---
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.