gaojie
847 lines
20 KiB
Markdown
847 lines
20 KiB
Markdown
# 酒店场景建模项目 - 数据格式规范
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## 📋 文档概述
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本文档详细定义了酒店场景数字孪生项目中所有数据的存储格式、命名规范和交换标准，确保数据的可复用性、可扩展性和跨平台兼容性。
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---
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## 一、数据组织结构
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### 1.1 目录树结构
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```
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hotel_dataset/
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├── metadata.json # 全局元数据
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├── calibration/ # 传感器标定
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│ ├── camera_intrinsics.yaml
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│ ├── lidar_camera_extrinsics.yaml
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│ ├── imu_camera_extrinsics.yaml
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│ └── calibration_report.pdf
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├── scenes/ # 场景数据
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│ ├── lobby_01/ # 大堂场景1
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│ │ ├── scene_info.json # 场景元数据
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│ │ ├── raw/ # 原始数据
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│ │ │ ├── lidar/ # LiDAR点云
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│ │ │ │ ├── 0000000.pcd
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│ │ │ │ ├── 0000001.pcd
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│ │ │ │ └── ...
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│ │ │ ├── rgb/ # RGB图像
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│ │ │ │ ├── 0000000.jpg
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│ │ │ │ ├── 0000001.jpg
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│ │ │ │ └── ...
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│ │ │ ├── depth/ # 深度图
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│ │ │ │ ├── 0000000.png # 16-bit PNG
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│ │ │ │ ├── 0000001.png
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│ │ │ │ └── ...
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│ │ │ ├── imu/ # IMU数据
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│ │ │ │ └── imu_data.csv
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│ │ │ ├── polarized/ # 偏振图像（卫生间）
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│ │ │ │ ├── 0deg/
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│ │ │ │ ├── 45deg/
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│ │ │ │ ├── 90deg/
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│ │ │ │ └── 135deg/
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│ │ │ └── timestamps.txt # 时间戳对齐
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│ │ ├── processed/ # 处理后数据
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│ │ │ ├── slam/ # SLAM结果
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│ │ │ │ ├── trajectory.txt # 相机轨迹
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│ │ │ │ ├── sparse_map.ply # 稀疏点云
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│ │ │ │ ├── dense_map.ply # 稠密点云
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│ │ │ │ └── loop_closures.json # 回环信息
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│ │ │ ├── colmap/ # COLMAP输出
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│ │ │ │ ├── cameras.txt
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│ │ │ │ ├── images.txt
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│ │ │ │ ├── points3D.txt
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│ │ │ │ └── sparse/
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│ │ │ ├── 3dgs/ # 3D Gaussian Splatting
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│ │ │ │ ├── point_cloud.ply # 初始点云
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│ │ │ │ ├── cameras.json # 相机参数
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│ │ │ │ ├── cfg_args # 训练配置
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│ │ │ │ ├── input.ply # 优化后高斯
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│ │ │ │ └── iteration_30000/ # 检查点
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│ │ │ ├── nerf/ # NeRF/Ref-NeRF
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│ │ │ │ ├── transforms.json # NeRF格式位姿
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│ │ │ │ ├── model.pth # 模型权重
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│ │ │ │ └── config.yaml
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│ │ │ ├── mesh/ # 网格模型
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│ │ │ │ ├── scene.obj # 主网格
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│ │ │ │ ├── scene.mtl # 材质文件
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│ │ │ │ ├── textures/ # 纹理贴图
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│ │ │ │ │ ├── diffuse.png
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│ │ │ │ │ ├── normal.png
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│ │ │ │ │ └── roughness.png
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│ │ │ │ └── scene_watertight.obj # 水密网格
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│ │ │ ├── semantic/ # 语义信息
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│ │ │ │ ├── detections/ # 2D检测
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│ │ │ │ │ ├── yolo_results.json
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│ │ │ │ │ └── sam_masks/
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│ │ │ │ ├── instances_3d.json # 3D实例
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│ │ │ │ ├── scene_graph.json # 场景图
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│ │ │ │ ├── clip_features.npy # CLIP特征
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│ │ │ │ └── semantic_map.ply # 语义点云
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│ │ │ └── physics/ # 物理属性
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│ │ │ ├── scene.usd # USD场景
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│ │ │ ├── collision_meshes/ # 碰撞网格
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│ │ │ ├── articulation.json # 铰接信息
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│ │ │ └── material_props.json # 材质物理属性
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│ │ └── renders/ # 渲染结果
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│ │ ├── novel_views/ # 新视角
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│ │ ├── depth_maps/ # 深度图
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│ │ └── semantic_maps/ # 语义图
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│ ├── room_301/ # 客房场景
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│ │ └── ...（结构同上）
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│ └── bathroom_301/ # 卫生间场景
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│ └── ...（结构同上）
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├── annotations/ # 人工标注（可选）
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│ ├── object_labels.json
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│ ├── spatial_relations.json
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│ └── physics_properties.json
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└── benchmarks/ # 评测数据
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├── test_views/ # 测试视角
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├── ground_truth/ # 真值数据
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└── metrics/ # 评测结果
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```
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### 1.2 命名规范
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```yaml
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场景命名:
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格式: {scene_type}_{id}
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示例: lobby_01, room_301, bathroom_301
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scene_type:
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- lobby: 大堂
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- corridor: 走廊
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- room: 客房
