QGround: Condition-Wise Evidence Aggregation for 3D Grounding with 2D VLMs

Using 2D vision-language models (VLMs) for 3D grounding raises a decision-formulation problem: how should image-level evidence be turned into a 3D object decision? This problem matters as pretrained 2D VLMs provide strong visual-semantic evidence, while 3D grounding requires disambiguating objects in cluttered scenes. Current VLM-based formulations make this conversion holistically: the model either selects one object from a rendered candidate set or assigns one overall match judgment to each candidate. Such decisions obscure the evidence needed for disambiguation, since different candidates may satisfy different subsets of the category, attribute, and relational cues. In this work, we reformulate 3D grounding with 2D VLMs as condition-wise evidence aggregation. The reformulation follows three principles: use natural object-centric images rather than rendered candidate images; read the referring expression condition by condition rather than as one holistic query; and use model preference rather than a hard match output as grounding evidence. We instantiate these principles in QGround, a training-free implementation of this decision formulation. Experiments on ScanRefer and Nr3D show improved grounding performance, with analyses demonstrating stronger candidate disambiguation and more interpretable decisions.