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Datasets:
ahanjaya
/
crossed_arm_point_clouds

Tasks:
Image Classification
Robotics
Size:
1K<n<10K
Tags:
point-cloud
3d-vision
human-robot-interaction
lidar
pose-classification
robotics
License:
Dataset card Files
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ahanjaya commited on
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1 Parent(s): b2d9e98

Adding initial datasets.

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  1. README.md +79 -3
  2. left_arm_top/left_arm_top-1.npz +3 -0
  3. left_arm_top/left_arm_top-10.npz +3 -0
  4. left_arm_top/left_arm_top-100.npz +3 -0
  5. left_arm_top/left_arm_top-1000.npz +3 -0
  6. left_arm_top/left_arm_top-1001.npz +3 -0
  7. left_arm_top/left_arm_top-1002.npz +3 -0
  8. left_arm_top/left_arm_top-1003.npz +3 -0
  9. left_arm_top/left_arm_top-1004.npz +3 -0
  10. left_arm_top/left_arm_top-1005.npz +3 -0
  11. left_arm_top/left_arm_top-1006.npz +3 -0
  12. left_arm_top/left_arm_top-1007.npz +3 -0
  13. left_arm_top/left_arm_top-1008.npz +3 -0
  14. left_arm_top/left_arm_top-1009.npz +3 -0
  15. left_arm_top/left_arm_top-101.npz +3 -0
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  17. left_arm_top/left_arm_top-1011.npz +3 -0
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  19. left_arm_top/left_arm_top-1013.npz +3 -0
  20. left_arm_top/left_arm_top-1014.npz +3 -0
  21. left_arm_top/left_arm_top-1015.npz +3 -0
  22. left_arm_top/left_arm_top-1016.npz +3 -0
  23. left_arm_top/left_arm_top-1017.npz +3 -0
  24. left_arm_top/left_arm_top-1018.npz +3 -0
  25. left_arm_top/left_arm_top-1019.npz +3 -0
  26. left_arm_top/left_arm_top-102.npz +3 -0
  27. left_arm_top/left_arm_top-1020.npz +3 -0
  28. left_arm_top/left_arm_top-1021.npz +3 -0
  29. left_arm_top/left_arm_top-1022.npz +3 -0
  30. left_arm_top/left_arm_top-1023.npz +3 -0
  31. left_arm_top/left_arm_top-1024.npz +3 -0
  32. left_arm_top/left_arm_top-1025.npz +3 -0
  33. left_arm_top/left_arm_top-1026.npz +3 -0
  34. left_arm_top/left_arm_top-1027.npz +3 -0
  35. left_arm_top/left_arm_top-1028.npz +3 -0
  36. left_arm_top/left_arm_top-1029.npz +3 -0
  37. left_arm_top/left_arm_top-103.npz +3 -0
  38. left_arm_top/left_arm_top-1030.npz +3 -0
  39. left_arm_top/left_arm_top-1031.npz +3 -0
  40. left_arm_top/left_arm_top-1032.npz +3 -0
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  46. left_arm_top/left_arm_top-1038.npz +3 -0
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  48. left_arm_top/left_arm_top-104.npz +3 -0
  49. left_arm_top/left_arm_top-1040.npz +3 -0
  50. left_arm_top/left_arm_top-1041.npz +3 -0
README.md CHANGED
@@ -1,3 +1,79 @@
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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Crossed Arm Point Clouds Dataset
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+
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+ This dataset contains 3D point cloud data captured from a LiDAR scanner for crossed arm classification in the context of robot magic trick performance.
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+
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+ ## Overview
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+
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+ 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.
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+
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+ ## Dataset Description
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+
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+ 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.
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+
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+ ### Data Structure
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+
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+ The dataset is organized into two main categories:
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+
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+ - **`left_arm_top/`**: Point clouds where the left arm is positioned on top during the crossed-arm pose
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+ - **`right_arm_top/`**: Point clouds where the right arm is positioned on top during the crossed-arm pose
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+
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+ Each directory contains `.npz` files (NumPy compressed arrays) with individual point cloud samples.
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+
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+ ### File Format
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+
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+ Each `.npz` file contains 3D point cloud data captured from a LiDAR scanner. The files follow the naming convention:
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+ ```
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+ {category}-{sample_number}.npz
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+ ```
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+
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+ For example: `left_arm_top-1.npz`, `right_arm_top-42.npz`
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+
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+ **Point Cloud Structure:**
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+ - **Key name**: `pcl`
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+ - **Shape**: `(N, 4)` where N varies by sample (typically ~17,000+ points)
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+ - **Channels**: 4 values per point:
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+ - Channel 0: X coordinate
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+ - Channel 1: Y coordinate
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+ - Channel 2: Z coordinate
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+ - Channel 3: Intensity/reflectivity value
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+
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+ ## Usage
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+
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+ To load the point cloud data:
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+
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+ ```python
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+ import numpy as np
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+
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+ # Load a single sample
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+ data = np.load('left_arm_top/left_arm_top-1.npz')
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+
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+ # Access the point cloud data
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+ point_cloud = data['pcl'] # Shape: (N, 4)
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+
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+ # The point cloud has 4 channels: x, y, z, intensity
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+ print(f"Point cloud shape: {point_cloud.shape}") # e.g., (17727, 4)
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+ print(f"Available keys: {data.files}") # ['pcl']
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+
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+ # Extract individual components
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+ x = point_cloud[:, 0]
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+ y = point_cloud[:, 1]
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+ z = point_cloud[:, 2]
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+ intensity = point_cloud[:, 3]
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+ ```
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+
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+ ## Citation
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+
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+ If you use this dataset in your research, please cite:
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+
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+ ```bibtex
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+ @inproceedings{crossed_arm_magic_trick,
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+ title={A Crossed Arm Voxel Network for Humanoid Robot-Human Interaction in Magic Performances},
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+ author={Mandala, Hanjaya and Saeedvand, Saeed and Baltes, Jacky},
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+ booktitle={[Conference/Journal Name]},
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+ year={2026},
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+ }
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+ ```
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+
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+ ## License
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+
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+ This dataset is released under the MIT License.
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