Ambiguity-Aware Part Annotation Refinement via Ontology Constraints

Part annotation of 3D objects is important for part-level analysis, but local geometric evidence may not determine a unique semantic label. We propose an ontology-based annotation refinement framework that represents multiple semantic interpretations and filters candidates using structural and spatial constraints. For each point, a semantic interpretation set is constructed from labels observed in its local neighborhood, and ontology constraints yield a feasible subset without introducing new labels or forcing a single interpretation. We consider boundary ambiguity, part-definition ambiguity, and label-set incompleteness and evaluate the framework on the Airplane, Knife, and Skateboard categories of ShapeNet Part. Since feasible sets are subsets of semantic sets by construction, reductions in Ambiguity Size (AS) and Multi-label Ratio (MLR) are inherent properties of filtering, not independent evidence of improved annotation accuracy. The macro-average Ontology Filtering Rate (OFR) is 0.0947. Point-level analysis distinguishes unchanged candidate sets, partial filtering, and empty feasible sets, characterizing how the adopted ontology constraints affect candidate feasibility. The framework supports ambiguity-aware assessment of existing part annotations while preserving feasible alternatives, without claiming to resolve semantic ambiguity or recover ground-truth labels.