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arXiv 2403.16539cs.CV

数据高效的3D视觉定位:基于顺序感知指代

Data-Efficient 3D Visual Grounding via Order-Aware Referring

National Taiwan University(台湾大学);NVIDIA(英伟达)

Tung-Yu Wu, Sheng-Yu Huang, Yu-Chiang Frank Wang

AI总结:

本文提出Vigor框架,利用大语言模型生成指代顺序,通过堆叠指代模块逐步定位3D目标,无需锚定物体监督,并在低资源场景下显著提升定位准确率。

摘要:

3D视觉定位旨在识别自然语言描述所指的3D点云场景中的目标物体。以往的工作通常需要大量与点颜色及其描述相关的数据,以利用复杂的语言-视觉关系。在我们的工作中,我们提出了Vigor,一种新颖的基于顺序感知指代的数据高效3D视觉定位框架。Vigor利用大语言模型从输入描述中生成理想的指代顺序,用于3D视觉定位。通过所提出的堆叠式物体指代模块,按上述顺序预测的锚定物体能够逐步定位目标物体,而无需对锚定物体的身份或锚定/目标物体之间的精确关系进行监督。此外,我们提出了一种顺序感知的预热训练策略,该策略通过增强指代顺序来预训练视觉定位框架。这使我们能够更好地捕捉复杂的语言-视觉关系,并有利于理想的数据高效学习方案。在NR3D和ScanRefer数据集上的实验结果表明,我们在低资源场景下具有优越性。特别地,在NR3D数据集上,Vigor在1%数据和10%数据设置下分别比当前最先进的框架高出9.3%和7.6%的定位准确率。我们的代码公开于https://github.com/tony10101105/Vigor。

英文摘要:

3D visual grounding aims to identify the target object within a 3D point cloud scene referred to by a natural language description. Previous works usually require significant data relating to point color and their descriptions to exploit the corresponding complicated verbo-visual relations. In our work, we introduce Vigor, a novel Data-Efficient 3D Visual Grounding framework via Order-aware Referring. Vigor leverages LLM to produce a desirable referential order from the input description for 3D visual grounding. With the proposed stacked object-referring blocks, the predicted anchor objects in the above order allow one to locate the target object progressively without supervision on the identities of anchor objects or exact relations between anchor/target objects. In addition, we present an order-aware warm-up training strategy, which augments referential orders for pre-training the visual grounding framework. This allows us to better capture the complex verbo-visual relations and benefit the desirable data-efficient learning scheme. Experimental results on the NR3D and ScanRefer datasets demonstrate our superiority in low-resource scenarios. In particular, Vigor surpasses current state-of-the-art frameworks by 9.3% and 7.6% grounding accuracy under 1% data and 10% data settings on the NR3D dataset, respectively. Our code is publicly available at https://github.com/tony10101105/Vigor.

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。

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