| --- |
| 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. |
|
|
