论文标题
一个新颖的树结构点云数据集用于骨骼化算法评估
A novel tree-structured point cloud dataset for skeletonization algorithm evaluation
论文作者
论文摘要
从无组织点云中提取曲线骨架是计算机视觉和三维数据预处理和可视化的基本任务。从点云中提取骨骼已做出了大量工作。但是缺乏与地面真相骨架的点云的标准数据集，因此很难评估这些算法。在本文中，我们构建了一个全新的树结构点云数据集，包括地面真相骨骼和点云模型。此外，在清洁点云上构建了四种类型的点云：具有噪声的点云，缺少数据的点云，具有不同密度的点云以及密度分布不均的点云。我们首先使用树编辑器来构建树骨架和相应的网格模型。由于隐式表面足以保留复杂分支模型的边缘和细节，因此我们使用隐式表面对三角形网格进行建模。使用隐式表面，将虚拟扫描仪应用于点云的采样。最后，考虑到骨骼提取的挑战，我们引入了不同的方法来构建四种不同类型的点云模型。该数据集可以用作骨架提取算法的标准数据集。可以通过将地面真相骨骼与提取的骨骼进行比较来进行骨骼提取算法之间的评估。
Curve skeleton extraction from unorganized point cloud is a fundamental task of computer vision and three-dimensional data preprocessing and visualization. A great amount of work has been done to extract skeleton from point cloud. but the lack of standard datasets of point cloud with ground truth skeleton makes it difficult to evaluate these algorithms. In this paper, we construct a brand new tree-structured point cloud dataset, including ground truth skeletons, and point cloud models. In addition, four types of point cloud are built on clean point cloud: point clouds with noise, point clouds with missing data, point clouds with different density, and point clouds with uneven density distribution. We first use tree editor to build the tree skeleton and corresponding mesh model. Since the implicit surface is sufficiently expressive to retain the edges and details of the complex branches model, we use the implicit surface to model the triangular mesh. With the implicit surface, virtual scanner is applied to the sampling of point cloud. Finally, considering the challenges in skeleton extraction, we introduce different methods to build four different types of point cloud models. This dataset can be used as standard dataset for skeleton extraction algorithms. And the evaluation between skeleton extraction algorithms can be performed by comparing the ground truth skeleton with the extracted skeleton.
