Think, Act, Build: An Agentic Framework with Vision Language Models for Zero-Shot 3D Visual Grounding
3D Visual Grounding (3D-VG) aims to localize objects in 3D scenes via natural language descriptions. While recent advancements leveraging Vision-Language Models (VLMs) have explored zero-shot possibilities, they typically suffer from a static workflow relying on preprocessed 3D point clouds, essentially degrading grounding into proposal matching. To bypass this reliance, our core motivation is to decouple the task: leveraging 2D VLMs to resolve complex spatial semantics, while relying on deterministic multi-view geometry to instantiate the 3D structure. Driven by this insight, we propose "Think, Act, Build (TAB)", a dynamic agentic framework that reformulates 3D-VG tasks as a generative 2...
| Authors | Haibo Wang, Zihao Lin, Zhiyang Xu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 April 2026 |
| Last updated at source | 2 April 2026 |
Connected in the knowledge graph
- AntonioJun/indoor-3d-bbox-indicesDataset · cited by
- WHB139426/ScannetDataset · cited by
Models and datasets referencing this paper
- WHB139426/ScannetDataset
- AntonioJun/indoor-3d-bbox-indicesDataset
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