论文标题
类别级别的全局摄像头姿势估计带有多种假设点云对应关系
Category-Level Global Camera Pose Estimation with Multi-Hypothesis Point Cloud Correspondences
论文作者
论文摘要
通信搜索是刚性点云注册算法中的重要步骤。大多数方法在每个步骤都保持单个对应关系，并逐渐删除错误的通信。但是，建立一对一的对应关系非常困难，尤其是当将两个局部功能与两个点云匹配时。本文提出了一种优化方法，该方法在将部分点云与完整的点云匹配时保留每个关键点的所有可能对应关系。然后，通过考虑匹配成本，这些不确定的对应关系通过估计的刚性转换逐渐更新。此外，我们提出了一个新的点特征描述符，该描述符衡量本地点云区域之间的相似性。广泛的实验表明，即使在同一类别中与不同对象匹配时，我们的方法即使在匹配不同对象时也优于最先进的方法（SOTA）方法。值得注意的是，我们的方法在将真实世界的嘈杂深度图像注册到模板形状时的性能最多高20％的性能时优于SOTA方法。
Correspondence search is an essential step in rigid point cloud registration algorithms. Most methods maintain a single correspondence at each step and gradually remove wrong correspondances. However, building one-to-one correspondence with hard assignments is extremely difficult, especially when matching two point clouds with many locally similar features. This paper proposes an optimization method that retains all possible correspondences for each keypoint when matching a partial point cloud to a complete point cloud. These uncertain correspondences are then gradually updated with the estimated rigid transformation by considering the matching cost. Moreover, we propose a new point feature descriptor that measures the similarity between local point cloud regions. Extensive experiments show that our method outperforms the state-of-the-art (SoTA) methods even when matching different objects within the same category. Notably, our method outperforms the SoTA methods when registering real-world noisy depth images to a template shape by up to 20% performance.
