HybridFusion：LiDAR与视觉跨源点云融合
HybridFusion: LiDAR and Vision Cross-Source Point Cloud Fusion
AI总结：
针对跨源点云密度与结构差异导致配准困难的问题，提出由粗到细的HybridFusion算法，通过分块匹配、2D边界配准与3D调整及聚类融合确定最终位姿，在跨源点云配准中达到SOTA性能。
摘要：
近年来，来自不同传感器的跨源点云配准已成为一个重要的研究焦点。然而，由于跨源点云的密度和结构不同，传统方法面临挑战。为了解决这些问题，我们提出了一种名为HybridFusion的跨源点云融合算法。它能够在室外大场景中配准来自不同视角的跨源密集点云。整个配准过程是一个由粗到细的过程。首先，将点云划分为小块，并基于全局描述符和空间分布选择匹配块集合，这构成了粗匹配过程。为实现精细匹配，通过从块中提取2D边界点来进行2D配准，随后进行3D调整。最后，对多个块位姿估计的结果进行聚类和融合，以确定最终位姿。通过定性和定量实验对所提出的方法进行了全面评估。为了比较跨源点云配准的鲁棒性，将所提出的方法与广义迭代最近点方法进行了比较。此外，提出了一种描述点云填充程度的度量指标。实验结果表明，我们的方法在跨源点云配准中达到了最先进的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
