ViGiL3D：面向三维视觉定位的语言多样性数据集
ViGiL3D: A Linguistically Diverse Dataset for 3D Visual Grounding
AI总结：
本文提出ViGiL3D，一个语言多样性的三维视觉定位诊断数据集，并引入语言学分析框架，评估现有开放词汇方法在分布外提示上的不足，以推动实际应用。
摘要：
三维视觉定位（3DVG）涉及定位三维场景中由自然语言文本所指称的实体。此类模型对于具身人工智能和场景检索应用非常有用，这些应用涉及使用自然语言描述来搜索对象或模式。虽然近期工作集中于基于大语言模型（LLM）的三维视觉定位数据集扩展，但这些数据集并未涵盖英语中可能指定的全部潜在提示范围。为确保我们针对有用且具有代表性的提示集进行扩展和测试，我们提出一个用于对三维视觉定位提示进行语言学分析的框架，并引入具有三维多样化语言的视觉定位（ViGiL3D），这是一个用于针对多样化语言模式评估视觉定位方法的诊断数据集。我们评估了现有的开放词汇三维视觉定位方法，以证明这些方法在理解和识别更具挑战性的分布外提示目标方面尚不熟练，从而向实际应用迈进。
英文摘要：
3D visual grounding (3DVG) involves localizing entities in a 3D scene referred to by natural language text. Such models are useful for embodied AI and scene retrieval applications, which involve searching for objects or patterns using natural language descriptions. While recent works have focused on LLM-based scaling of 3DVG datasets, these datasets do not capture the full range of potential prompts which could be specified in the English language. To ensure that we are scaling up and testing against a useful and representative set of prompts, we propose a framework for linguistically analyzing 3DVG prompts and introduce Visual Grounding with Diverse Language in 3D (ViGiL3D), a diagnostic dataset for evaluating visual grounding methods against a diverse set of language patterns. We evaluate existing open-vocabulary 3DVG methods to demonstrate that these methods are not yet proficient in understanding and identifying the targets of more challenging, out-of-distribution prompts, toward real-world applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
