We present a new scale-adaptive ICP (Iterative Closest Point) method which aligns two objects that differ by rigid transformations (translations and rotations) and uniform scaling. The motivation is that input data may come in different scales (measurement units) which may not be known a priori, or when two range scans of the same object are obtained by different scanners. Classical ICP and its many variants do not handle this scale difference problem adequately. Our novel solution outperforms three different methods that estimate scale prior to alignment and a fourth method that, similar to ours, jointly optimizes for scale during the alignment.

我们提出了一种新的尺度自适应 ICP（迭代最近点）方法，该方法对齐两个因刚性变换（平移和旋转）和均匀缩放而不同的对象。动机是输入数据可能来自不同的尺度（测量单位），这可能是先验未知的，或者当同一对象的两次距离扫描由不同的扫描仪获得时。经典 ICP 及其许多变体不能充分处理这种尺度差异问题。我们的新解决方案优于在对齐之前估计比例的三种不同方法，以及与我们类似的第四种方法，在对齐期间联合优化比例。