[2] Bertram Drost, Markus Ulrich, Nassir Navab,等. Model globally, match locally: Efficient and robust 3D object recognition[C]// 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2010.
typedef std::vector<ppf_match_3d::Pose3DPtr> vector_Pose3DPtr; ^~~~ /media/wjl/0B8803760B880376/github/2/opencv_contrib-4.1.0/modules/surface_matching/misc/python/pyopencv_ppf_match_3d.hpp:16:21: note: suggested alternative: ‘rpmatch’ typedef std::vector<ppf_match_3d::Pose3DPtr> v...
match locally: Efficient and robust 3D object recognition. In Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, 13–18 June 2010; pp. 998–1005.
match locally: Efficient and robust 3D object recognition. In Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, 13–18 June 2010; pp. 998–1005.[2] Vidal, J.; Lin, C.; Martí, R. 6Dpose estimationusing an improve...
1.一个高度概括:原论文的标题,"Model Globally, Match Locally"高度概括了该法的优点;所谓Model Globally,是指对model中所有的点对(任取两个点组成一个点对,遍历所有可能的组合)都计算了PPF描述子,以描述子为key,以这两个点为value构建hash table, 该hash table可以看作是对model 的一个global的描述; 在使用...
ppf_match_3d :: PPF3D检测器检测器(0.03,0.05); detector.trainModel(PC); vector <Pose3DPtr>结果; detector.match(pcTest,results,1.0 / 10.0,0.05); cout << “Poses:” << endl; //打印姿势 for ( size_t i = 0; i <results.size(); i ++) ...
在机器视觉的领域中，PPF（Point Pair Features）以其独特的6D姿态估计技术占据着重要地位，特别是在工业环境中，对于那些纹理缺失或曲率较小的物体，它的应用尤为显著。其核心理念在于"Model Globally, Match Locally"，即通过构建全局描述模型，配合精细的局部匹配策略，实现了3D版的广义霍夫变换，展现出...
1. “Model globally, match locally: Efficient and robust 3D object recognition” 这篇文章是PPF的鼻祖,发表在2010年CVPR。其构建两点及其法向量之间的几何关系形成的四维特征作为点对特征(Point Pair Features)。线下建立哈希表存储模型的所有四维特征作为模型的整体描述。线上匹配阶段,借用全局坐标系简化刚体变换自...
为了从累加器中提取对象姿态,Drost-ppf使用了一种贪婪的聚类方法,它们从累加器中提取峰值,每个峰值对应于对象3D姿态上的假设以及模型和场景点之间的对应关系,并以与其票数相同的顺序处理它们,如果它们足够接近,则将它们分配给最近的集群,或者以其他方式创建另一个集群。我们发现,这种方法并不总是可靠的,特别是在噪声...
[2] Bertram Drost, Markus Ulrich, Nassir Navab,等. Model globally, match locally: Efficient and robust 3D object recognition[C]// 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2010.