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- bathroom: 卫生间
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- restaurant: 餐厅
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- gym: 健身房
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文件命名:
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时序数据: {frame_id:07d}.{ext}
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示例: 0000000.jpg, 0000001.pcd
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处理结果: {descriptor}_{version}.{ext}
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示例: dense_map_v2.ply, scene_graph_final.json
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版本控制:
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格式: v{major}.{minor}.{patch}
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示例: v1.0.0, v1.2.3
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```
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---
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## 二、原始数据格式
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### 2.1 点云数据（LiDAR）
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#### 格式：PCD（Point Cloud Data）
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```yaml
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文件格式: ASCII 或 Binary PCD
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编码: UTF-8
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字段:
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- x, y, z: 坐标（float32, 单位：米）
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- intensity: 反射强度（uint8, 0-255）
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- timestamp: 时间戳（float64, Unix时间）
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- ring: 激光环ID（uint16, 可选）
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示例文件头:
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VERSION 0.7
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FIELDS x y z intensity timestamp
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SIZE 4 4 4 1 8
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TYPE F F F U F
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COUNT 1 1 1 1 1
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WIDTH 65536
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HEIGHT 1
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VIEWPOINT 0 0 0 1 0 0 0
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POINTS 65536
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DATA binary
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```
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**Python读取示例**：
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```python
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import open3d as o3d
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import numpy as np
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def load_pcd(filepath):
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"""加载PCD点云"""
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pcd = o3d.io.read_point_cloud(filepath)
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# 提取数据
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points = np.asarray(pcd.points) # (N, 3)
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# 如果有颜色
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if pcd.has_colors():
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colors = np.asarray(pcd.colors) # (N, 3)
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# 如果有法线
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if pcd.has_normals():
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normals = np.asarray(pcd.normals) # (N, 3)
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return pcd, points
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# 保存PCD
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def save_pcd(filepath, points, colors=None, normals=None):
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"""保存PCD点云"""
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pcd = o3d.geometry.PointCloud()
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pcd.points = o3d.utility.Vector3dVector(points)
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if colors is not None:
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pcd.colors = o3d.utility.Vector3dVector(colors)
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if normals is not None:
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pcd.normals = o3d.utility.Vector3dVector(normals)
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o3d.io.write_point_cloud(filepath, pcd, write_ascii=False)
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```
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### 2.2 RGB图像
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#### 格式：JPEG / PNG
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```yaml
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RGB图像:
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格式: JPEG（有损压缩）
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分辨率: 1920×1080 或更高
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色彩空间: sRGB
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质量: 95（JPEG质量参数）
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命名: {frame_id:07d}.jpg
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高质量纹理:
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格式: PNG（无损）
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分辨率: 4K (3840×2160) 或更高
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色彩深度: 8-bit per channel
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命名: texture_{id:04d}.png
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```
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**EXIF元数据**（嵌入图像）：
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```json
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{
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"DateTime": "2026:05:16 10:30:45",
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"Make": "Apple",
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"Model": "iPhone 15 Pro Max",
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"FocalLength": 24.0,
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"FNumber": 1.78,
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"ISO": 100,
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"ExposureTime": "1/120",
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"GPSLatitude": 31.2304,
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"GPSLongitude": 121.4737,
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"GPSAltitude": 10.5
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}
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```
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### 2.3 深度图
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#### 格式：16-bit PNG
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```yaml
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格式: PNG
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位深度: 16-bit unsigned integer
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编码: 深度值（毫米）
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范围: 0-65535 mm (0-65.535米)
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无效值: 0（表示无深度数据）
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深度值计算:
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depth_meters = pixel_value / 1000.0
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示例:
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pixel_value = 1500 → depth = 1.5米
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pixel_value = 0 → 无效深度
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```
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**Python读取示例**：
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```python
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import cv2
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import numpy as np
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def load_depth(filepath):
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"""加载16-bit深度图"""
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depth_mm = cv2.imread(filepath, cv2.IMREAD_ANYDEPTH)
