|
|
---
|
|
|
license: mit
|
|
|
task_categories:
|
|
|
- depth-estimation
|
|
|
size_categories:
|
|
|
- n>1T
|
|
|
---
|
|
|
# ByteDepth Dataset |
|
|
|
|
|
ByteDepth 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**: MIT |
|
|
|
|
|
## 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 |
|
|
|
|
|
## License |
|
|
|
|
|
This dataset is released under the MIT License. |
