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.

本研究提出一种名为 HybridFusion 的跨来源点云融合算法，包括粗到细的 patch 匹配，2D/3D 注册和多个 Patch 姿态估计结果簇合和融合来确定最终姿态，经过定性和定量实验证明 HybridFusion 达到了近期点云同源性配准的最好表现。