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depth_m = depth_mm.astype(np.float32) / 1000.0
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# 标记无效深度
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depth_m[depth_mm == 0] = np.nan
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return depth_m
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def save_depth(filepath, depth_m):
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"""保存深度图"""
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depth_mm = (depth_m * 1000.0).astype(np.uint16)
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depth_mm[np.isnan(depth_m)] = 0
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cv2.imwrite(filepath, depth_mm)
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```
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### 2.4 IMU数据
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#### 格式：CSV
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```csv
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timestamp,accel_x,accel_y,accel_z,gyro_x,gyro_y,gyro_z,mag_x,mag_y,mag_z
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1715875845.123456,-0.05,0.02,9.81,0.001,-0.002,0.000,25.3,10.2,-42.1
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1715875845.133456,-0.04,0.03,9.80,0.002,-0.001,0.001,25.4,10.1,-42.0
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...
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```
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**字段说明**：
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```yaml
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timestamp: Unix时间戳（秒，float64）
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accel_x/y/z: 加速度（m/s², float32）
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gyro_x/y/z: 角速度（rad/s, float32）
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mag_x/y/z: 磁场强度（μT, float32, 可选）
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坐标系: 右手坐标系
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X: 前
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Y: 左
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Z: 上
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```
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### 2.5 时间戳对齐文件
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#### 格式：timestamps.txt
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```
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# Timestamp alignment file
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# Format: frame_id timestamp_unix sensor_type
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# timestamp_unix: seconds since epoch (float64)
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0000000 1715875845.123456 lidar
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0000000 1715875845.125678 rgb
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0000000 1715875845.126789 depth
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0000001 1715875845.223456 lidar
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0000001 1715875845.225678 rgb
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0000001 1715875845.226789 depth
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...
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```
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---
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## 三、处理后数据格式
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### 3.1 SLAM轨迹
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#### 格式：TUM格式（trajectory.txt）
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```
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# TUM RGB-D SLAM trajectory format
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# timestamp tx ty tz qx qy qz qw
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# timestamp: Unix time (float64)
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# tx ty tz: translation (meters)
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# qx qy qz qw: rotation quaternion
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1715875845.123456 0.000 0.000 0.000 0.000 0.000 0.000 1.000
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1715875845.223456 0.050 0.001 0.002 0.001 0.002 0.003 0.999
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1715875845.323456 0.102 0.003 0.004 0.002 0.004 0.006 0.998
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...
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```
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**Python读取示例**：
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```python
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import numpy as np
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from scipy.spatial.transform import Rotation
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def load_trajectory(filepath):
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"""加载TUM格式轨迹"""
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data = np.loadtxt(filepath)
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timestamps = data[:, 0]
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positions = data[:, 1:4] # (N, 3)
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quaternions = data[:, 4:8] # (N, 4) [qx, qy, qz, qw]
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# 转换为旋转矩阵
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rotations = Rotation.from_quat(quaternions).as_matrix() # (N, 3, 3)
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# 构建4x4变换矩阵
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poses = np.zeros((len(data), 4, 4))
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poses[:, :3, :3] = rotations
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poses[:, :3, 3] = positions
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poses[:, 3, 3] = 1.0
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return timestamps, poses
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def save_trajectory(filepath, timestamps, poses):
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"""保存TUM格式轨迹"""
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N = len(timestamps)
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data = np.zeros((N, 8))
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data[:, 0] = timestamps
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data[:, 1:4] = poses[:, :3, 3] # 位置
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# 旋转矩阵转四元数
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rotations = Rotation.from_matrix(poses[:, :3, :3])
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data[:, 4:8] = rotations.as_quat() # [qx, qy, qz, qw]
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np.savetxt(filepath, data, fmt='%.6f')
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```
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### 3.2 COLMAP格式
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#### cameras.txt
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```
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# Camera list with one line of data per camera:
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# CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]
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# Number of cameras: 1
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1 PINHOLE 1920 1080 1066.778 1067.487 960.000 540.000
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```
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#### images.txt
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```
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# Image list with two lines of data per image:
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# IMAGE_ID, QW, QX, QY, QZ, TX, TY, TZ, CAMERA_ID, NAME
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# POINTS2D[] as (X, Y, POINT3D_ID)
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# Number of images: 500
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1 0.999 0.001 0.002 0.003 0.000 0.000 0.000 1 0000000.jpg
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512.5 384.2 1234 678.9 456.1 5678 ...
