将点云转换为 depth/multi 通道图像

Question

我有一个通过使用立体相机扫描平面生成的点云。我已经生成了法线、fpfh 等特征，并使用这些信息我想 class 化点云中的区域。为了能够使用更传统的 CNN 方法，我想将此点云转换为 opencv 中的多通道图像。我将点云折叠到 XY 平面，并与 X 和 Y 轴对齐，以便我可以为图像创建边界框。

我正在寻找有关如何进一步处理从点到像素的映射的想法。具体来说，我对图像大小以及如何用适当的数据填充每个像素感到困惑。（重叠点将被平均掉，空的点将被相应地标记）。由于这是一个无组织的点云，我没有相机参数可以使用，我猜 PCL 的 RangImage class 在我的情况下不起作用。

感谢任何帮助！

Answer 1

首先尝试创建一个空的 cv::Mat 预定大小。然后遍历该 Mat 的每个像素以确定它应该取什么值。

下面是一些代码，其功能类似于您所描述的内容：

cv::Mat makeImageFromPointCloud(pcl::PointCloud<pcl::PointXYZI>::Ptr cloud, std::string dimensionToRemove, float stepSize1, float stepSize2)
{
    pcl::PointXYZI cloudMin, cloudMax;
    pcl::getMinMax3D(*cloud, cloudMin, cloudMax);

    std::string dimen1, dimen2;
    float dimen1Max, dimen1Min, dimen2Min, dimen2Max;
    if (dimensionToRemove == "x")
    {
        dimen1 = "y";
        dimen2 = "z";
        dimen1Min = cloudMin.y;
        dimen1Max = cloudMax.y;
        dimen2Min = cloudMin.z;
        dimen2Max = cloudMax.z;
    }
    else if (dimensionToRemove == "y")
    {
        dimen1 = "x";
        dimen2 = "z";
        dimen1Min = cloudMin.x;
        dimen1Max = cloudMax.x;
        dimen2Min = cloudMin.z;
        dimen2Max = cloudMax.z;
    }
    else if (dimensionToRemove == "z")
    {
        dimen1 = "x";
        dimen2 = "y";
        dimen1Min = cloudMin.x;
        dimen1Max = cloudMax.x;
        dimen2Min = cloudMin.y;
        dimen2Max = cloudMax.y;
    }

    std::vector<std::vector<int>> pointCountGrid;
    int maxPoints = 0;

    std::vector<pcl::PointCloud<pcl::PointXYZI>::Ptr> grid;

    for (float i = dimen1Min; i < dimen1Max; i += stepSize1)
    {
        pcl::PointCloud<pcl::PointXYZI>::Ptr slice = passThroughFilter1D(cloud, dimen1, i, i + stepSize1);
        grid.push_back(slice);

        std::vector<int> slicePointCount;

        for (float j = dimen2Min; j < dimen2Max; j += stepSize2)
        {
            pcl::PointCloud<pcl::PointXYZI>::Ptr grid_cell = passThroughFilter1D(slice, dimen2, j, j + stepSize2);

            int gridSize = grid_cell->size();
            slicePointCount.push_back(gridSize);

            if (gridSize > maxPoints)
            {
                maxPoints = gridSize;
            }
        }
        pointCountGrid.push_back(slicePointCount);
    }

    cv::Mat mat(static_cast<int>(pointCountGrid.size()), static_cast<int>(pointCountGrid.at(0).size()), CV_8UC1);
    mat = cv::Scalar(0);

    for (int i = 0; i < mat.rows; ++i)
    {
        for (int j = 0; j < mat.cols; ++j)
        {
            int pointCount = pointCountGrid.at(i).at(j);
            float percentOfMax = (pointCount + 0.0) / (maxPoints + 0.0);
            int intensity = percentOfMax * 255;

            mat.at<uchar>(i, j) = intensity;
        }
    }

    return mat;
}

将点云转换为 depth/multi 通道图像

Converting a pointcloud to a depth/multi channel image

3d

opencv

machine-learning

computer-vision

point-cloud-library