Learning 3D Attachment Rankings from Geometric Proxies

Can geometric supervision teach a model which component an assembled part attaches to? We study parent ranking from a reference-pose mesh with supplied component assignments. C2LT learns from estimated patch-connection labels computed from ShapeNet geometry, without annotated parent relations, and scores pairs of surface patches to rank candidate parents. We evaluate the trained model without fine-tuning on 4,069 queries from 931 PartNet-Mobility objects. The full model reaches 44.74% parent accuracy, compared with 38.49% for a model trained under the same protocol but without cross-patch attention. Its advantage remains when every candidate receives the same number of patch-pair scores. However, when sampling guarantees at least one patch per component and allocates the remaining patches by area, the full model reaches 65.37%, whereas choosing the candidate with the largest whole-component surface area in the supplied mesh reaches 90.17%. Cross-patch attention therefore improves the learned predictor, but this improvement does not surpass a simple geometric rule on the evaluated task.