Datasets:
File size: 3,206 Bytes
090446e 750f021 090446e 750f021 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | ---
license: mit
task_categories:
- image-feature-extraction
- robotics
tags:
- scene-graph-generation
- ai2thor
- embodied-ai
- adversarial-attacks
size_categories:
- 1K<n<10K
---
# NSAP sim data
Ground-truth scene graphs rendered from AI2-THOR, built for the NSAP paper on adversarial
attacks against the symbolic grounding of scene-graph models.
Every frame ships an instance segmentation map and a scene graph with per-object
categories, attributes and pairwise relations, all read from the simulator rather than
predicted. That is the point of the set: it lets you score a scene-graph model against
exact ground truth, and it lets an attack be trained against precise object masks with no
detector in the loop.
Code: https://github.com/MasoudJTehrani/NSAP
## Getting it
```python
from huggingface_hub import snapshot_download
snapshot_download("MasoudJTehrani/NSAP-sim-data", repo_type="dataset", local_dir="dataset/sim")
```
Or, from the code repository, which also lays out `kitchen_ebnav/` where the tooling
expects it:
```bash
python NSAP.py --with-dataset
```
Pin a revision if you care about exact reproducibility. The NSAP code pins one by default.
## Layout
```
manifest.json 202 scenarios: id, scene, target category, frame count, source
scenarios/<scenario_id>/
meta.json scenario id, FloorPlan, seed, target object, frame count
scene_graph.json the ground truth, per frame (see below)
frames/NNN.png RGB, 600x600
instance/NNN.png instance segmentation, colour-keyed to object ids
kitchen_ebnav/ 12 extra scenarios, scene-matched to EB-Navigation episodes
```
`scene_graph.json` gives, for each frame, every visible object with its integer id, category,
AI2-THOR object id, instance colour, bounding box and attribute list, plus the frame's
relation edges.
## What is in it
202 scenarios, 2389 frames, 30 FloorPlans, 75 distinct object categories. Roughly 12
viewpoints per scenario, orbiting a designated target object.
| subset | scenarios | built for |
|---|---|---|
| `pilot_*` | 161 | general coverage: apple, bowl, coffee machine, fridge, kettle, microwave, pot, toaster |
| `tv_*` | 30 | the attribute head, televisions in `displaying` / `showing` states |
| `pot_*` | 11 | the relation head, pots staged in controlled `on` configurations |
Attribute vocabulary: `open`, `closed`, `clean`, `off`, `on`, `displaying`, `showing`, `filled`.
Relation vocabulary: `near`, `on`, `in`.
`kitchen_ebnav/` is separate on purpose. Its 12 scenarios are framed on the exact object
instances that EB-Navigation episodes target, so a patch trained there can be injected into
the live benchmark. Targets are pot (6), toaster (3), kettle (2) and coffee machine (1).
## Generation
Rendered with [SceneGraphBench](https://github.com/MasoudJTehrani/SGDS_AI2_THOR) at seed 0,
600x600, 12 viewpoints per target, minimum visible area 600 px, with object states
randomised and materials held fixed. `manifest.json` carries the exact generator config.
## Citation
If you use this, please cite the NSAP paper. AI2-THOR should be cited separately.
|