Create README.md

README.md

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+---
+license: cc-by-4.0
+---
+
+# ByteCameraDepth Dataset
+
+ByteCameraDepth is a multi-camera depth estimation dataset containing synchronized depth, color, and auxiliary data captured from various 3D cameras. The dataset provides comprehensive depth sensing from multiple cameras in various in-door scenarios, making it ideal for developing and evaluating depth estimation algorithms.
+
+## Dataset Overview
+
+- **Purpose**: Multi-camera depth estimation research and benchmarking
+- **Total Sessions**: 39 recording sessions
+- **Uncompressed Size**: ~2.7TB
+- **Data Collection System**: [Multi-Camera Depth Recording System](https://github.com/Ericonaldo/depth_recording)
+- **License**: CC-BY-4.0
+
+## Quick Start
+
+### Data Extraction
+
+The dataset is provided as split archive files. To extract the complete dataset:
+
+```bash
+cat recorded_data.tar.part.* | tar -xvf -
+```
+
+This will create a `recorded_data` folder containing all 39 recording sessions.
+
+## Dataset Structure
+
+### Archive Organization
+
+```
+recorded_data_packed/
+├── recorded_data.tar.part.000
+├── recorded_data.tar.part.001
+├── ...
+└── recorded_data.tar.part.136
+```
+
+### Extracted Data Structure
+
+After extraction, the data is organized as follows:
+
+```
+recorded_data/
+└── YYYYMMDD_HHMM/              # Timestamp-based session folder (39 sessions total)
+    ├── camera_realsense_455/    # Intel RealSense D455
+    │   ├── depth_000.png        # 16-bit depth images
+    │   ├── color_000.png        # 8-bit color images
+    │   └── ...
+    ├── camera_realsense_d405/   # Intel RealSense D405
+    │   ├── depth_000.png
+    │   ├── color_000.png
+    │   └── ...
+    ├── camera_realsense_d415/   # Intel RealSense D415
+    │   ├── depth_000.png
+    │   ├── color_000.png
+    │   └── ...
+    ├── camera_realsense_d435/   # Intel RealSense D435
+    │   ├── depth_000.png
+    │   ├── color_000.png
+    │   └── ...
+    ├── camera_realsense_l515/   # Intel RealSense L515
+    │   ├── depth_000.png
+    │   ├── color_000.png
+    │   └── ...
+    ├── camera_kinect/           # Microsoft Azure Kinect
+    │   ├── depth_000.png        # 16-bit depth images
+    │   ├── color_000.png        # 8-bit color images
+    │   ├── ir_000.png           # Infrared images
+    │   └── ...
+    ├── camera_zed2i_neural/     # Stereolabs ZED2i (Neural mode)
+    │   ├── raw_depth_000.npy    # 32-bit float depth arrays
+    │   ├── depth_000.png        # 16-bit depth images
+    │   ├── color_000.png        # Color images
+    │   ├── pcd_000.npy          # Point cloud data (X,Y,Z)
+    │   ├── normal_000.npy       # Surface normal vectors
+    │   └── ...
+    ├── camera_zed2i_performance/
+    ├── camera_zed2i_quality/
+    ├── camera_zed2i_ultra/
+    └── ...
+```
+
+## Camera Systems and Specifications
+
+The dataset includes data collected by our [depth recording toolkit](https://github.com/Ericonaldo/depth_recording):
+
+### Intel RealSense Cameras
+
+- **Models**: D405, D415, D435, D455, L515
+- **Output**: `depth_xxx.png` (16-bit), `color_xxx.png` (8-bit)
+
+### Microsoft Azure Kinect
+
+- **Depth Resolution**: Wide FOV unbinned
+- **Output**: `depth_xxx.png` (16-bit), `color_xxx.png` (8-bit), `ir_xxx.png` (infrared)
+
+### Stereolabs ZED2i
+
+- **Depth Resolution**: 1280×720
+- **Depth Modes**: 4 different modes (neural, performance, quality, ultra)
+- **Output**: 
+  - `raw_depth_xxx.npy` (32-bit float depth arrays)
+  - `depth_xxx.png` (16-bit depth images)
+  - `color_xxx.png` (8-bit color images)
+  - `pcd_xxx.npy` (point cloud data)
+  - `normal_xxx.npy` (surface normal vectors)
+
+## Data Formats
+
+### File Types and Specifications
+
+| Data Type | Format | Bit Depth | Description |
+|-----------|--------|-----------|-------------|
+| Depth Images | PNG | 16-bit | Standard depth maps |
+| Color Images | PNG | 8-bit RGB | Color/texture images |
+| Raw Depth | NPY | 32-bit float | High-precision depth (ZED2i only) |
+| Point Clouds | NPY | 32-bit float | 3D point coordinates (X,Y,Z) |
+| Surface Normals | NPY | 32-bit float | Surface normal vectors |
+| Infrared | PNG | 8-bit | IR images (Kinect only) |
+
+### Depth Data
+The unit of the depth data is 'mm' for most of the cameras, which means that we can obtain the 'm'-scale by dividing the raw depth by 1000.
+Note that RealSense D405/L515 has different scales, which are 2500 and 10000, respectively. In other words, we should divide the raw depth by 2500 and 10000 to obtain the 'm'-scale depth.
+
+### File Naming Convention
+
+- Sequential numbering: `xxx` represents frame index (000, 001, 002, ...)
+- Synchronized capture: Same frame numbers across cameras represent simultaneous capture
+- Camera identification: Folder names clearly identify camera type and model
+
+## 📄 Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@article{liu2025manipulation,
+  title={Manipulation as in Simulation: Enabling Accurate Geometry Perception in Robots},
+  author={Liu, Minghuan and Zhu, Zhengbang and Han, Xiaoshen and Hu, Peng and Lin, Haotong and 
+          Li, Xinyao and Chen, Jingxiao and Xu, Jiafeng and Yang, Yichu and Lin, Yunfeng and 
+          Li, Xinghang and Yu, Yong and Zhang, Weinan and Kong, Tao and Kang, Bingyi},
+  journal={arXiv preprint},
+  year={2025}
+}
+```
+
+## License
+
+This dataset is released under the CC BY 4.0 License.