Fast template matching and pose estimation in 3D point clouds
Fast template matching and pose estimation in 3D point clouds
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DOI：
10.1016/j.cag.2018.12.007
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发表时间：
2019-04-01
影响因子：
2.5
通讯作者：
Klein, Reinhard
中科院分区：
文献类型：
--
作者：
Vock, Richard;Dieckmann, Alexander;Klein, Reinhard
点云数据中三维形状的模板匹配是众多应用的重要前提，例如已知物体的料箱拾取任务、扫描过程中冗余物体实例的检测和补全以及工业装配的验证。基于现有的模板匹配方法，特别是利用点元组特征在随机抽样一致性（RANSAC）环境中快速生成变换猜测的方法，我们引入了一种改进的、有针对性的采样策略以及一种高效的假设验证方法，以大幅提高整体运行时间。在我们的实验中，与未优化的实现相比，所提出的优化使性能提高了两个数量级。在各种真实世界和模拟数据集上的若干实验证明了我们所提出方法的稳健性。© 2019爱思唯尔有限公司。保留所有权利。
Template matching for 3D shapes in point cloud data is an essential prerequisite for a multitude of applications such as bin picking tasks for known objects, detection and completion of redundant object instances during scanning endeavors, and verification of industrial assemblies. Building on existing approaches for template matching, especially on methods utilizing point tuple features for the quick generation of transformation guesses in a RANdom SAmple Consensus (RANSAC) setting, we introduce an improved, targeted sampling strategy as well as an efficient hypothesis validation approach to drastically improve the overall runtime. In our experiments the proposed optimizations lead to a performance increase by two orders of magnitude in comparison to an unoptimized implementation. Several experiments on diverse real-world and simulated datasets demonstrate the robustness of our proposed approach. (C) 2019 Elsevier Ltd. All rights reserved.