等问题,提出一种基于多薄板样条的多视角非刚性配准算法.首先通过局部迭代最近点刚性配准算法得到重叠视角深度数据之间的对应点;然后基于多薄板样条的全局优化能量公式为每个视角求解一个薄板样条变换,使所有对应点之间距离的平方和最小;最后将优化后的薄板样条变换应用于每个视角的深度数据.通过在优化模型中引入初始点位置约束,该算法能使配准后的数据尽可能保持初始形状.为了加快求解速度,迭代地求解每个薄板样条变换,并且在优化过程中增量式地引入径向基函数.实验室结果表明,文中算法有较高的精度和效率,能够有效地减少累积误差并且提升重叠区域的对齐效果.更多还原

【Abstract】 Registration of multi-view range data containing low-frequency warps is prone to accumulation errors and misalignments of overlapping regions. To this end, a multi-view non-rigid registration algorithm is proposed based on the multiple thin-plate splines. Firstly, the correspondences between each pair of overlapping views are established via a local iterative closest point(ICP) rigid registration algorithm. Then, using the global energy formula in terms of the multiple thin-plate splines, a thin-plate spline transformation is computed for each view by minimizing the sum of squared distances between the corresponding points. Finally, the optimized thin-plate spline transformations are applied to all of views. By introducing the initial point positions of each view as constraints, the registered range data can keep their original shapes as much as possible. To accelerate the solution of optimization, each thin-plate spline is iteratively solved and the radial basis functions are progressively added in the optimization process. Experimental results showed that the proposed algorithm is accurate and efficient, and can effectively eliminate the accumulation errors and improve the alignments of overlapping regions.更多还原