Datasets:

Modalities:
Text
Video
Formats:
text
ArXiv:
Libraries:
Datasets
License:
text
stringlengths
39
41
03636649/5077728a780b5873f8d6a4359d6a181b
02828884/b6410fb9ca3cad2170a97514aa270017
04379243/c15aaf668a80aad9ee5912a5f7e89744
04379243/492de0f32fc58c83297936c81e7f6629
02828884/393bc2b638ed33fd201085e80edb26a
03636649/d3e339b83170d89629a60d6ab40898d
02691156/8d84a34d5aac3bffc6f6da58b133bae0
02691156/398ff83ba75191f050405f236096897d
02691156/1597cbeb8266af206aa3bf023a2b42a5
02828884/d1caf5fd22c6c5083892cfdb72a66fb4
04256520/7d756e083f671c091b17743c18fb63dc
04090263/59ff0c5e24abf33f25ff9d2d1e4772c3
02691156/dab7d624889233a863dc0bc8d259b20e
04379243/5bcb0976657fe6df37b2bb75885cfc44
02828884/38dff7c9994cda58f4273dc8988e4c4
03636649/c898f9b1dddbb8801735ea0e092a805a
04256520/15d2efe697d229aceaf288f952624966
04090263/86886a49bb69dce3fb5c1b0f759e2bc1
02933112/575d99ba4197caf041ba999b450870a8
03001627/7a2d21eddb89c8242058afcc28d23393
03636649/c4dcfcc8c434f13230584014222e685
02828884/419f4ab8254c97a2056b4bd5d870b47
02933112/72048f89b480cd411b17743c18fb63dc
02958343/bbf01f037b55306f8c2fb4d10f176f65
04256520/b526808fe0903b4484e132150e6e3ece
02691156/9eef6307dc504b88392b84e3285cce39
04379243/bf15d331be699886a005442d4981d053
02691156/3ab1e94b6c3a1730c56cc5a87f567365
03001627/36cb782fbc164ac312591a3ac05fadf1
02958343/6782126a676ca77d7a04ba129c539b64
03636649/80931bba35ec76db7088591b1a3e2750
03636649/c89d854d5c61e751cdd3c867acb77e12
03001627/d75a219d343d2469e22abcbea1db98d0
04256520/d54be63f3df4a80aafb1dd61dbf468dd
02691156/a1ce38065b93520335fc197bbabcd5bd
04256520/601bf25b4512502145c6cb69e0968783
02933112/8697ab2a0da98ba65588a2543ef0b0b4
02958343/75221b7668e145b549415f1fb7067088
04090263/86db029236b5dbafcc34b900bb2492e
03001627/88376e3d3a23d263de29d28278a34a18
04379243/4019bfe9bd7605f7a52709499e423710
02933112/87b62cc2f0368983824662341ce2b233
02691156/2b8bcb6e208a69b16a3383b58c4a9330
02828884/e28f8467945b5d526070f6b7b2547ecc
03636649/703b4edd4d407a10f8ddacb75f806b29
03001627/8098750b6089a9d8ad3a7d07aac2767
04256520/3ced29b0f7f38bcb8964df2a32faa49f
02933112/c28ae120a2d2829e50e9662c8e47fff
02958343/890e61bfb197190e6382e1684e46349f
04256520/45cc71dc2483972e742728b30848ed03
02933112/76271327a3224e56c59350d819542ec7
03001627/d2c93dac1e088b092561e050fe719ba
02691156/3d6a8f8c27e465aac6747f7c9ffe9e32
02691156/7a95a024f5616009ab21e26e992b2c94
04090263/2f2e30fde0c26bb36b2c8bb96250e4a0
02691156/cabce3320f119855a5131d38588a62b
02828884/516f47d7763d1db5ad55477d55e7af82
04090263/ff84eb89a2c7e1b55cd168ffead8840c
03636649/ff07372af062502af47e57eb62ec59ec
02828884/22da6d7559e28ac9d2b12aa6a0f050b3
04090263/e9cd2397c0a7ea86341aafe10fd0cbd4
04379243/34b36de23e2e64852e3db45253b87bcb
02958343/18cd6293d7b17a698ce68842a487b19
02691156/3ca058682dfe98f7f678b53750b6d181
03636649/230efad5abd6b56bfcb8d8c6d4df8143
04090263/744064e205da68779180711d39b16e1
04379243/222c56ff9cddbaf4139eb23f7c8036f
02828884/a6947bdac4ebc97517b431cae0dd70ed
04090263/55fde587d83088b39a2ee232a197081e
04256520/60ad8be4124fda92408442c6701ebe92
02958343/40f0bac5bcb4f8686ff5dbbe822945fd
03636649/6e913f0b67bb638288c10250d0e7fba1
03636649/53846d2802f58f97c066b9622c005c53
02691156/23e30666530887e69a6e43b878d5b335
02958343/650238554cb16926bda733a39f84326d
02691156/77a81458ea729c62ace5721ccacba16
03636649/c778a6ac6cf0c81d4904d89e9169817b
04090263/ba789d3b971e4095e2bb19fbad3e4596
04379243/fefd88656d9d0d2d82e6c3a4e742651d
03001627/708e7ef3c2afc842febad4f49b26ec52
04256520/6e0e701ad50f5f8c63a3732f072a64ec
02933112/28195efc838cbb6a4da5feafe6f1c8fc
04090263/5bb16f97c928f6dc4b49cd65dfcc3a9a
02933112/1fd36ae1d93b6f3fc59350d819542ec7
02933112/b404faa639b8600855f46d55537192b6
02958343/1660d6b7221223708a49a62fbc70ff9a
04256520/776c31753fadd5593a1c86745128d0e2
02958343/37954fb8bb9a7e351076d1567fc9aa51
03636649/833baf068fb6225c99570bac758be6a4
03001627/1bec15f362b641ca7350b1b2f753f3a2
02828884/8141d9182908d7288be87af3b9c7b4c7
02828884/14a73dd6b5d7ef35feea12256ad59f11
02691156/754d9b0f12e6c6104af5b53e1d2ec5b6
03636649/50cade4e6f714f2fc72e6d708486db91
04256520/16ca439cd60eae5f23500a5b036df62e
02933112/4c94892ca2cd066e29a50a2b3c5e5b6
04379243/7588da8ef1e427d1177f2a3a0c71fbcd
02691156/7977f492ebf2c1d5ce78be835f7c74e3
02933112/4edcf59d45bfff5a5b903ba10d2ec446
02691156/1628b65a9f3cd7c05e9e2656aff7dd5b

