Abstract Registration is a ubiquitous operation in Visual Computing, with applications in 3D object retrieval among others. Registration is the process of overlaying two or more datasets taken from different viewpoints, at different times or by different sensors into a common reference frame. Multimodal registration is a special case where the data to be matched do not belong to the same modality and is challenging due to the diverse nature of the modalities involved which makes the creation of a distance function harder. Due to the large number of possible modality combinations and application fields, a considerable number of multimodal registration techniques have been proposed in diverse fields, including medicine and archaeology. This survey aims to unify 3D multimodal registration techniques (i.e. where at least one of the modalities is in 3D) across application domains, with the hope of providing an application-independent view and the potential for cross-fertilization. The problem of 3D multimodal registration is explicitly defined and the various methods are systematically categorized and described in terms of a number of important properties. Methods with publicly available source code have been compared on common datasets. A discussion on trends, observations and challenges for further research concludes the review.

中文翻译：

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多模态 3D 配准方法的独立应用审查

摘要配准是视觉计算中普遍存在的操作，在 3D 对象检索等方面有应用。配准是将从不同视点、不同时间或不同传感器获取的两个或多个数据集叠加到公共参考系中的过程。多模态配准是一种特殊情况，其中要匹配的数据不属于同一模态，并且由于所涉及的模态的多样性使得距离函数的创建更加困难，因此具有挑战性。由于存在大量可能的模态组合和应用领域，在包括医学和考古学在内的不同领域提出了相当多的多模态配准技术。本次调查旨在统一 3D 多模态配准技术（即 其中至少一种模式在 3D 中）跨应用程序域，希望提供独立于应用程序的视图和交叉施肥的潜力。明确定义了 3D 多模态配准的问题，并根据许多重要属性系统地分类和描述了各种方法。已经在常见数据集上比较了具有公开源代码的方法。对进一步研究的趋势、观察和挑战的讨论结束了审查。已经在常见数据集上比较了具有公开源代码的方法。对进一步研究的趋势、观察和挑战的讨论结束了审查。已经在常见数据集上比较了具有公开源代码的方法。对进一步研究的趋势、观察和挑战的讨论结束了审查。