Departamento de Engenharia de Teleinformática, Universidade Federal do Ceará, Fortaleza 60455-970, Brazil.
Sensors (Basel). 2022 Apr 9;22(8):2887. doi: 10.3390/s22082887.
In 3D reconstruction applications, an important issue is the matching of point clouds corresponding to different perspectives of a particular object or scene, which is addressed by the use of variants of the Iterative Closest Point (ICP) algorithm. In this work, we introduce a cloud-partitioning strategy for improved registration and compare it to other relevant approaches by using both time and quality of pose correction. Quality is assessed from a rotation metric and also by the root mean square error (RMSE) computed over the points of the source cloud and the corresponding closest ones in the corrected target point cloud. A wide and plural set of experimentation scenarios was used to test the algorithm and assess its generalization, revealing that our cloud-partitioning approach can provide a very good match in both indoor and outdoor scenes, even when the data suffer from noisy measurements or when the data size of the source and target models differ significantly. Furthermore, in most of the scenarios analyzed, registration with the proposed technique was achieved in shorter time than those from the literature.
在 3D 重建应用中，一个重要的问题是匹配特定物体或场景不同视角的点云，这可以通过使用迭代最近点（ICP）算法的变体来解决。在这项工作中，我们引入了一种云分区策略，以改进配准，并通过使用时间和姿态校正质量来与其他相关方法进行比较。质量评估包括旋转度量标准以及源点云中的点和校正后的目标点云中最近的点之间的均方根误差（RMSE）。我们使用广泛而多样的实验场景来测试算法并评估其泛化能力，结果表明，我们的云分区方法可以在室内和室外场景中提供非常好的匹配，即使数据受到噪声测量的影响，或者源模型和目标模型的数据大小差异很大。此外，在分析的大多数场景中，与所提出的技术的配准时间都比文献中的配准时间短。
