Datasets:

wlsdzyzl
/

MedPointS-cpl

partial sequencelengths 2.05k 2.05k	target sequencelengths 2.05k 2.05k	label sequencelengths 46 46
[[-0.3835538327693939,-0.3929283022880554,-0.1887635588645935],[0.6208478212356567,-0.22725024819374(...TRUNCATED)	[[0.5514898896217346,-0.032678909599781036,0.13073520362377167],[0.6926737427711487,-0.0635124742984(...TRUNCATED)	[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED)
[[-0.5378422737121582,-0.433886855840683,0.2469240128993988],[-0.6002985239028931,-0.142281278967857(...TRUNCATED)	[[-0.5378422737121582,-0.433886855840683,0.2469240128993988],[-0.6002985239028931,-0.142281278967857(...TRUNCATED)	[1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED)
[[-0.13123555481433868,-0.07372531294822693,-0.19995419681072235],[0.044016119092702866,-0.068124577(...TRUNCATED)	[[-0.13134150207042694,-0.016813993453979492,-0.47040116786956787],[0.01256971713155508,-0.124938212(...TRUNCATED)	[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0(...TRUNCATED)
[[-0.345342755317688,0.14480054378509521,-0.35873842239379883],[-0.25601717829704285,0.6739689111709(...TRUNCATED)	[[-0.3506908416748047,0.6175650358200073,-0.6815122365951538],[-0.2734837234020233,-0.15924343466758(...TRUNCATED)	[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED)
[[0.16609284281730652,0.8120800852775574,-0.14261355996131897],[0.06522627919912338,0.97310787439346(...TRUNCATED)	[[-0.007034395355731249,-0.5001687407493591,0.08722671866416931],[-0.10236356407403946,0.94630789756(...TRUNCATED)	[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED)
[[0.5906967520713806,0.31957119703292847,0.13402743637561798],[-0.6068885326385498,-0.22744955122470(...TRUNCATED)	[[-0.5802068710327148,-0.33404967188835144,-0.24737772345542908],[-0.3825802505016327,-0.51505011320(...TRUNCATED)	[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED)
[[-0.50295090675354,0.25197821855545044,0.04323625937104225],[-0.38283732533454895,0.307568728923797(...TRUNCATED)	[[0.972815752029419,-0.07703309506177902,-0.19237780570983887],[-0.7132983207702637,-0.2356956303119(...TRUNCATED)	[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED)
[[-0.6186241507530212,0.0678502693772316,0.14729078114032745],[-0.3335418105125427,-0.45781403779983(...TRUNCATED)	[[-0.6693559885025024,-0.08814885467290878,-0.18772532045841217],[-0.8543333411216736,0.042480889707(...TRUNCATED)	[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED)
[[-0.17979714274406433,0.13814395666122437,0.8985661864280701],[0.22553180158138275,0.31187227368354(...TRUNCATED)	[[0.004916684702038765,0.2331704944372177,0.3886214792728424],[0.023199094459414482,0.21751815080642(...TRUNCATED)	[0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED)
[[0.40374937653541565,0.5640502572059631,-0.18078374862670898],[0.46988916397094727,0.21301192045211(...TRUNCATED)	[[-0.7015784382820129,0.6988839507102966,0.20591554045677185],[0.486093670129776,0.08551770448684692(...TRUNCATED)	[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED)

End of preview. Expand in Data Studio

MedPointS-CPL

This is the medical point cloud completion dataset from MedPointS, where partial is the partial point cloud, 'target' is the target point cloud, and label is the class label.

Each point cloud has been normalized and sub-sampled to 2048 points. The correspondence between class names and labels is listed as follows (the label value plus 1 is the actual key of following map):

coarse_label_to_organ = {1: 'adrenalgland',
    2: 'aorta',
    3: 'autochthon',
    4: 'bladder',
    5: 'brain',
    6: 'breast',
    7: 'bronchie',
    8: 'celiactrunk',
    9: 'cheek',
    10: 'clavicle',
    11: 'colon',
    12: 'costa',
    13: 'duodenum',
    14: 'esophagus',
    15: 'eyeball',
    16: 'femur',
    17: 'gallbladder',
    18: 'gluteusmaximus',
    19: 'heart',
    20: 'hip',
    21: 'humerus',
    22: 'iliacartery',
    23: 'iliacvena',
    24: 'iliopsoas',
    25: 'inferiorvenacava',
    26: 'kidney',
    27: 'liver',
    28: 'lung',
    29: 'mediastinaltissue',
    30: 'pancreas',
    31: 'portalveinandsplenicvein',
    32: 'smallbowel',
    33: 'spleen',
    34: 'stomach',
    35: 'thymus',
    36: 'thyroid',
    37: 'trachea',
    38: 'uterocervix',
    39: 'uterus',
    40: 'vertebrae',
    41: 'gonads',
    42: 'sacrum',
    43: 'clavicula',
    # 44: 'prostate',
    44: 'pulmonaryartery',
    # 45: 'ribcartilage',
    45: 'rib',
    46: 'scapula',
    # 48: 'skull',
    # 49: 'spinalcanal',
    # 50: 'sternum'
    }

If you find our project helpful, please consider to cite the following works:

@misc{zhang2025hierarchicalfeaturelearningmedical,
      title={Hierarchical Feature Learning for Medical Point Clouds via State Space Model}, 
      author={Guoqing Zhang and Jingyun Yang and Yang Li},
      year={2025},
      eprint={2504.13015},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2504.13015}, 
}

dataset_info: features: - name: partial sequence: sequence: float32 - name: target sequence: sequence: float32 - name: label sequence: float32 splits: - name: train num_bytes: 1888940484 num_examples: 28737 download_size: 1438880848 dataset_size: 1888940484 configs: - config_name: default data_files: - split: train path: data/train-*

Downloads last month: 32

Size of downloaded dataset files:

1.44 GB

Size of the auto-converted Parquet files:

1.44 GB

Number of rows:

28,737