School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China.
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China.
Sensors (Basel). 2018 Jul 11;18(7):2239. doi: 10.3390/s18072239.
Laser scanners are widely used to collect coordinates, also known as point-clouds, of three-dimensional free-form objects. For creating a solid model from a given point-cloud and transferring the data from the model, features-based optimization of the point-cloud to minimize the number if points in the cloud is required. To solve this problem, existing methods mainly extract significant points based on local surface variation of a predefined level. However, comprehensively describing an object's geometric information using a predefined level is difficult since an object usually has multiple levels of details. Therefore, we propose a simplification method based on a multi-level strategy that adaptively determines the optimal level of points. For each level, significant points are extracted from the point cloud based on point importance measured by both local surface variation and the distribution of neighboring significant points. Furthermore, the degradation of perceptual quality for each level is evaluated by the adjusted mesh structural distortion measurement to select the optimal level. Experiments are performed to evaluate the effectiveness and applicability of the proposed method, demonstrating a reliable solution to optimize the adaptive laser scanning of point clouds for free-forms objects.
激光扫描仪被广泛用于采集三维自由曲面物体的坐标，也称为点云。为了从给定的点云中创建一个实体模型并传输模型数据，需要对点云进行基于特征的优化，以最小化云中的点数。为了解决这个问题，现有的方法主要基于预定义的局部曲面变化来提取显著点。然而，使用预定义的级别来全面描述物体的几何信息是困难的，因为物体通常具有多个细节层次。因此，我们提出了一种基于多层次策略的简化方法，该方法自适应地确定最佳点级别。对于每个级别，基于局部曲面变化和相邻显著点分布测量的点重要性，从点云中提取显著点。此外，通过调整的网格结构失真测量来评估每个级别的感知质量退化，以选择最佳级别。实验评估了所提出方法的有效性和适用性，为自由曲面物体的自适应激光扫描点云优化提供了可靠的解决方案。
