Update dataset card: remove device refs, add scaling and QC info

README.md

@@ -25,7 +25,9 @@ size_categories:
 
 # MCAP-Housing: Egocentric RGB-D Household Manipulation Dataset
 
-**MCAP-Housing** is an egocentric **RGB + Depth + IMU** dataset of human household manipulation activities, captured with iPhone LiDAR and packaged in robotics-native `.mcap` (ROS2) format. Designed for robotics research, policy learning, and embodied AI.
+**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.
 
 ---
 
@@ -35,7 +37,7 @@ size_categories:
 |----------|-------|
 | **Modalities** | Synchronized RGB + 16-bit Depth + IMU + Point Clouds |
 | **Resolution (RGB)** | 1920 × 1440 @ 60 FPS |
-| **Depth** | 16-bit millimeter, LiDAR-sourced (256×192 native, aligned to RGB) |
+| **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 |
@@ -43,7 +45,6 @@ size_categories:
 | **Total Frames** | ~30,000 synchronized RGB-D pairs |
 | **Total Size** | ~30 GB |
 | **Container** | `.mcap` with ROS2 CDR serialization |
-| **Compression** | zstd (~95% ratio) |
 
 ---
 
@@ -67,32 +68,18 @@ Each `.mcap` file contains **11 synchronized ROS2 topics**:
 
 ---
 
-## Sequences
-
-| Sequence | Duration | Frames | Size |
-|----------|----------|--------|------|
-| Chopping | 58s | 3,449 | 3.2 GB |
-| Filling water bottles | 57s | 3,442 | 3.5 GB |
-| Folding T shirts | 66s | 3,977 | 3.7 GB |
-| Grating in the Kitchen | 59s | 3,538 | 3.1 GB |
-| Lighting a stove | 25s | 1,507 | 1.4 GB |
-| Peeling Banana | 48s | 2,854 | 2.5 GB |
-| Preparing a solution | 42s | 2,539 | 2.4 GB |
-| Sweeping | 32s | 1,904 | 2.3 GB |
-| Transferring processed spices | 39s | 2,368 | 2.2 GB |
-| Working mortar and Pestle | 112s | 6,701 | 6.3 GB |
-
----
-
-## Optional Derived Signals (Available on Request)
+## Available on Request
 
-Beyond the raw synchronized streams, the following derived signals can be provided:
+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
-- **Additional QA layers and consistency checks** tailored to your training setup upon request
+- **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.
 
 ---
 
@@ -100,9 +87,9 @@ Beyond the raw synchronized streams, the following derived signals can be provid
 
 - Tight RGB ↔ Depth ↔ IMU synchronization (all streams at 60 Hz)
 - Per-frame camera intrinsics (not a single fixed calibration)
-- Per-frame 6DoF pose from ARKit visual-inertial odometry
+- Per-frame 6DoF pose from visual-inertial odometry
 - Depth hole-filling and bilateral filtering provided as separate topics
-- zstd compression with ~95% ratio for efficient storage and transfer
+- Full QC reports and filtered datasets available on request
 
 ---
 
@@ -152,6 +139,19 @@ pip install mcap mcap-ros2-support numpy opencv-python
 
 ---
 
+## 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.
@@ -165,4 +165,4 @@ This dataset is released under **CC-BY-NC-4.0**. Free for research and non-comme
 - **Email:** shashin.bhaskar@gmail.com
 - **Organization:** [Cortex Data Labs](https://huggingface.co/cortexdatalabs)
 
-For custom data capture, derived signals (hand tracking, SLAM, body pose), or commercial licensing, reach out directly.
+For custom data capture, derived signals, QC-validated datasets, or commercial licensing, reach out directly.