MasoudJTehrani/NSAP-sim-data

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

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:

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 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.