通过引用文本短语,实现精细的3D视觉接地
概括
这项研究引入了3D短语意识接地 (3DPAG) 以在3D场景中更精确地定位对象. 新的语句对象对齐和预训练方法显著提高了3D视觉接地性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 对3D场景的理解对于机器人和增强现实等任务至关重要.
- 当前的3D视觉接地 (3DVG) 方法缺乏细粒度的上下文理解.
- 现有的3DVG方法忽视了描述词和非目标对象之间的关系.
研究的目的:
- 为了引入一个更细致的任务,3D句子意识接地 (3DPAG),增强3D视觉接地.
- 通过识别与短语相关的对象和上下文推理来开发定位目标对象的方法.
- 创建和使用一个全面的数据集,用于精细的句子意识到3D场景的理解.
主要方法:
- 在Nr3D,Sr3D和ScanRefer数据集中的88K句子中的227K句子级注释的手动标签.
- 开发一个独立的平台,用于高效的数据注释.
- 语句对象对齐 (POA) 优化和语句特定预训练 (PSP) 的实施.
主要成果:
- 通过将方法扩展到3DPAG场景,在3DVG中显著提高了性能.
- 最先进的方法实现了对Nr3D的3.9%,对Sr3D的3.5%,以及对ScanRefer的4.6%的精度提升.
- 证明了POA和PSP在提高细粒度接地能力方面的有效性.
结论:
- 与传统的3DVG相比,3DPAG提供了更详细的3D对象定位方法.
- 新的数据集和方法推进了细粒度3D场景理解领域.
- 发布的数据集和平台将有助于进一步研究语句意识的3D视觉接地.
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