Diff9D:基于扩散的域-一般化的类别-级别9-DoF对象位置估计
概括
本研究引入了分类级九度自由度 (9-DoF) 对象姿势估计的扩散模型,只使用合成数据,可以将其推广到真实世界的场景. 该方法实现了最先进的性能,而不需要3D形状先验.
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
- 机器学习 机器学习
背景情况:
- 类别级的九度自由度 (9-DoF) 对象姿势和尺寸估计对于增强现实和机器人操纵至关重要.
- 现有的类别级别方法一般化到看不见的对象,但需要广泛的现实世界数据收集和标签.
- 域泛化仍然是一个重大挑战,限制了在特定数据集上训练的模型的适用性.
研究的目的:
- 开发一个基于扩散的范式,用于域泛化类别级别的9-DoF对象构成估计.
- 利用扩散模型的概括能力来克服领域转移问题.
- 为了在现实场景中使用仅在合成数据上训练的模型来实现准确的姿势估计.
主要方法:
- 为 9-DoF 对象姿势估计提出了一个新的扩散模型,将其作为一个生成任务.
- 仅在染合成数据上训练模型,消除了对现实世界标记数据的需求.
- 使用了Denoising Diffusion Implicit Model (DDIM) 进行高效的反向扩散,在短短3个步骤中实现了近乎实时的性能.
- 该模型不需要在训练或推断过程中使用3D形状先验.
主要成果:
- 在两个基准数据集上实现了最先进的域泛化性能.
- 在现实世界机器人抓取系统中成功应用.
- 通过三步反向扩散过程实现了近乎实时的性能.
结论:
- 拟议的基于扩散的方法有效地解决了类别级9-DoF对象立场估计中的域概括挑战.
- 仅仅在合成数据上的训练能够对现实世界的场景进行强有力的概括.
- 该方法为机器人操纵和增强现实等应用提供了实用解决方案.
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