OQ-Atoms: Deferred Semantic Commitment for Open-Vocabulary 3D Segmentation

Open-vocabulary 3D segmentation is fundamental to indoor embodied agents, whose semantic maps are built incrementally as RGB-D observations stream in. However, existing systems collapse multi-view semantic alternatives before the query is known, while learned refiners often over-commit and degrade reliable predictions. We present OQ-Atoms (Open-vocabulary Query-addressable Atoms), a three-stage framework that recasts semantic map updating as a query-time commitment decision. OQ-Atoms groups dense voxel evidence into compact atoms that preserve per-view observation provenance until query time, enabling class scores to be read out against original multi-view support rather than an averaged posterior. A conservative committer accepts an update only when four label-free evidence gates pass, keeping no-update as the default action. With fixed atoms and no per-scene tuning, OQ-Atoms reaches 41.63 mIoU on Replica and 40.13 mIoU on ScanNet. The accuracy gain comes from provenance-weighted readout, while the committer is evaluated separately as a safety rule. Atom construction costs two orders of magnitude less than semantic scoring. We further introduce leave-one-scene-out calibration for label-free semantic committers, showing that same-set gains can be threshold artifacts and that deployment rules should be judged by held-out abstention behavior.