/**@brief register the format header of PCD file of dataset into PCL. */
struct PointXYZIT {
    float x;
    float y;
    float z;
    unsigned char intensity;
    double timestamp;
    EIGEN_MAKE_ALIGNED_OPERATOR_NEW 
} EIGEN_ALIGN16;

POINT_CLOUD_REGISTER_POINT_STRUCT (PointXYZIT,
                                    (float, x, x)
                                    (float, y, y)
                                    (float, z, z)
                                    (uint8_t, intensity, intensity)
                                    (double, timestamp, timestamp)
                                    )
    
/**@brief load pcd file.
    * @param  <pose_file_path> path of pcd file.
    * @param  <cloud> a PointXYZIT type pointer to store the content of PCD file.
    */
bool load_pcd_file(
        const std::string& load_pcd_path, 
        pcl::PointCloud <PointXYZIT>::Ptr cloud) {
    if (pcl::io::loadPCDFile(load_pcd_path.c_str(), *cloud) != 0) {
        std::cerr  < < "Can't load pcd file from "  < < load_pcd_path  < < std::endl;   
        return false;            
    }    
    return true;
}          
            

/**@brief project point cloud onto image with rolling shutter.
    * @param <timestamp> timestamp of the image.
    * @param <cam_intrinsic> intrinsic of the camera.
    * @param <cloud> 3D coords of point cloud.
    * @param <pixels> corresponding pixel coords on image.
*/
const double shutter_time = 45.56 * 1e-3;
void project_rolling_shutter(
    const double ×tamp, const CameraIntrinsics &cam_intrinsic,
    const std::vector<Eigen::Vector3d> &cloud, std::vector<ImagePixel> *pixels) {
    project_global_shutter(timestamp, cam_intrinsic, cloud, pixels);

    uint32_t height = cam_intrinsic.height;
        uint32_t width = cam_intrinsic.width;

    // Generate cloud2camera transformation per image line
    double last_timestamp = timestamp;
    double first_timestamp = last_timestamp - shutter_time;
    double stamp_stride = (last_timestamp - first_timestamp) / height;
	std::vector<Eigen::Affine3d> cloud2camera_array(height);
    for (uint32_t idx = 0; idx < height; ++idx) {
        double cur_stamp = first_timestamp + stamp_stride * (idx + 0.5);
        get_cloud2camera(cur_stamp, &cloud2camera_array[idx]);
    }

    // Iterative projection
    const int itr_num = 2;
    for (int itr = 0; itr < itr_num; ++itr) {
        for (size_t idx = 0; idx < cloud.size(); ++idx) {
            auto &pix = (*pixels)[idx];

            // Use pose from current image line
            int row = pix.y;
            if (row < 0) {row = 0;}
            if (row > height - 1) {row = height - 1;}
            const Eigen::Vector3d pt3d_cam = cloud2camera_array[row] * cloud[idx];

            if (pt3d_camΐ] > 1e-3) {
                project_3d_2d(cam_intrinsic, pt3d_cam, &pix);
            }
        }
    }
}
            

                @INPROCEEDINGS{DA4AD_2020_ECCV,
                    title={DA4AD: End-to-end Deep Attention-based Visual Localization for Autonomous Driving},
                    author={Zhou, Yao and Wan, Guowei and Hou, Shenhua and Yu, Li and Wang, Gang and Rui, Xiaofei and Song, Shiyu},
                    booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
                    year={2020}
                }