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2 0.998 0.002 0.004 0.006 0.050 0.001 0.002 1 0000001.jpg
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510.3 382.7 1235 680.1 458.3 5679 ...
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...
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```
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#### points3D.txt
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```
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# 3D point list with one line of data per point:
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# POINT3D_ID, X, Y, Z, R, G, B, ERROR, TRACK[] as (IMAGE_ID, POINT2D_IDX)
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# Number of points: 123456
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1 0.123 0.456 0.789 255 128 64 0.5 1 512 2 510 ...
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2 0.234 0.567 0.890 200 150 100 0.3 1 678 3 680 ...
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...
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```
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### 3.3 3D Gaussian Splatting格式
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#### point_cloud.ply（初始点云）
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```
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ply
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format binary_little_endian 1.0
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element vertex 123456
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property float x
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property float y
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property float z
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property uchar red
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property uchar green
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property uchar blue
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property float nx
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property float ny
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property float nz
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end_header
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<binary data>
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```
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#### cameras.json
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```json
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[
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{
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"id": 0,
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"img_name": "0000000",
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"width": 1920,
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"height": 1080,
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"position": [0.0, 0.0, 0.0],
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"rotation": [
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[1.0, 0.0, 0.0],
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[0.0, 1.0, 0.0],
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[0.0, 0.0, 1.0]
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],
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"fy": 1067.487,
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"fx": 1066.778
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},
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...
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]
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```
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#### input.ply（优化后的高斯）
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```
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ply
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format binary_little_endian 1.0
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element vertex 500000
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property float x
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property float y
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property float z
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property float nx
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property float ny
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property float nz
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property float f_dc_0
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property float f_dc_1
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property float f_dc_2
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property float f_rest_0
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...
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property float f_rest_44
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property float opacity
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property float scale_0
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property float scale_1
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property float scale_2
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property float rot_0
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property float rot_1
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property float rot_2
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property float rot_3
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end_header
|
||
<binary data>
|
||
```
|
||
|
||
**字段说明**：
|
||
|
||
```yaml
|
||
x, y, z: 高斯中心位置
|
||
nx, ny, nz: 法线（可选）
|
||
f_dc_*: 球谐函数DC分量（RGB）
|
||
f_rest_*: 球谐函数高阶分量（45个）
|
||
opacity: 不透明度
|
||
scale_*: 缩放（3个轴）
|
||
rot_*: 旋转四元数（4个分量）
|
||
```
|
||
|
||
### 3.4 NeRF格式
|
||
|
||
#### transforms.json
|
||
|
||
```json
|
||
{
|
||
"camera_angle_x": 0.6911112070083618,
|
||
"frames": [
|
||
{
|
||
"file_path": "./images/0000000",
|
||
"rotation": 0.012566370614359171,
|
||
"transform_matrix": [
|
||
[0.999, -0.001, 0.002, 0.000],
|
||
[0.001, 0.999, -0.003, 0.000],
|
||
[-0.002, 0.003, 0.999, 0.000],
|
||
[0.0, 0.0, 0.0, 1.0]
|
||
]
|
||
},
|
||
...
|
||
]
|
||
}
|
||
```
|
||
|
||
### 3.5 网格模型（Mesh）
|
||
|
||
#### OBJ格式
|
||
|
||
```
|
||
# Wavefront OBJ file
|
||
# Vertices: 123456
|
||
# Faces: 234567
|
||
|
||
mtllib scene.mtl
|
||
|
||
v 0.123 0.456 0.789
|
||
v 0.234 0.567 0.890
|
||
...
|
||
|
||
vn 0.577 0.577 0.577
|
||
vn 0.707 0.000 0.707
|
||
...
|
||
|
||
vt 0.500 0.500
|
||
vt 0.600 0.400
|
||
...
|
||
|
||
usemtl material_0
|
||
f 1/1/1 2/2/2 3/3/3
|
||
f 4/4/4 5/5/5 6/6/6
|
||
...
