Multimodal registration across 3D point clouds and CT-volumes

Abstract

Multimodal registration is a challenging problem in visual computing, commonly faced during medical image-guided interventions, data fusion and 3D object retrieval. The main challenge of multimodal registration is finding accurate correspondence between modalities, since different modalities do not exhibit the same characteristics. This paper explores how the coherence of different modalities can be utilized for the challenging task of 3D multimodal registration. A novel deep learning multimodal registration framework is proposed by introducing a siamese deep learning architecture, especially designed for aligning and fusing modalities of different structural and physical principles. The cross-modal attention blocks lead the network to establish correspondences between features of different modalities. The proposed framework focuses on the alignment of 3D point clouds and the micro-CT 3D volumes of the same object. A multimodal dataset consisting of real micro-CT scans and their synthetically generated 3D models (point clouds) is presented and utilized for evaluating our methodology.