In the following sections you can download the RGB-D dataset, derived data, Yolo files, and relevant documents. Clicking the icon will redirect you to the relevant part in the information page for an overview on the corresponding data format or other relevant information.
Ground truth for bimanual manipulation tasks for researching temporal task models.
| File | Updated | Size | SHA256 hash |
|---|---|---|---|
| "Make muesli"-task (Action+Object ground truth, bimanual) | 03 Mar 2022 | 30.2 KiB | 179c99da7b416e2c0780b07fb8620d707dbd3c351dccd2177bc78418a25fd62c |
| "Disassemble motor"-task (Action+Object ground truth (bimanual, synthetic) | 03 Mar 2022 | 70.6 KiB | bd8306ceca43b79c364190d0585f7618e213d6195188212fdd5eb2cedf73a377 |
| "Clean table"-task (Action+Object ground truth (synthetic) | 03 Mar 2022 | 3.3 KiB | caed96b9a913c0c91cfd28137d442a496eb23b22427e12101eefc2960ba0405d |
| "Clean table"-task (Action+Object ground truth (evaluation set, synthetic) | 03 Mar 2022 | 3.5 KiB | 32f40596ca1f5271a5535cd26d52b693887621abe4f976b5cf86acafd5fb9740 |
The RGB-D dataset split on individual subjects and annotations.
| File | Updated | Size | SHA256 hash |
|---|---|---|---|
| RGB-D videos part 1/6 (subject 1) | 03 Jan 2020 | 13.8 GiB | 57139511f25ecefcba6499ef0969cea3f29ead09fff8cfaad98a1c6e5063298c |
| RGB-D videos part 2/6 (subject 2) | 03 Jan 2020 | 12.3 GiB | b64fb15cb9ea23ae3d7968379fe71ea07dcf29244be4806af222143667a666c9 |
| RGB-D videos part 3/6 (subject 3) | 03 Jan 2020 | 17.9 GiB | f51124c21fa05ac459560e43758cf8fcb42ba2db00bb086f851533fac2613c51 |
| RGB-D videos part 4/6 (subject 4) | 03 Jan 2020 | 15.7 GiB | a4e3ecfbdd090015fa7f99c4da155ed8cd6efdb5d0486c400cc7aaf159c88425 |
| RGB-D videos part 5/6 (subject 5) | 03 Jan 2020 | 14.5 GiB | 60608ee04b45a0d6b26cfdb86577e7bd90d1d65c39569dee1df7b8bea1ebb33f |
| RGB-D videos part 6/6 (subject 6) | 03 Jan 2020 | 19.0 GiB | d9a64213b06e9c914bfcf6ddefed582a4d9c3997561916e5c246120d3d00480b |
| Action ground truth (all subjects, both hands) | 20 Aug 2019 | 206.9 KiB | 0624c16ffe733ad57cea43620545801bba91881ec1e6a10b4118aee37c6ce0e0 |
| Camera normalisation (all recordings) | 20 Aug 2019 | 784.0 B | 1517f25e59caa35df632afaade5584493b5d6e244e263e98313ae880edd3d20a |
Data which was derived from the RGB-D dataset, like human pose or object bounding boxes. Each downloadable file contains the information of all subjects.
| File | Updated | Size | SHA256 hash |
|---|---|---|---|
| 2D human body pose (OpenPose) | 20 Aug 2019 | 267.9 MiB | e0978fb88f94a97ea61826c40e0b4e74f8ec088c5fb0bc8ddade734a2ac3193f |
| 2D human hand pose (OpenPose) | 20 Aug 2019 | 406.2 MiB | 3998cb654e7c38e640ffc035b26dffc44a8cc4bc1a8b15fc0bc4b3afe96dc58e |
| 2D object bounding boxes (Yolo) | 20 Aug 2019 | 174.7 MiB | ccc909149a6b363419a8fa3ef0f984e4cc5181efeb36f9f9c6cb52ed5b0b5414 |
| 3D object bounding boxes | 20 Aug 2019 | 298.2 MiB | c4218502a10550888c1d864e8055fa675e5362f3f1f0d81f3e4f251f4a0cdf92 |
| 3D spatial relations | 20 Aug 2019 | 160.2 MiB | 621fef3a0d310c7279ac33c4eb70d1ae1b446a7bc9a2fc18bd28d19b05098fed |
Data related to Yolo as object detection framework. To label of the dataset, Yolo_mark was used.
| File | Updated | Size | SHA256 hash |
|---|---|---|---|
| Object detection dataset (images & ground truth) | 20 Aug 2019 | 679.0 MiB | a5e4f2f6918edfbbda7c30e1c719678223fc5c83c3938344cc4f15ea95cc379f |
| Yolo environment for training (includes dataset) | 03 Jan 2020 | 823.4 MiB | 24ce83b898a61cbc5d523db3a64fa154a3249c14824844467160d59b8f5c5d75 |
| Yolo environment for execution (weightfile and network conf. only) | 03 Jan 2020 | 219.2 MiB | 1835cfdaf352931ed335301caaf4bb573515ba77a91e87811f3392e671539877 |
Relevant documents for this dataset.
| File | Updated | Size | SHA256 hash |
|---|---|---|---|
| Original briefing document | 20 Aug 2019 | 83.6 KiB | 44296b1c45b09b647b3c72c12f03dee590e39325cf17a5ca213c137d4f34d200 |