In this paper, a shape-constrained iterative algorithm is proposed to
register a rigid template point-cloud to a given reference point-cloud. The
algorithm embeds a shape-based similarity constraint into the principle of
gravitation. The shape-constrained gravitation, as induced by the reference,
controls the movement of the template such that at each iteration, the template
better aligns with the reference in terms of shape. This constraint enables the
alignment in difficult conditions indtroduced by change (presence of outliers
and/or missing parts), translation, rotation and scaling. We discuss efficient
implementation techniques with least manual intervention. The registration is
shown to be useful for change detection in the 3D point-cloud. The algorithm is
compared with three state-of-the-art registration approaches. The experiments
are done on both synthetic and real-world data. The proposed algorithm is shown
to perform better in the presence of big rotation, structured and unstructured
outliers and missing data.

提出一种基于形状约束迭代算法，将一个刚体模板点云注册到给定参考点云，从而检测三维点云的变化并适应存在异常值或缺失数据的复杂场景。与三种最新的配准方法进行比较，实验结果表明该算法在大旋转、结构和非结构异常值和缺失数据的情况下表现更佳。