Pov9D:以点云为基础的开放词汇9D对象立场估计
Tianfu Wang1,2, Hongguang Wang1
1State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Chuangxin Road 135, Shenyang 110016, China.
Journal of imaging
|November 26, 2025
概括
Pov9D是一个新的点云框架,用于开放的词汇对象位置估计. 它使用文本描述和3D几何来准确地预测姿势,优于现有的方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 几何深度学习 几何深度学习
背景情况:
- 传统的对象姿势估计在很大程度上依赖于RGB数据和外观线索.
- 没有纹理或功能较差的对象对现有方法构成重大挑战,导致模两可.
- 开放词汇设置需要模型将其概括为未见对象类别.
研究的目的:
- 介绍Pov9D,一种基于点云的新框架,用于强大的开放词汇对象立场估计.
- 利用3D几何信息和语义文本描述来提高姿势的准确性.
- 为了使具有有限或没有纹理的对象能够准确地估计姿势.
主要方法:
- 使用3D点云作为主要输入,专注于几何结构.
- 集成了类别级的文本描述,以指导姿势估计过程.
- 引入文本条件形状预先生成器,从点云和文本中预测正常化的对象形状.
主要成果:
- 在OO3D-9D基准上,Pov9D取得了最先进的表现.
- 与OV9D相比,在绝对IOU@50 (+7.2%) 和相对10°10cm (+27.2%) 中显著改善.
- 有效地预测对象的转换,旋转和大小,即使是以前看不见的类别.
结论:
- 基于点云的方法与文本指导相结合,为开放词汇对象立场估计提供了强大的解决方案.
- 通过提供一致的几何参考,Pov9D的文本受条件的形状前置使准确的姿势预测成为可能.
- 该框架显示出强大的概括能力,解决了基于外观的方法的局限性.
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