| --- |
| license: other |
| license_name: matterport3d-tou |
| license_link: https://niessner.github.io/Matterport/ |
| task_categories: |
| - visual-question-answering |
| - robotics |
| language: |
| - en |
| tags: |
| - embodied-ai |
| - vision-language-navigation |
| - spatial-reasoning |
| - vln-ce |
| - matterport3d |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: train/**/meta.json |
| - split: test |
| path: test/**/meta.json |
| --- |
| |
| # MindCraftV2 |
|
|
| Cognitive probes injected into VLN-CE navigation episodes, regenerated with the |
| LASAR data-generation pipeline ([arXiv:2605.16899](https://arxiv.org/abs/2605.16899)). |
|
|
| **10,819 trajectories · 61 Matterport3D scenes · 56,755 queries · 279 GB** |
|
|
| ## Split |
|
|
| 80/20 **by episode**, seed 42. The split is at trajectory level on purpose: the |
| ~5 probes of one episode all target the same rooms and objects, so a |
| query-level split would leak test scenes into training. |
|
|
| | split | episodes | queries | |
| |-------|----------|---------| |
| | train | 8,655 | 45,373 | |
| | test | 2,164 | 11,382 | |
|
|
| ## Layout |
|
|
| ``` |
| train/000517/ |
| data.npz observations, semantic_observations, actions, instruction, |
| instruction_text, scene_id, memory_log, |
| mindcraft_queries, primary_query |
| meta.json instruction + queries as plain JSON |
| query_summary.png one-page render of every query for the episode |
| split.json the episode ids in each split |
| ``` |
|
|
| `data.npz` holds object arrays, so it needs `allow_pickle`: |
|
|
| ```python |
| import numpy as np |
| d = np.load("train/000517/data.npz", allow_pickle=True) |
| memory_log = d["memory_log"].item() # per-timestep pose, room, visible objects |
| queries = d["mindcraft_queries"].tolist() |
| frames = d["observations"] # (T, 256, 256, 3) uint8 |
| semantics = d["semantic_observations"] # (T, 256, 256, 1) int32 instance ids |
| ``` |
|
|
| ## Query types |
|
|
| | type | count | coverage | answer balance | |
| |------|-------|----------|----------------| |
| | L1.1 Object Attribute Recall | 9,852 | 91.1% | right 51 / left 49 | |
| | L1.2 Temporal Relation Recall | 9,447 | 87.3% | after 50 / before 50 | |
| | L2.1 Self-Localization | 10,816 | 100.0% | A 33 / B 33 / C 34 | |
| | L2.2 Local Spatial Relation | 8,805 | 81.4% | right 48 / left 46 / behind 3 / in front of 3 | |
| | L3.1 Topological Adjacency | 9,190 | 84.9% | A 33 / B 34 / C 33 | |
| | L3.2 Landmark Path Validation | 8,645 | 79.9% | yes 50 / no 50 | |
|
|
| Coverage below 100% is deliberate: a generator that cannot find an unambiguous |
| probe emits nothing rather than a guessable question. |
|
|
| ## Differences from MindCraft v1 |
|
|
| Regenerated with the label leaks and unanswerable questions in the original |
| generators fixed: |
|
|
| - **L1.2** answered `"before"` 100% of the time (the earlier object was always |
| named first). The pair is now swapped half the time. |
| - **L1.1** read left/right off one arbitrarily sampled frame, so an object the |
| agent walked past had no single correct answer; the relation must now hold |
| across every frame where the object was clearly visible. Instance uniqueness |
| and room naming now use the object's own region rather than the observer's. |
| - **L2.1** drew distractors from a hand-written list of tidy room names, so |
| against a Matterport label like `familyroom/lounge` the odd one out was the |
| answer. Distractors now come from the scene's own region vocabulary. |
| - **L2.2** always asked "left or right?" but could answer `front`/`back`. The |
| question now matches the relation. |
| - **L3.1** only excluded the chosen answer's name from the distractors, so a |
| "wrong" option could name a room type that really is adjacent through another |
| instance. All adjacent room names are excluded now. |
| - **L3.2** was 64% "yes" and its negatives were room types absent from the |
| scene, answerable without reasoning about the path. Both labels are now |
| enumerated before one is chosen, and negatives are rooms the scene really has. |
|
|
| No episode-level video is shipped; `observations` in `data.npz` holds every |
| frame. |
|
|
| ## License |
|
|
| The RGB and semantic frames are renders of **Matterport3D** scenes and are |
| governed by the [Matterport3D Terms of Use](https://niessner.github.io/Matterport/). |
| You must sign that agreement before using this data. Generation code follows |
| LASAR / habitat-lab (MIT). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{tang2026lasar, |
| title={LASAR: Towards Spatio-temporal Reasoning with Latent Cognitive Map}, |
| author={Tang, Jinzhou and Liu, Sidi and Xiu, Waikit and Chen, Weixing and Wang, Keze}, |
| journal={arXiv preprint arXiv:2605.16899}, |
| year={2026} |
| } |
| ``` |
|
|
