PCL: Surface模块之GridProjection表面重建

PCL的GridProjection算法用于三维点云表面重建,但不保留原始表面的拓扑结构,适合ExtremalSurface如RidgeSurfaces的重构。算法基于立方网格,效率较低。设置关键参数包括立方体分辨率、填充大小和搜索深度。

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参考文献:

        关于此类的详细信息和学术上的理论推导大家可以查看RuosiLi的文章“ Polygonizing Extremal Surfaces with ManifoldGuarantees”。

用法小结:

        说实话,参考文献上的算法跟PCL中的具体实现差别很大,我觉并不具有很大的参考意义,所以尽可跳过参考文献直接开动。下面分享一下我的心得:
  1. 算法输出为一个个小立方体堆积成的三维立体,并不是物体表面,所以它会破坏原始表面的拓扑结构;
  2. 参考文献指出此算法只面对ExtremalSurface的重构,当然主要指RidgeSurfaces或者Point set surfaces;
  3. 参考文献中使用的是octree对立方体进行分割,而PCL中使用的是绑定哈希表的立方网格;
  4. 程序处理效率偏低,所花费的时间较久。这一点和参考文献的上叙述一致-_-!。

详细介绍:

参数输入输出:

        此类由基类SurfaceReconstruction派生,有两种方式生成对象,如下:
Pcl:: GridProjection<PointInT > gp;
Pcl:: GridProjection<PointInT > gp(doub
### PCL Recognition Module Documentation and Usage Examples The Point Cloud Library (PCL) offers a comprehensive set of functionalities for point cloud processing, including the recognition module which is crucial for identifying objects within three-dimensional data sets[^3]. This section delves into how one can utilize this specific part of PCL. #### Overview of the Recognition Module Recognition refers to detecting known models or objects inside larger scenes captured as point clouds. The algorithms implemented under `pcl::recognition` namespace aim at finding instances of predefined shapes in new scans efficiently. These methods often rely heavily upon feature extraction techniques like FPFH (Fast Point Feature Histograms), VFH (Viewpoint Feature Histogram), etc., ensuring robustness against noise and partial occlusions present during real-world acquisitions[^1]. #### Installation Prerequisites Before diving deep into coding with PCL's recognition capabilities, ensure that all necessary dependencies are installed correctly. For instance, apart from downloading the main library archive (`pcl-pcl-1.11.1.tar.gz`) mentioned earlier, additional packages such as those related to optimization frameworks might be required depending on chosen approaches within your project scope[^4]. #### Basic Example Code Using Object Recognition Pipeline Below demonstrates an elementary example illustrating object detection using PCL’s built-in tools: ```cpp #include <pcl/io/pcd_io.h> #include <pcl/features/fpfh_estimation.h> #include <pcl/recognition/cg/hough_3d.h> int main () { // Load input model and scene point clouds... pcl::PointCloud<pcl::PointXYZ>::Ptr model(new pcl::PointCloud<pcl::PointXYZ>); pcl::io::loadPCDFile ("model.pcd", *model); pcl::PointCloud<pcl::PointXYZ>::Ptr scene(new pcl::PointCloud<pcl::PointXYZ>); pcl::io::loadPCDFile ("scene.pcd", *scene); // Estimate normals for both datasets... pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne; pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ> ()); // ...and so forth until implementing Hough-based matching algorithm. } ``` This snippet initializes essential components needed before applying more sophisticated processes found inside the actual documentation pages dedicated specifically towards these tasks. --related questions-- 1. What preprocessing steps should be taken prior to performing object recognition? 2. How does changing parameters affect performance when estimating features used by recognition algorithms? 3. Can you provide guidance on integrating custom sensors' outputs compatible with PCL pipelines? 4. Are there any particular challenges associated with recognizing multiple overlapping objects simultaneously?
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