A novel formulation called Virtual Interest Point is presented and used to register point clouds. An implicit quadric surface representation is first used to model the point cloud segments. Macaulay’s resultant then provides the intersection of three such quadrics, which forms a virtual interest point (VIP). A unique feature descriptor for each VIP is computed, and correspondences in descriptor space are established to compute the rigid transformation to register two point clouds. Each step in the process is designed to consider robustness to noise and data density variations, as well as computational efficiency. Experiments were performed on 12 data sets, collected with a variety of range sensors, to characterize robustness to noise, data density variation, and computational efficiency. The data sets were extracted from both natural scenes, including plants and rocks, and indoor architectural scenes, such as cluttered offices and laboratories. Similarly, several 3D models were tested for registration to demonstrate the generality of the technique. The proposed method significantly outperformed a variety of alternative state-of-the-art approaches, such as 2.5D SIFT-based RANSAC method, Super 4-Point Congruent Sets and Super Generalized 4PCS, and the Go-ICP method in registering overlapping point clouds with both a higher success rate and reduced computational cost.

提出了一种称为“虚拟兴趣点”的新颖公式，并用于注册点云。首先使用隐式二次曲面表示法对点云线段进行建模。然后，Macaulay的结果提供了三个这样的二次曲面的交集，形成了一个虚拟兴趣点（VIP）。计算每个VIP的唯一特征描述符，并在描述符空间中建立对应关系以计算刚性变换以注册两个点云。该过程中的每个步骤都旨在考虑对噪声和数据密度变化的鲁棒性以及计算效率。对12种数据集进行了实验，这些数据集使用各种距离传感器收集，以表征对噪声的鲁棒性，数据密度变化和计算效率。数据集是从两个自然场景中提取的，包括植物和岩石，以及室内建筑场景，例如凌乱的办公室和实验室。同样，测试了几个3D模型进行注册，以证明该技术的普遍性。拟议的方法明显优于各种替代的最新方法，例如基于2.5D SIFT的RANSAC方法，超四点同余集和超广义4PCS，以及在注册重叠点云时使用Go-ICP方法具有更高的成功率和降低的计算成本。