Accurate Registration of Cross-Modality Geometry via Consistent Clustering

The registration of unitary-modality geometric data has been successfully explored over past decades. However, existing approaches typically struggle to handle cross-modality data due to the
intrinsic difference
between different models. To address this problem, in this article, we formulate the cross-modality registration problem as a
consistent clustering process
. First, we study the structure similarity between different modalities based on an
adaptive
fuzzy shape clustering, from which a coarse alignment is successfully operated. Then, we optimize the result using fuzzy clustering consistently, in which the source and target models are formulated as
clustering memberships
and
centroids
, respectively. This optimization casts new insight into point set registration, and substantially improves the robustness against outliers. Additionally, we investigate the effect of
fuzzier
in fuzzy clustering on the cross-modality registration problem, from which we theoretically prove that the classical Iterative Closest Point (ICP) algorithm is a special case of our newly defined objective function. Comprehensive experiments and analysis are conducted on both synthetic and real-world cross-modality datasets. Qualitative and quantitative results demonstrate that our method outperforms state-of-the-art approaches with higher accuracy and robustness. Our code is publicly available at
https://github.com/zikai1/CrossModReg
.