基于多类型几何图元匹配的三维点云配准

3D point cloud registration via matching multi types of geometric primitives

三维点云配准是计算机视觉、计算机图形学和遥感领域内诸多应用的重要基础,然而待配准数据中常有的噪声和共有区域小等问题给点云配准带来了极大的挑战。针对可能具有以上问题的人造物体点云或城市场景点云,提出一个基于多类型几何图元匹配的点云配准方法。该方法首先在原始点云中进行图元提取并基于它们的有效组合创建特征描述子,然后在描述子匹配的基础上,实现图元匹配并从中计算出变换参数,最后利用全局评估策略选出最佳变换参数并以此实现点云配准。该方法充分继承了多类型图元的优势,具有更强的鲁棒性和高效性,实验结果表明该方法在多种基准数据上取得了最佳的配准效果。

3D point cloud registration is the fundamental of a large number of applications in computer vision, computer graphics, and remote sensing, etc. However, the existence of noises and a low overlapping ratio in the scanning data poses a great challenge to the existing registration methods. Facing the point clouds of man-made objects or urban scenes that are likely to have the aforementioned issues, we propose a registration method of 3D point clouds via matching multi types of geometric primitives. Our method first extracts common geometric primitives from raw point clouds and further builds the feature descriptors from their effective combinations. Then, under the matching of the descriptors, our method realizes the matching of primitives and acquires the transformation parameters from them. Finally, based on the global evaluation for every candidate transformation, the best transformation is identified and applied to achieve the registration. Our method fully inherits the advantages of multiple types of primitives and has stronger robustness and efficiency. Experiments on various benchmarks demonstrate that our method achieves state-of-the-art registration performance.