QVR3D: Query-Guided Visual Reasoning for Training-Free 3D Grounding
Jung-Hao Kao, Jing-En Huang, Jia-Wei Liao, Tzuhsuan Huang, Yu-Lun Liu, Cheng-Fu Chou, Jun-Cheng Chen
Abstract
3D visual grounding localizes objects in 3D scenes from natural language descriptions, requiring reasoning over semantics, spatial relationships, and multi-view observations. Training-free approaches avoid costly annotations but often rely on explicit scene transformation or scene-level frame selection, which may waste limited visual capacity on query-irrelevant regions. We observe that effective grounding should prioritize observations that provide evidence for the query rather than uniformly represent the entire scene. Based on this insight, we introduce QVR3D, a query-guided visual reasoning framework that operates on RGB-D observations using off-the-shelf foundation models. QVR3D combines Query-Guided Maximum Coverage (QGMC) for selecting complementary query-relevant views with Grounding Probability Estimation (GPE) that integrates instance confidence, attribute consistency, and relationship consistency. The framework supports both single- and multi-object grounding without task-specific adaptation. Experiments on ScanRefer and Nr3D achieve state-of-the-art zero-shot performance, while QGMC also improves frame efficiency across multiple 3D vision-language tasks. Our code is available at https://github.com/jwliao1209/QVR3D.