3D visual grounding is a critical skill for household robots, enabling them
to navigate, manipulate objects, and answer questions based on their
environment. While existing approaches often rely on extensive labeled data or
exhibit limitations in handling complex language queries, we propose
LLM-Grounder, a novel zero-shot, open-vocabulary, Large Language Model
(LLM)-based 3D visual grounding pipeline. LLM-Grounder utilizes an LLM to
decompose complex natural language queries into semantic constituents and
employs a visual grounding tool, such as OpenScene or LERF, to identify objects
in a 3D scene. The LLM then evaluates the spatial and commonsense relations
among the proposed objects to make a final grounding decision. Our method does
not require any labeled training data and can generalize to novel 3D scenes and
arbitrary text queries. We evaluate LLM-Grounder on the ScanRefer benchmark and
demonstrate state-of-the-art zero-shot grounding accuracy. Our findings
indicate that LLMs significantly improve the grounding capability, especially
for complex language queries, making LLM-Grounder an effective approach for 3D
vision-language tasks in robotics. Videos and interactive demos can be found on
the project website this https URL .

LLM-Grounder 是一种零样本、开放词汇量的基于大型语言模型（LLM）的 3D 视觉定位流水线。通过将复杂自然语言查询拆解为语义元素，并利用可视化定位工具识别 3D 场景中的对象，LLM-Grounder 评估所提议对象之间的空间和常识关系，从而做出最终的定位决策。该方法不需要有标签的培训数据，可应用于新型 3D 场景和任意文本查询，显示出最先进的零样本定位准确性。研究结果表明，LLM 显著提高了定位能力，尤其对于复杂语言查询，在机器人的 3D 视觉语言任务中，LLM-Grounder 是一种有效的方法。