Select, Compete, Refine: Training-Free 3D Visual Grounding from RGB-D Views

Training-free 3D visual grounding from calibrated RGB-D views enables language-conditioned object localization without task-specific model training or preconstructed scene point clouds. However, selecting the object that satisfies a full referring expression remains ambiguous, and geometry reconstructed from partial masks and depth can yield incomplete object boxes. We propose Select, Compete, Refine (SCR), a framework that combines clause-scoped object selection and verification with multi-view boundary recovery using frozen pretrained models. Select organizes entity-level options and clause-bound references. Compete separates challenger discovery under withheld candidate evidence from replacement verification after evidence restoration. Refine recovers observed but underrepresented extent through adaptive per-view bounds and face-wise multi-view support, without additional segmentation or vision-language model calls. We evaluate SCR on SeqAlign3DVG, EmbodiedScan and ScanRefer. On SeqAlign3DVG, SCR achieves 62.96% [email protected] and 38.07% [email protected], improving over our VLM-Grounder reproduction using the same Qwen model by 11.38 and 12.45 percentage points, respectively. Ablation studies further support the benefits of object selection, verification, and boundary recovery.