pcl::RANSAC segmentation, get all planes in cloud?

Once you get the first plane, remove the points and use the algorithm to compute a new plane until either there are no points left of the estimated plane is no such thing anymore. The second case is because using RANSAC you will always find a plane as long as there are enough points. I have something similar done here (this is a callback for a ros node):

void pointCloudCb(const sensor_msgs::PointCloud2::ConstPtr &msg){

    // Convert to pcl point cloud
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_msg (new pcl::PointCloud<pcl::PointXYZ>);
    pcl::fromROSMsg(*msg,*cloud_msg);
    ROS_DEBUG("%s: new ponitcloud (%i,%i)(%zu)",_name.c_str(),cloud_msg->width,cloud_msg->height,cloud_msg->size());

    // Filter cloud
    pcl::PassThrough<pcl::PointXYZ> pass;
    pass.setInputCloud(cloud_msg);
    pass.setFilterFieldName ("z");
    pass.setFilterLimits(0.001,10000);
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
    pass.filter (*cloud);

    // Get segmentation ready
    pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients);
    pcl::PointIndices::Ptr inliers(new pcl::PointIndices);
    pcl::SACSegmentation<pcl::PointXYZ> seg;
    pcl::ExtractIndices<pcl::PointXYZ> extract;
    seg.setOptimizeCoefficients (true);
    seg.setModelType (pcl::SACMODEL_PLANE);
    seg.setMethodType (pcl::SAC_RANSAC);
    seg.setDistanceThreshold(_max_distance);

    // Create pointcloud to publish inliers
    pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_pub(new pcl::PointCloud<pcl::PointXYZRGB>);
    int original_size(cloud->height*cloud->width);
    int n_planes(0);
    while (cloud->height*cloud->width>original_size*_min_percentage/100){

        // Fit a plane
        seg.setInputCloud(cloud);
        seg.segment(*inliers, *coefficients);

        // Check result
        if (inliers->indices.size() == 0)
            break;

        // Iterate inliers
        double mean_error(0);
        double max_error(0);
        double min_error(100000);
        std::vector<double> err;
        for (int i=0;i<inliers->indices.size();i++){

            // Get Point
            pcl::PointXYZ pt = cloud->points[inliers->indices[i]];

            // Compute distance
            double d = point2planedistnace(pt,coefficients)*1000;// mm
            err.push_back(d);

            // Update statistics
            mean_error += d;
            if (d>max_error) max_error = d;
            if (d<min_error) min_error = d;

        }
        mean_error/=inliers->indices.size();

        // Compute Standard deviation
        ColorMap cm(min_error,max_error);
        double sigma(0);
        for (int i=0;i<inliers->indices.size();i++){

            sigma += pow(err[i] - mean_error,2);

            // Get Point
            pcl::PointXYZ pt = cloud->points[inliers->indices[i]];

            // Copy point to noew cloud
            pcl::PointXYZRGB pt_color;
            pt_color.x = pt.x;
            pt_color.y = pt.y;
            pt_color.z = pt.z;
            uint32_t rgb;
            if (_color_pc_with_error)
                rgb = cm.getColor(err[i]);
            else
                rgb = colors[n_planes].getColor();
            pt_color.rgb = *reinterpret_cast<float*>(&rgb);
            cloud_pub->points.push_back(pt_color);

        }
        sigma = sqrt(sigma/inliers->indices.size());

        // Extract inliers
        extract.setInputCloud(cloud);
        extract.setIndices(inliers);
        extract.setNegative(true);
        pcl::PointCloud<pcl::PointXYZ> cloudF;
        extract.filter(cloudF);
        cloud->swap(cloudF);

        // Display infor
        ROS_INFO("%s: fitted plane %i: %fx%s%fy%s%fz%s%f=0 (inliers: %zu/%i)",
                 _name.c_str(),n_planes,
                 coefficients->values[0],(coefficients->values[1]>=0?"+":""),
                 coefficients->values[1],(coefficients->values[2]>=0?"+":""),
                 coefficients->values[2],(coefficients->values[3]>=0?"+":""),
                 coefficients->values[3],
                 inliers->indices.size(),original_size);
        ROS_INFO("%s: mean error: %f(mm), standard deviation: %f (mm), max error: %f(mm)",_name.c_str(),mean_error,sigma,max_error);
        ROS_INFO("%s: poitns left in cloud %i",_name.c_str(),cloud->width*cloud->height);

        // Nest iteration
        n_planes++;
    }

    // Publish points
    sensor_msgs::PointCloud2 cloud_publish;
    pcl::toROSMsg(*cloud_pub,cloud_publish);
    cloud_publish.header = msg->header;
    _pub_inliers.publish(cloud_publish);
}

you can find the whole node here