论文标题
一般对象本地化
One-Shot General Object Localization
论文作者
论文摘要
本文介绍了一种一般的单发对象定位算法，称为OnEloc。当前的一击对象定位或检测方法要么依赖于缓慢的详尽特征匹配过程，要么缺乏推广到新颖对象的能力。相比之下，我们提议的OnEloc算法有效地通过特殊投票方案找到了对象中心和框架大小。为了保持我们的方法量表不变，仅估计单位中心偏移方向和相对尺寸。提出了一个新颖的均衡投票模块，以更好地找到无纹理的物体。实验表明，所提出的方法在两个数据集上实现了最新的总体性能：OnePose数据集和LineMod数据集。此外，我们的方法还可以实现单发多实体检测和非刚性对象定位。代码存储库：https：//github.com/qq456cvb/oneloc。
This paper presents a general one-shot object localization algorithm called OneLoc. Current one-shot object localization or detection methods either rely on a slow exhaustive feature matching process or lack the ability to generalize to novel objects. In contrast, our proposed OneLoc algorithm efficiently finds the object center and bounding box size by a special voting scheme. To keep our method scale-invariant, only unit center offset directions and relative sizes are estimated. A novel dense equalized voting module is proposed to better locate small texture-less objects. Experiments show that the proposed method achieves state-of-the-art overall performance on two datasets: OnePose dataset and LINEMOD dataset. In addition, our method can also achieve one-shot multi-instance detection and non-rigid object localization. Code repository: https://github.com/qq456cvb/OneLoc.
