arXiv:2610.34040v1 Announce Type: new Abstract: Construction robots must label building elements they can only partly see. We show that when occlusion at test time removes whole regions of a scene, rather than parts of an object as in training, point-cloud segmentation networks do not simply miss the hidden elements: they relabel them, and the errors collapse onto a single sink class. With fixed synthetic layouts the column absorbs up to 70% of all test points; with randomized layouts the sink moves to the stair, predicted at 4.5-5.4x its true share across three training seeds. Baseline mIoU falls to 0.15-0.27 (fixed) and 0.36-0.72 (randomized), and up to 269 whole objects vanish in a single test set. Appending 14 hand-computed geometric features per point (GPE) restores mIoU to 0.54-0.92 and removes 64-100% of whole-object losses in every run, for PointNet++ and a PTv3-style transformer in both layout regimes; on real S3DIS scans it removes every whole-wall loss (10 to 0). In an as-built column check computed from the predictions, GPE raises the share of correctly checked views from 17-58% to 71-92%. Feature permutation on fixed, randomized and real scenes explains the pattern: each class relies on whichever cue is stable in its training data, position for height-banded slabs and beams, intrinsic shape for walls and stairs once their position varies. Occlusion augmentation is a strong alternative on mIoU, but in every setting it loses more whole objects than GPE (e.g., 25-35 vs. 0).