---
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}
}
```