---
license: mit
task_categories:
- image-classification
- robotics
tags:
- point-cloud
- 3d-vision
- human-robot-interaction
- lidar
- pose-classification
- robotics
size_categories:
- 1K<n<10K
---

# Crossed Arm Point Clouds Dataset

This dataset contains 3D point cloud data captured from a LiDAR scanner for crossed arm classification in the context of robot magic trick performance.

## Overview

This dataset was collected for training and evaluating the **Crossed Arm Voxel Network (CAVN)** architecture, a deep learning model designed for 3D point cloud classification in human-robot interaction magic performances. The data supports classification of human arm positions during the interactive magic trick "Which hand is the coin in?", performed by the **first fully autonomous humanoid robot magic performance** that requires no human assistance during execution.

## Dataset Description

The dataset consists of 3D point cloud data captured from participants during crossed-arm poses, where participants hide a coin in one of their hands. The robot must determine which hand contains the coin by analyzing the point cloud data.

### Data Structure

The dataset is organized into two main categories:

- **`left_arm_top/`**: Point clouds where the left arm is positioned on top during the crossed-arm pose
- **`right_arm_top/`**: Point clouds where the right arm is positioned on top during the crossed-arm pose

Each directory contains `.npz` files (NumPy compressed arrays) with individual point cloud samples.

### File Format

Each `.npz` file contains 3D point cloud data captured from a LiDAR scanner. The files follow the naming convention:
```
{category}-{sample_number}.npz
```

For example: `left_arm_top-1.npz`, `right_arm_top-42.npz`

**Point Cloud Structure:**
- **Key name**: `pcl`
- **Shape**: `(N, 4)` where N varies by sample (typically ~17,000+ points)
- **Channels**: 4 values per point:
  - Channel 0: X coordinate
  - Channel 1: Y coordinate
  - Channel 2: Z coordinate
  - Channel 3: Intensity/reflectivity value

## Usage

To load the point cloud data:

```python
import numpy as np

# Load a single sample
data = np.load('left_arm_top/left_arm_top-1.npz')

# Access the point cloud data
point_cloud = data['pcl']  # Shape: (N, 4)

# The point cloud has 4 channels: x, y, z, intensity
print(f"Point cloud shape: {point_cloud.shape}")  # e.g., (17727, 4)
print(f"Available keys: {data.files}")  # ['pcl']

# Extract individual components
x = point_cloud[:, 0]
y = point_cloud[:, 1]
z = point_cloud[:, 2]
intensity = point_cloud[:, 3]
```

## Citation

If you use this dataset in your research, please cite:

```bibtex
@inproceedings{crossed_arm_magic_trick,
  title={A Crossed Arm Voxel Network for Humanoid Robot-Human Interaction in Magic Performances},
  author={Mandala, Hanjaya and Saeedvand, Saeed and Baltes, Jacky},
  booktitle={[Conference/Journal Name]},
  year={2026},
}
```

## License

This dataset is released under the MIT License.