[ECCV 2024] MinD-3D: Reconstruct High-quality 3D objects in Human Brain

ArXiv Github

Overview

MinD-3D aims to reconstruct high-quality 3D objects based on fMRI data.

Repository Structure

  • annotations: Contains metadata and annotations related to the fMRI data for each subject.
  • sub-00xx: Each folder corresponds to a specific subject and includes their respective raw and processed fMRI data.
  • stimuli.zip: A ZIP archive of all videos shown to subjects during the fMRI scans. This file includes the stimuli used across different sessions and is critical for reproducibility of the study findings.
  • camera_pose.zip: The camera pose for each frame in the videos (each containing 192 frames) in the stimuli.

Data Description

  • raw_data: Raw fMRI data collected directly from the imaging machine.
  • npy_data: Processed data. We utilized fMRIPrep and the methodologies described in our paper to derive and store the data in NumPy format (.npy).

Citation

If you find our paper useful for your research and applications, please cite using this BibTeX:

@misc{gao2023mind3d,
  title={MinD-3D: Reconstruct High-quality 3D objects in Human Brain}, 
  author={Jianxiong Gao and Yuqian Fu and Yun Wang and Xuelin Qian and Jianfeng Feng and Yanwei Fu},
  year={2023},
  eprint={2312.07485},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}
Downloads last month
2,603
Edit dataset card