Reliable geometric verification is essential in the construction industry, particularly for large-scale maritime infrastructures where deviations can critically affect functionality and safety. The emerging <em>Scan-vs-BIM</em> approach enables automated quality assessment by comparing as-built point clouds with as-designed BIM models. It allows evaluation of the entire structure, rather than just specific points, but relies heavily on accurate spatial registration. This paper presents an evaluation of several Iterative Closest Point (ICP) variants for fine registration within a <em>Scan-vs-BIM</em> framework dedicated to construction tolerance verification. Three ICP variants are compared in terms of convergence behavior, robustness to noise, and stability using synthetic point clouds derived from maritime structures. The methods are then tested on real datasets, each acquired under different conditions, leading to varying data quality. Based on the results, a hybrid method is proposed to improve registration reliability. The results show that the proposed approach improves the inlier rate by 8–9 % while reducing the mean deviation by approximately 1 cm on the noisiest datasets, compared to the classical <em>point-to-plane</em> ICP.