|
||
```
|
||
|
||
#### MTL格式（材质）
|
||
|
||
```
|
||
# Material file
|
||
|
||
newmtl material_0
|
||
Ka 0.2 0.2 0.2
|
||
Kd 0.8 0.8 0.8
|
||
Ks 0.5 0.5 0.5
|
||
Ns 96.078431
|
||
map_Kd textures/diffuse_0.png
|
||
map_Bump textures/normal_0.png
|
||
map_Ks textures/roughness_0.png
|
||
```
|
||
|
||
---
|
||
|
||
## 四、语义数据格式
|
||
|
||
### 4.1 2D检测结果（YOLO）
|
||
|
||
#### yolo_results.json
|
||
|
||
```json
|
||
{
|
||
"version": "1.0",
|
||
"model": "yolov9-world",
|
||
"images": [
|
||
{
|
||
"image_id": "0000000",
|
||
"image_path": "rgb/0000000.jpg",
|
||
"width": 1920,
|
||
"height": 1080,
|
||
"detections": [
|
||
{
|
||
"detection_id": 0,
|
||
"label": "bed",
|
||
"confidence": 0.95,
|
||
"bbox": [100, 200, 800, 600],
|
||
"bbox_format": "xyxy",
|
||
"mask_rle": "...",
|
||
"clip_feature": [0.123, 0.456, ...]
|
||
},
|
||
{
|
||
"detection_id": 1,
|
||
"label": "desk",
|
||
"confidence": 0.89,
|
||
"bbox": [1000, 300, 1500, 900],
|
||
"bbox_format": "xyxy"
|
||
}
|
||
]
|
||
}
|
||
]
|
||
}
|
||
```
|
||
|
||
### 4.2 3D实例
|
||
|
||
#### instances_3d.json
|
||
|
||
```json
|
||
{
|
||
"version": "1.0",
|
||
"scene_id": "room_301",
|
||
"coordinate_system": "right_hand_z_up",
|
||
"unit": "meter",
|
||
"instances": [
|
||
{
|
||
"instance_id": "bed_001",
|
||
"label": "bed",
|
||
"category": "furniture",
|
||
"confidence": 0.95,
|
||
"centroid": [1.5, 2.0, 0.5],
|
||
"bbox_min": [0.5, 1.0, 0.0],
|
||
"bbox_max": [2.5, 3.0, 1.0],
|
||
"oriented_bbox": {
|
||
"center": [1.5, 2.0, 0.5],
|
||
"extent": [2.0, 2.0, 1.0],
|
||
"rotation": [0.0, 0.0, 0.0, 1.0]
|
||
},
|
||
"volume": 4.0,
|
||
"mesh_path": "mesh/bed_001.obj",
|
||
"point_indices": [1234, 5678, ...],
|
||
"semantic_feature": [0.123, 0.456, ...],
|
||
"attributes": {
|
||
"material": "fabric",
|
||
"color": "white",
|
||
"state": "made",
|
||
"affordance": ["sittable", "lyable"]
|
||
}
|
||
}
|
||
]
|
||
}
|
||
```
|
||
|
||
### 4.3 场景图
|
||
|
||
#### scene_graph.json
|
||
|
||
```json
|
||
{
|
||
"version": "1.0",
|
||
"scene_id": "room_301",
|
||
"metadata": {
|
||
"room_type": "hotel_room",
|
||
"floor_area": 25.5,
|
||
"ceiling_height": 2.8,
|
||
"capture_date": "2026-05-16"
|
||
},
|
||
"nodes": [
|
||
{
|
||
"node_id": "bed_001",
|
||
"type": "object",
|
||
"label": "bed",
|
||
"instance_ref": "bed_001",
|
||
"properties": {
|
||
"size": "queen",
|
||
"material": "fabric",
|
||
"color": "white"
|
||
}
|
||
},
|
||
{
|
||
"node_id": "nightstand_001",
|
||
"type": "object",
|
||
"label": "nightstand",
|
||
"instance_ref": "nightstand_001"
|
||
},
|
||
{
|
||
"node_id": "lamp_001",
|
||
"type": "object",
|
||
"label": "lamp",
|
||
"instance_ref": "lamp_001"
|
||
}
|
||
],
|
||
"edges": [
|
||
{
|
||
"edge_id": 0,
|
||
"source": "lamp_001",
|
||
"target": "nightstand_001",
|
||
"relation": "supported_by",
|
||
"confidence": 0.98,
|
||
"properties": {
|
||
"contact_area": 0.05,
|
||
"stability": "stable"
|
||
}
|
||
},
|
||
{
|
||
"edge_id": 1,
|
||
"source": "nightstand_001",
|
||
"target": "bed_001",
|
||
"relation": "next_to",
|
||
"confidence": 0.95,
|
||
"properties": {
|
||
"distance": 0.1,
|
||
"side": "left"
|
||
}
|
||
}
|
||
]
|
||
}
|
||
```
|
||
|
||
---
|
||
|
||
## 五、物理数据格式
|
||
|
||
### 5.1 USD场景（Universal Scene Description）
|
||
|
||
#### scene.usd（文本格式示例）
|
||
|
||
```python
|
||
#usda 1.0
|
||
(
|
||
defaultPrim = "World"
|
||
metersPerUnit = 1
|
||
upAxis = "Z"
|
||
)
|
||
|
||
def Xform "World"
|
||
{
|
||
def Mesh "Bed"
|
||
{
|
||
float3[] extent = [(-1, -1, 0), (1, 1, 1)]
|
||
int[] faceVertexCounts = [4, 4, 4, 4, 4, 4]
|
||
int[] faceVertexIndices = [0, 1, 3, 2, ...]
