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.

翻译：通信搜索是硬点云登记算法中的一个基本步骤。 大多数方法在每步都保持单一对应, 并逐渐消除错误对应。 但是, 建立一对一的对等与硬任务极为困难, 特别是在匹配两点云和许多本地相似的特性时。 本文建议了一种优化方法, 在匹配部分点云和完整点云时, 保留每个关键点的所有可能的对应。 这些不确定的对应程序随后会通过考虑匹配成本, 与估计的僵化转换一起逐步更新。 此外, 我们提议了一个新的点特征描述符, 以测量本地点云区域之间的相似性 。 广泛的实验显示, 即使将不同对象匹配在同一类别中, 我们的方法也超过了最先进的( SoTA) 方法 。 值得注意的是, 我们的方法在将真实世界的热度图像登记到高达20%的性能时, 也超过了 SoTA 方法 。