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QVR3D: Query-Guided Visual Reasoning for Training-Free 3D Grounding — arXiv:2611.01084

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01084
License (as archived): CC0-1.0
Archived: 2026-11-03T11:40:51.884Z
SHA-256 of the archived text: 4d2a6d1f6af9d6060c774d8b82aa194de76d6f2c38c9909fc7436350e4585cab
Stable URL of this page: https://metraipolis.com/documento/dc_84eb574bbf13

How to cite

QVR3D: Query-Guided Visual Reasoning for Training-Free 3D Grounding — arXiv:2611.01084 — arXiv (en), https://arxiv.org/abs/2611.01084 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_84eb574bbf13 (Metraipolis archive dc_84eb574bbf13, sha256 4d2a6d1f6af9d6060c774d8b82aa194de76d6f2c38c9909fc7436350e4585cab)

BibTeX

@misc{dc84eb574bbf13,
  title = {QVR3D: Query-Guided Visual Reasoning for Training-Free 3D Grounding — arXiv:2611.01084},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01084},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_84eb574bbf13; sha256 4d2a6d1f6af9d6060c774d8b82aa194de76d6f2c38c9909fc7436350e4585cab},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01084. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 this https URL .

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2611.01084 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T11:06:37.465Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).