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[[0.2687426507472992,-0.18558183312416077,0.6158682107925415],[0.4019162356853485,-0.099080167710781(...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.(...TRUNCATED) |
[[0.5106569528579712,-0.3968597650527954,0.22847266495227814],[-0.5524895191192627,-0.57139855623245(...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.(...TRUNCATED) |
[[0.05373100936412811,-0.29799240827560425,0.25970131158828735],[-0.011619244702160358,-0.1204599589(...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.(...TRUNCATED) |
[[-0.2755780816078186,0.17172540724277496,-0.8866153955459595],[0.35451194643974304,-0.0506801642477(...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.(...TRUNCATED) |
[[0.6301254034042358,0.11313020437955856,0.9968953728675842],[0.41495925188064575,-0.316775143146514(...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.(...TRUNCATED) |
[[0.3055073618888855,0.1118980199098587,0.4034581482410431],[0.3258039355278015,-0.1708856225013733,(...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.(...TRUNCATED) |
[[-0.12739549577236176,-0.5505359172821045,0.0944964662194252],[0.6521085500717163,-0.43998184800148(...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.(...TRUNCATED) |
[[-0.13807156682014465,-0.15514284372329712,0.2610704004764557],[-0.08452985435724258,-0.13893929123(...TRUNCATED) | [[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.(...TRUNCATED) |
[[0.30764397978782654,-0.20269876718521118,0.028748001903295517],[-0.041783686727285385,-0.175087660(...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.(...TRUNCATED) |
[[-0.024161117151379585,-0.22340084612369537,-0.19506672024726868],[0.10432766377925873,0.0629183650(...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.(...TRUNCATED) |
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MedPointS-SEG
This is the medical point cloud segmentation dataset from MedPointS, where data is the input point cloud, and label is the class label.
Each point cloud has been normalized and sub-sampled to 4096 points.
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: label sequence: sequence: float32 splits: - name: train num_bytes: 856481800 num_examples: 1025 download_size: 60709044 dataset_size: 856481800 configs: - config_name: default data_files: - split: train path: data/train-* license: mit language: - en tags: - biology - medical - point cloud - segmentation
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