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Abstract：
Point cloud registration is a fundamental task in computer vision and graphics, which is widely used in 3D reconstruction, object tracking, and atlas reconstruction. Learning-based optimization and deep learning methods have been widely developed in pairwise registration due to their own distinctive advantages. Deep learning methods offer greater flexibility and enable registering unseen point clouds that are not trained. Learning-based optimization methods exhibit enhanced robustness and stability when handling registration under various perturbations, such as noise, outliers, and occlusions. To leverage the strengths of both approaches to achieve a less time-consuming, robust, and stable registration for multiple instances, we propose a novel computational framework called SGRTmreg for multiple pairwise registrations in this paper. The SGRTmreg framework utilizes three components-a Searching scheme, a learning-based optimization method called Graph-based Reweighted discriminative optimization (GRDO), and a Transfer module to achieve multi-instance point cloud registration.Given a collection of instances to be matched, a template as a target point cloud, and an instance as a source point cloud, the searching scheme selects one point cloud from the collection that closely resembles the source. GRDO then learns a sequence of regressors by aligning the source to the target, while the transfer module stores and applies the learned regressors to align the selected point cloud to the target and estimate the transformation of the selected point cloud. In short, SGRTmreg harnesses a shared sequence of regressors to register multiple point clouds to a target point cloud. We conduct extensive registration experiments on various datasets to evaluate the proposed framework. The experimental results demonstrate that SGRTmreg achieves multiple pairwise registrations with higher accuracy, robustness, and stability than the state-of-the-art deep learning and traditional registration methods.
摘要：
点云配准是计算机视觉和图形学中的一项基本任务,广泛应用于三维重建，对象跟踪,和图集重建。基于学习的优化和深度学习方法由于其自身独特的优势在成对配准中得到了广泛的发展。深度学习方法提供了更大的灵活性，可以注册未经训练的看不见的点云。基于学习的优化方法在处理各种扰动下的配准时表现出增强的鲁棒性和稳定性，比如噪音,异常值,和闭塞。为了利用这两种方法的优势来实现更短的耗时，健壮,和多个实例的稳定注册，在本文中，我们提出了一种新的计算框架，称为SGRTmreg，用于多个成对注册。SGRTmreg框架利用三个组件-搜索方案，一种基于学习的优化方法，称为基于图的加权判别优化(GRDO)，传输模块实现多实例点云配准。给定要匹配的实例集合，作为目标点云的模板，和一个实例作为源点云，搜索方案从集合中选择一个与源非常相似的点云。然后，GRDO通过将源与目标对齐来学习一系列回归变量，而传输模块存储并应用学习的回归量以将所选择的点云与目标对齐并估计所选择的点云的变换。总之,SGRTmreg利用回归量的共享序列将多个点云注册到目标点云。我们对各种数据集进行了广泛的注册实验，以评估所提出的框架。实验结果表明，SGRTmreg以更高的精度实现了多个成对配准，鲁棒性，和稳定性比国家的最先进的深度学习和传统的注册方法。
