The automation of Scan-to-BIM remains a major challenge within the Architecture, Engineering, and Construction industry, particularly in the detection and geometric characterisation of architectural openings such as doors and windows. Although recent advances in 3D semantic segmentation have improved the classification of architectural elements, the effect of segmentation accuracy on downstream geometric detection and reconstruction is still under study. This work compares five state-of-the-art deep learning models, PointNeXt, PointMetaBase, Point Transformer V1, Point Transformer V3, and Swin3D, on opening detection in Scan-to-BIM. A unified evaluation framework integrating DBSCAN clustering with axis-aligned bounding box fitting is introduced to generate per-instance geometric representations. The models are assessed using semantic metrics and geometric reliability indicators, including centroid error, dimensional deviation and 3D IoU. Experiments on the S3DIS Area 5 dataset, reveal notable performance differences across models. Swin3D achieved the highest door detection rate of 96.9%, followed by PointMetaBase at 92.9%, PointNeXt at 87.4%, PTV3 at 85.0%, and PTV1 at 81.9%. Window detection proved more challenging, with Swin3D and PTV3 both achieving 75.0%, PTV1 at 71.2%, and PointNeXt and PointMetaBase at 67.3%. Notably, PointMetaBase produced strong geometric accuracy for doors despite lower semantic scores. These results suggest that high segmentation accuracy does not always lead to precise geometric reconstruction. To assess generalisation, the trained models were applied to 11 Matterport3D rooms, confirming that the observed patterns extend across different scanning environments. This study concludes that in Scan-to-BIM workflows, greater emphasis should be placed on geometric reconstruction algorithms than segmentation performance alone.