开发者ID:aktayade,项目名称:3DSceneUnderstanding,代码行数:82,代码来源:FeatSegment.cpp 注:本文中的pcl::search::KdTree::radiusSearch方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许...
# Neighbors within radius search # std::vector<int> pointIdxRadiusSearch; # std::vector<float> pointRadiusSquaredDistance; # float radius = 256.0f * rand () / (RAND_MAX + 1.0f); # std::cout << "Neighbors within radius search at (" << searchPoint.x # << " " << searchPoint.y ...
" << pointNKNSquaredDistance[i] << ")" << std::endl; } // Neighbors within radius search std::vector<int> pointIdxRadiusSearch; std::vector<float> pointRadiusSquaredDistance; float radius = 256.0f * rand () / (RAND_MAX + 1.0f); std::cout << "Neighbors within radius search at ...
KDTree 常用于 radius search, 例如: 给定点云,计算某个点法向的时候,需要先筛出周围的点才能做 PCA. 在特征空间中,寻找和某一个用户特征最相似的一些用户。 编辑于 2024-04-22 19:34・IP 属地北京 机器学习 赞同7添加评论 分享喜欢收藏申请转载 ...
if kdtree.radiusSearch(searchPoint, radius, pointIdxNKNSearch, pointNKNSquaredDistance) > 0: for i in range(len(pointIdxNKNSearch)): print(" ", cloud.x[pointIdxNKNSearch[i]], " ", cloud.y[pointIdxNKNSearch[i]], " ", cloud.z[pointIdxNKNSearch[i]], ...
# 需要导入模块: from sklearn.neighbors import KDTree [as 别名]# 或者: from sklearn.neighbors.KDTree importquery_radius[as 别名]defstudy_redmapper_lrg_3d(hemi='north'):# create 3d grid objectgrid = grid3d(hemi=hemi)# load SDSS datasdss = load_sdss_data_both_catalogs(hemi)# load re...
}// Neighbors within radius searchstd::vector<int> pointIdxRadiusSearch; std::vector<float> pointRadiusSquaredDistance;floatradius =0.3f;if(kdtree.radiusSearch(searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) >0) { std::cout<<"pointIdxRadiusSearch.size = "<<pointIdxRadiusSe...
KDTree 后，每个点都有其空间区域坐标。不同空间区域在树中相邻，意味着它们在实际空间中也近。因此，给定一个点坐标，可通过 KDTree 快速定位到该坐标对应的空间区域及其邻近区域，方便查找该点的邻近点。RTree 通常应用于碰撞检测等场景。KDTree 则常用于 radius search，即查找指定半径内所有点。
}//搜索半径R范围内的所有近邻vector<int>Rsearch_idx; vector<float>Rsearch_dis;intR =256* rand() / (RAND_MAX +1.0f); cout<<"In"<< R <<"field at"<< keypoint.x <<""<< keypoint.y <<""<< keypoint.z <<endl;if(kdtree.radiusSearch(keypoint, R, Rsearch_idx, Rsearch_dis) ...
Once you create aKDTreeSearchermodel object, you can search the stored tree to find all neighboring points to the query data by performing a nearest neighbor search usingknnsearchor a radius search usingrangesearch. TheKd-tree algorithm is more efficient than the exhaustive search algorithm whenKis...