README.md

---
license: cc-by-nc-4.0
task_categories:
  - robotics
  - video-classification
  - depth-estimation
tags:
  - egocentric
  - rgbd
  - manipulation
  - mcap
  - ros2
  - imu
  - pointcloud
  - depth
  - kitchen
  - household
  - slam
  - hand-tracking
  - body-pose
pretty_name: "MCAP-Housing: Egocentric RGB-D Manipulation Dataset"
size_categories:
  - 1K<n<10K
---

# MCAP-Housing: Egocentric RGB-D Household Manipulation Dataset

**MCAP-Housing** is an egocentric **RGB + Depth + IMU** dataset of human household manipulation activities, packaged in robotics-native `.mcap` (ROS2) format. Designed for robotics research, policy learning, and embodied AI.

This is a **sample release**. We can scale to custom episode counts, new activities, and specific environments on request. Contact us to discuss your requirements.

---

## Quick Facts

| Property | Value |
|----------|-------|
| **Modalities** | Synchronized RGB + 16-bit Depth + IMU + Point Clouds |
| **Resolution (RGB)** | 1920 × 1440 @ 60 FPS |
| **Depth** | 16-bit millimeter, LiDAR-sourced, aligned to RGB |
| **Point Clouds** | Per-frame colored XYZRGB (up to 50k points) |
| **IMU** | 6-axis (accel + gyro) + magnetometer + gravity + orientation @ 60 Hz |
| **Pose** | 6DoF camera pose (world → camera transform) per frame |
| **Activities** | 10 household manipulation sequences |
| **Total Frames** | ~30,000 synchronized RGB-D pairs |
| **Total Size** | ~30 GB |
| **Container** | `.mcap` with ROS2 CDR serialization |

---

## What's Included Per Sequence

Each `.mcap` file contains **11 synchronized ROS2 topics**:

| Topic | Message Type | Description |
|-------|-------------|-------------|
| `/camera/rgb/compressed` | `sensor_msgs/CompressedImage` | JPEG-encoded RGB frames |
| `/camera/depth/aligned` | `sensor_msgs/Image` | Raw 16-bit depth aligned to RGB |
| `/camera/depth/filtered` | `sensor_msgs/Image` | Bilateral-filtered depth (hole-filled) |
| `/camera/depth/colorized` | `sensor_msgs/Image` | Turbo-colormap depth visualization |
| `/camera/points` | `sensor_msgs/PointCloud2` | Colored XYZRGB point cloud |
| `/camera/camera_info` | `sensor_msgs/CameraInfo` | Per-frame intrinsics (fx, fy, cx, cy) |
| `/tf` | `tf2_msgs/TFMessage` | 6DoF camera pose (world → camera) |
| `/imu` | `sensor_msgs/Imu` | Linear acceleration + angular velocity |
| `/imu/gravity` | `geometry_msgs/Vector3Stamped` | Gravity vector |
| `/imu/orientation` | `geometry_msgs/QuaternionStamped` | Device orientation quaternion |
| `/imu/mag` | `sensor_msgs/MagneticField` | Magnetometer readings |

---

## Available on Request

Beyond the raw synchronized streams, the following are available on request:

- **Ego-motion / trajectories** (VIO-style) — smooth, drift-corrected camera trajectories
- **SLAM reconstructions** — dense maps, optimized trajectories, keyframe selection
- **Accurate body pose estimation** — full skeletal tracking during manipulation
- **State-of-the-art 3D hand landmarks** — true 3D hand joint positions, not 2D reprojections
- **QC-validated data** — quality-checked sequences with automated scoring for frame drops, motion blur, depth sanity, and sync integrity
- **Additional QA layers and consistency checks** tailored to your specific training setup

Contact us to discuss which derived signals you need.

---

## Data Quality

- Tight RGB ↔ Depth ↔ IMU synchronization (all streams at 60 Hz)
- Per-frame camera intrinsics (not a single fixed calibration)
- Per-frame 6DoF pose from visual-inertial odometry
- Depth hole-filling and bilateral filtering provided as separate topics
- Full QC reports and filtered datasets available on request

---

## Getting Started

### Inspect a file
```bash
pip install mcap

mcap info Chopping.mcap
```

### Read in Python
```python
from mcap.reader import make_reader
from mcap_ros2.decoder import DecoderFactory

with open("Chopping.mcap", "rb") as f:
    reader = make_reader(f, decoder_factories=[DecoderFactory()])
    for schema, channel, message, decoded in reader.iter_decoded_messages():
        if channel.topic == "/camera/rgb/compressed":
            print(f"RGB frame at t={message.log_time}, size={len(decoded.data)} bytes")
        elif channel.topic == "/camera/depth/aligned":
            print(f"Depth frame: {decoded.width}x{decoded.height}, encoding={decoded.encoding}")
        elif channel.topic == "/camera/points":
            print(f"Point cloud: {decoded.width} points")
```

### Visualize
Open any `.mcap` file directly in [Foxglove Studio](https://foxglove.dev/) for full 3D visualization of RGB, depth, point clouds, and transforms.

### Dependencies
```bash
pip install mcap mcap-ros2-support numpy opencv-python
```

---

## Intended Uses

- **Policy and skill learning** — imitation learning, VLA pre-training
- **Action detection and segmentation** — temporal activity recognition
- **Hand and body pose estimation** — grasp analysis, manipulation understanding
- **Depth-based reconstruction** — SLAM, scene understanding, 3D mapping
- **World-model training** — ego-motion prediction, scene dynamics
- **Sensor fusion research** — RGB-D-IMU alignment and calibration

---

## Scaling & Custom Data

This release is a sample. We offer:

- **Custom episode capture** — specific activities, environments, and object sets
- **Scalable data collection** — hundreds to thousands of episodes on demand
- **Derived signal pipelines** — hand tracking, body pose, SLAM, tailored to your model
- **Custom QC gates** — filtering and validation matched to your training requirements

Reach out to discuss your needs.

---

## License

This dataset is released under **CC-BY-NC-4.0**. Free for research and non-commercial use with attribution. For commercial licensing, contact us.

**Required attribution:** *"This work uses the MCAP-Housing dataset (Cortex Data Labs, 2025)."*

---

## Contact

- **Email:** shashin.bhaskar@gmail.com
- **Organization:** [Cortex Data Labs](https://huggingface.co/cortexdatalabs)

For custom data capture, derived signals, QC-validated datasets, or commercial licensing, reach out directly.