|
||
point3f[] points = [(0.5, 1.0, 0.0), ...]
|
||
|
||
# 物理属性
|
||
def PhysicsRigidBodyAPI
|
||
{
|
||
bool kinematicEnabled = true
|
||
}
|
||
|
||
def PhysicsMassAPI
|
||
{
|
||
float mass = 50.0
|
||
}
|
||
|
||
def PhysicsCollisionAPI
|
||
{
|
||
}
|
||
|
||
def PhysicsMaterialAPI
|
||
{
|
||
float staticFriction = 0.5
|
||
float dynamicFriction = 0.4
|
||
float restitution = 0.1
|
||
}
|
||
}
|
||
|
||
def Mesh "Wardrobe_Door"
|
||
{
|
||
# 铰接物体
|
||
def PhysicsRevoluteJoint "Hinge"
|
||
{
|
||
rel body0 = </World/Wardrobe>
|
||
rel body1 = </World/Wardrobe_Door>
|
||
point3f localPos0 = (0, 0, 0)
|
||
point3f localPos1 = (0.5, 0, 0)
|
||
float3 axis = (0, 0, 1)
|
||
float lowerLimit = 0.0
|
||
float upperLimit = 120.0
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
### 5.2 铰接信息
|
||
|
||
#### articulation.json
|
||
|
||
```json
|
||
{
|
||
"version": "1.0",
|
||
"scene_id": "room_301",
|
||
"articulated_objects": [
|
||
{
|
||
"object_id": "wardrobe_door_001",
|
||
"base_link": "wardrobe_001",
|
||
"joint_type": "revolute",
|
||
"joint_axis": [0, 0, 1],
|
||
"joint_origin": [1.0, 2.0, 1.0],
|
||
"joint_limits": {
|
||
"lower": 0.0,
|
||
"upper": 2.094,
|
||
"effort": 10.0,
|
||
"velocity": 1.0
|
||
},
|
||
"damping": 0.5,
|
||
"friction": 0.1
|
||
},
|
||
{
|
||
"object_id": "drawer_001",
|
||
"base_link": "desk_001",
|
||
"joint_type": "prismatic",
|
||
"joint_axis": [1, 0, 0],
|
||
"joint_origin": [1.5, 1.0, 0.7],
|
||
"joint_limits": {
|
||
"lower": 0.0,
|
||
"upper": 0.4,
|
||
"effort": 5.0,
|
||
"velocity": 0.5
|
||
}
|
||
}
|
||
]
|
||
}
|
||
```
|
||
|
||
### 5.3 材质物理属性
|
||
|
||
#### material_props.json
|
||
|
||
```json
|
||
{
|
||
"version": "1.0",
|
||
"materials": [
|
||
{
|
||
"material_id": "wood_oak",
|
||
"density": 600.0,
|
||
"static_friction": 0.5,
|
||
"dynamic_friction": 0.4,
|
||
"restitution": 0.3,
|
||
"young_modulus": 11000000000.0,
|
||
"poisson_ratio": 0.3,
|
||
"damping": 0.1
|
||
},
|
||
{
|
||
"material_id": "fabric_cotton",
|
||
"density": 200.0,
|
||
"static_friction": 0.7,
|
||
"dynamic_ |