目录

一、概述

1.1原理

1.2实现步骤

1.3应用场景

二、代码实现

2.1关键函数

2.1.1点云压缩

2.1.2点云解压缩

2.2完整代码

三、实现效果

3.1原始点云

3.2数据显示

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PCL点云算法汇总及实战案例汇总的目录地址链接：

PCL点云算法与项目实战案例汇总（长期更新）

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一、概述

        八叉树（Octree）是一种高效的三维空间数据结构，用于组织和索引点云数据。除了用于邻域搜索、体素化和可视化等操作，八叉树还可以用于点云的压缩。通过压缩，点云数据的大小可以显著减少，同时保留大致的几何形状，从而加速点云处理。

1.1原理

        八叉树压缩点云的原理是通过递归地将三维空间划分为八个子空间，直到每个体素（空间区域）中只有一个或几个点。可以根据指定的分辨率对点云进行体素化，当多个点落在同一个体素时，只保留一个点作为代表点，这样就减少了点云中的点数，达到压缩的目的。

1.2实现步骤

1. 读取点云数据。
2. 使用 pcl::octree::OctreePointCloudCompression 创建八叉树压缩对象。
3. 压缩点云，并保存压缩结果。
4. 解压缩点云以恢复压缩后的数据，并将结果可视化。

1.3应用场景

1. 存储优化：减少点云存储所需的空间。
2. 传输优化：通过压缩减少点云在网络传输中的数据量。
3. 点云降采样：通过压缩降低点云数据的密度。

二、代码实现

2.1关键函数

2.1.1点云压缩

        通过 pcl::octree::OctreePointCloudCompression 对点云进行压缩，可以显著减少点云的大小。

#include <pcl/compression/octree_pointcloud_compression.h>

// 创建压缩对象
pcl::io::OctreePointCloudCompression<pcl::PointXYZ> pointCloudEncoder(true);  // 压缩设置为true

// 将点云压缩到字符串流中
std::stringstream compressedData;  // 存储压缩后的数据
pointCloudEncoder.encodePointCloud(cloud, compressedData);  // 压缩点云数据

2.1.2点云解压缩

        通过 pcl::io::OctreePointCloudCompression 解压缩先前压缩的点云数据，将数据还原为点云格式。 

// 创建解压缩对象
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_decoded(new pcl::PointCloud<pcl::PointXYZ>);
pcl::io::OctreePointCloudCompression<pcl::PointXYZ> pointCloudDecoder(false);  // 解压缩设置为false

// 从压缩数据流中解压缩点云
pointCloudDecoder.decodePointCloud(compressedData, cloud_decoded);  // 解压缩

2.2完整代码

#include <pcl/compression/octree_pointcloud_compression.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <iostream>
#include <sstream>

// 封装的可视化函数，显示两个点云，一个原始点云，一个解压缩后的点云
void visualizePointClouds(
    pcl::PointCloud<pcl::PointXYZ>::Ptr original_cloud,
    pcl::PointCloud<pcl::PointXYZ>::Ptr decompressed_cloud)
{
    pcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer("Dual PointCloud Viewer"));

    // 设置视口1，显示原始点云
    int vp_1;
    viewer->createViewPort(0.0, 0.0, 0.5, 1.0, vp_1);
    viewer->setBackgroundColor(1.0, 1.0, 1.0, vp_1);
    viewer->addText("Original PointCloud", 10, 10, "vp1_text", vp_1);
    pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> original_color_handler(original_cloud, 0, 255, 0);
    viewer->addPointCloud(original_cloud, original_color_handler, "original_cloud_vp1", vp_1);

    // 设置视口2，显示解压缩后的点云
    int vp_2;
    viewer->createViewPort(0.5, 0.0, 1.0, 1.0, vp_2);
    viewer->setBackgroundColor(1.0, 1.0, 1.0, vp_2);
    viewer->addText("Decompressed PointCloud", 10, 10, "vp2_text", vp_2);
    pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> decompressed_color_handler(decompressed_cloud, 0, 0, 255);
    viewer->addPointCloud(decompressed_cloud, decompressed_color_handler, "decompressed_cloud_vp2", vp_2);

    viewer->addCoordinateSystem(1.0);
    viewer->initCameraParameters();

    while (!viewer->wasStopped())
    {
        viewer->spinOnce(100);
    }
}

int main(int argc, char** argv)
{
    // 读取点云
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
    if (pcl::io::loadPCDFile<pcl::PointXYZ>("person2.pcd", *cloud) == -1)
    {
        PCL_ERROR("Couldn't read file!");
        return -1;
    }

    std::cout << "Original cloud size: " << cloud->points.size() << " points." << std::endl;

    // 创建压缩器和解压缩器对象，使用默认配置
    pcl::io::compression_Profiles_e compressionProfile = pcl::io::LOW_RES_ONLINE_COMPRESSION_WITHOUT_COLOR;

    // 压缩对象
    pcl::io::OctreePointCloudCompression<pcl::PointXYZ> pointCloudEncoder(compressionProfile, true);
    std::stringstream compressedData;

    // 压缩点云
    pointCloudEncoder.encodePointCloud(cloud, compressedData);
    std::cout << "Point cloud compressed successfully." << std::endl;

    // 解压缩对象
    pcl::io::OctreePointCloudCompression<pcl::PointXYZ> pointCloudDecoder(compressionProfile, false);
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_decoded(new pcl::PointCloud<pcl::PointXYZ>);

    // 解压缩点云
    pointCloudDecoder.decodePointCloud(compressedData, cloud_decoded);
    std::cout << "Point cloud decompressed successfully." << std::endl;
    std::cout << "Decompressed cloud size: " << cloud_decoded->points.size() << " points." << std::endl;

    // 可视化压缩前后的点云
    visualizePointClouds(cloud, cloud_decoded);

    return 0;
}

三、实现效果

3.1原始点云

3.2数据显示

Original cloud size: 10000 points.
*** POINTCLOUD ENCODING ***
Frame ID: 1
Encoding Frame: Intra frame
Number of encoded points: 8890
XYZ compression percentage: 7.122234%
XYZ bytes per point: 0.854668 bytes
Color compression percentage: 0.000000%
Color bytes per point: 0.000000 bytes
Size of uncompressed point cloud: 138.906250 kBytes
Size of compressed point cloud: 7.419922 kBytes
Total bytes per point: 0.854668 bytes
Total compression percentage: 5.341676%
Compression ratio: 18.720716

Point cloud compressed successfully.
Point cloud decompressed successfully.
Decompressed cloud size: 8890 points.