metadata

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
License: MIT

Quick Start

Data Extraction

The dataset is provided as split archive files. To extract the complete dataset:

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:

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.