Registration is a ubiquitous operation in visual computing and constitutes an important pre-processing step for operations such as 3D object reconstruction, retrieval and recognition. Particularly in cultural heritage (CH) applications, registration techniques are essential for the digitization and restoration pipelines. Cross-time registration is a special case where the objects to be registered are instances of the same object after undergoing processes such as erosion or restoration. Traditional registration techniques are inadequate to address this problem with the required high accuracy for detecting minute changes; some are extremely slow. A deep learning registration framework for cross-time registration is proposed which uses the DeepGMR network in combination with a novel down-sampling scheme for cross-time registration. A dataset especially designed for cross-time registration is presented (called ECHO) and an extensive evaluation of state-of-the-art methods is conducted for the challenging case of cross-time registration.

配准是视觉计算中无处不在的操作，是3D对象重建、检索和识别等操作的重要预处理步骤。特别是在文化遗产 (CH) 应用中，注册技术对于数字化和修复管道至关重要。跨时间注册是一种特殊情况，即要注册的对象是经过侵蚀或恢复等过程后同一对象的实例。传统的配准技术不足以通过检测微小变化所需的高精度来解决这个问题。有些非常慢。提出了一种用于跨时间配准的深度学习配准框架，该框架使用 DeepGMR 网络结合新颖的下采样方案进行跨时间配准。