Moradi Saed, Laurendeau Denis, Gosselin Clement
Department of Electrical and Computer Engineering, Faculty of Science and Engineering, Laval University, Quebec, QC G1V0A6, Canada.
Computer Vision and Systems Laboratory (CVSL), Laval University, Quebec, QC G1V0A6, Canada.
Sensors (Basel). 2021 Nov 17;21(22):7630. doi: 10.3390/s21227630.
Most man-made objects are composed of a few basic geometric primitives (GPs) such as spheres, cylinders, planes, ellipsoids, or cones. Thus, the object recognition problem can be considered as one of geometric primitives extraction. Among the different geometric primitives, cylinders are the most frequently used GPs in real-world scenes. Therefore, cylinder detection and extraction are of great importance in 3D computer vision. Despite the rapid progress of cylinder detection algorithms, there are still two open problems in this area. First, a robust strategy is needed for the initial sample selection component of the cylinder extraction module. Second, detecting multiple cylinders simultaneously has not yet been investigated in depth. In this paper, a robust solution is provided to address these problems. The proposed solution is divided into three sub-modules. The first sub-module is a fast and accurate normal vector estimation algorithm from raw depth images. With the estimation method, a closed-form solution is provided for computing the normal vector at each point. The second sub-module benefits from the maximally stable extremal regions (MSER) feature detector to simultaneously detect cylinders present in the scene. Finally, the detected cylinders are extracted using the proposed cylinder extraction algorithm. Quantitative and qualitative results show that the proposed algorithm outperforms the baseline algorithms in each of the following areas: normal estimation, cylinder detection, and cylinder extraction.
大多数人造物体由一些基本的几何基元（GP）组成，如球体、圆柱体、平面、椭球体或圆锥体。因此，目标识别问题可以被视为几何基元提取问题之一。在不同的几何基元中，圆柱体是现实场景中最常用的几何基元。因此，圆柱体检测和提取在三维计算机视觉中具有重要意义。尽管圆柱体检测算法取得了快速进展，但该领域仍存在两个未解决的问题。首先，圆柱体提取模块的初始样本选择组件需要一个鲁棒的策略。其次，同时检测多个圆柱体尚未得到深入研究。本文提供了一种鲁棒的解决方案来解决这些问题。所提出的解决方案分为三个子模块。第一个子模块是一种从原始深度图像中快速准确估计法向量的算法。利用该估计方法，提供了一种闭式解来计算每个点的法向量。第二个子模块利用最大稳定极值区域（MSER）特征检测器来同时检测场景中存在的圆柱体。最后，使用所提出的圆柱体提取算法提取检测到的圆柱体。定量和定性结果表明，所提出的算法在以下每个方面都优于基线算法：法向量估计、圆柱体检测和圆柱体提取。
