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.

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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

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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

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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.

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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

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Getting Started

Inspect a file

pip install mcap

mcap info Chopping.mcap

Read in 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 for full 3D visualization of RGB, depth, point clouds, and transforms.

Dependencies

pip install mcap mcap-ros2-support numpy opencv-python

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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

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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.

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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)."

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Contact

• Email: shashin.bhaskar@gmail.com
• Organization: Cortex Data Labs

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