论文标题
6D多对象姿势估计的迭代精炼耦合
Coupled Iterative Refinement for 6D Multi-Object Pose Estimation
论文作者
论文摘要
我们解决了6D多对象姿势的任务：给定一组已知的3D对象和RGB或RGB-D输入图像，我们检测并估算每个对象的6D姿势。我们为6D对象提出了一种新方法，该方法由端到端的可区分体系结构组成，该体系结构利用几何知识。我们的方法以紧密耦合的方式迭代地完善了姿势和对应关系，使我们能够动态删除异常值以提高准确性。我们使用新颖的可区分层来执行姿势改进，通过解决优化问题，我们称为双向深度凸起的透视图N点（BD-PNP）。我们的方法在标准6D对象构成基准测试方面实现了最先进的精度。代码可从https://github.com/princeton-vl/coupled-iterative-refinement获得。
We address the task of 6D multi-object pose: given a set of known 3D objects and an RGB or RGB-D input image, we detect and estimate the 6D pose of each object. We propose a new approach to 6D object pose estimation which consists of an end-to-end differentiable architecture that makes use of geometric knowledge. Our approach iteratively refines both pose and correspondence in a tightly coupled manner, allowing us to dynamically remove outliers to improve accuracy. We use a novel differentiable layer to perform pose refinement by solving an optimization problem we refer to as Bidirectional Depth-Augmented Perspective-N-Point (BD-PnP). Our method achieves state-of-the-art accuracy on standard 6D Object Pose benchmarks. Code is available at https://github.com/princeton-vl/Coupled-Iterative-Refinement.
