Recently, cross-source point cloud registration from different sensors has become a significant research focus. However, traditional methods confront challenges due to the varying density and structure of cross-source point clouds. In order to solve these problems, we propose a cross-source point cloud fusion algorithm called HybridFusion. It can register cross-source dense point clouds from different viewing angle in outdoor large scenes. The entire registration process is a coarse-to-fine procedure. First, the point cloud is divided into small patches, and a matching patch set is selected based on global descriptors and spatial distribution, which constitutes the coarse matching process. To achieve fine matching, 2D registration is performed by extracting 2D boundary points from patches, followed by 3D adjustment. Finally, the results of multiple patch pose estimates are clustered and fused to determine the final pose. The proposed approach is evaluated comprehensively through qualitative and quantitative experiments. In order to compare the robustness of cross-source point cloud registration, the proposed method and generalized iterative closest point method are compared. Furthermore, a metric for describing the degree of point cloud filling is proposed. The experimental results demonstrate that our approach achieves state-of-the-art performance in cross-source point cloud registration.

翻译：最近，来自不同传感器的交叉源点云配准已成为重要的研究重点。然而，传统方法面临着由于交叉源点云的不同密度和结构而带来的挑战。为了解决这些问题，我们提出了一种名为混合融合（HybridFusion）的交叉源点云融合算法。它可以注册来自不同视角的交叉源密集点云，适用于室外大场景。整个注册过程是一个从粗到细的过程。首先，将点云分成小块，基于全局描述符和空间分布选取匹配块集，构成粗匹配过程。为了实现细匹配，通过从块中提取2D边界点进行2D配准，然后进行3D调整。最后，将多个块位姿估计的结果进行聚类并融合以确定最终的位姿。我们通过定性和定量实验对所提出的方法进行了全面评估。为了比较交叉源点云配准的鲁棒性，将所提出的方法和广义迭代最近点（Generalized ICP）方法进行了比较。此外，提出了一种描述点云填充度的指标。实验结果表明，我们的方法在交叉源点云配准方面达到了最新的性能。