文本引导的3D视觉定位综述：要素、近期进展与未来方向
A Survey on Text-guided 3D Visual Grounding: Elements, Recent Advances, and Future Directions
AI总结：
本文首次系统综述文本引导的3D视觉定位任务，涵盖流程要素、方法分类、数据集与评估指标，并指出未来研究方向。
摘要：
文本引导的3D视觉定位（T-3DVG）旨在从复杂的3D场景中定位与语言查询语义对应的特定对象，在过去几年中引起了3D研究界的日益关注。与2D视觉定位相比，该任务因其更贴近现实世界以及数据收集和3D点云源处理的复杂性而展现出巨大的潜力和挑战。在本综述中，我们试图全面概述T-3DVG的进展，包括其基本要素、近期研究进展和未来研究方向。据我们所知，这是首个关于T-3DVG任务的系统性综述。具体而言，我们首先以教程风格提供T-3DVG流程的总体结构及详细组件，呈现完整的背景概述。然后，我们将现有的T-3DVG方法归纳为不同类别，并分析其优缺点。我们还介绍了基准数据集和评估指标以评估其性能。最后，我们讨论了现有T-3DVG的潜在局限性，并对几个有前景的研究方向分享了一些见解。最新论文持续收集于https://github.com/liudaizong/Awesome-3D-Visual-Grounding。
英文摘要：
Text-guided 3D visual grounding (T-3DVG), which aims to locate a specific object that semantically corresponds to a language query from a complicated 3D scene, has drawn increasing attention in the 3D research community over the past few years. Compared to 2D visual grounding, this task presents great potential and challenges due to its closer proximity to the real world and the complexity of data collection and 3D point cloud source processing. In this survey, we attempt to provide a comprehensive overview of the T-3DVG progress, including its fundamental elements, recent research advances, and future research directions. To the best of our knowledge, this is the first systematic survey on the T-3DVG task. Specifically, we first provide a general structure of the T-3DVG pipeline with detailed components in a tutorial style, presenting a complete background overview. Then, we summarize the existing T-3DVG approaches into different categories and analyze their strengths and weaknesses. We also present the benchmark datasets and evaluation metrics to assess their performances. Finally, we discuss the potential limitations of existing T-3DVG and share some insights on several promising research directions. The latest papers are continually collected at https://github.com/liudaizong/Awesome-3D-Visual-Grounding.
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。
