# S3DB Scene-Graph / Layout-Attn Data Bundle

Everything needed to **train and evaluate scene-graph (SG) and layout-attention
conditioning** in the merged 3D-Belief repo (`merge-sg-conditioning` branch).
Trained model checkpoints are **not** included (20–21 GB each) — see the bottom.

## Contents

```
s3db_sg_layout_bundle/
├── s3db_dataset/train/<episode>/        # 2997 ProcTHOR-style episodes (minimal files)
│   ├── rgb_trajectory.mp4               # RGB frames (read via cv2 — the loader uses THIS, not all_rgb.npz)
│   ├── all_depths.npz                   # metric depth, key "depths" [N,H,W]
│   ├── all_poses.npz                    # GT camera-to-world poses, key "poses" [N,4,4]
│   ├── all_scene_graphs.json            # per-frame scene graph: object instances + walls (THE conditioning signal)
│   ├── door_window_bboxes.json          # (optional) precomputed door/window bboxes; refines wall conditioning
│   └── trajectory_metadata.json         # episode metadata
├── vocab/
│   ├── s3db/                            # S3DB object vocabulary + frozen text embeddings
│   │   ├── type_to_id.json / id_to_type.json            # 750-type vocab (+ *_751.json variants)
│   │   ├── sg_type_embeddings.pt        # CLIP ViT-B/16 text embeddings (512-d) — SG / FiLM conditioning
│   │   ├── sg_type_embeddings_minilm.pt # MiniLM-L6-v2 text embeddings (384-d) — layout-attn text head
│   │   └── class_freq.pt                # per-class frequency — layout-attn class weighting
│   └── procthor/                        # ProcTHOR vocab variant (id maps + CLIP embeddings)
├── checkpoints/
│   └── dinov3_vitb16_pretrain_lvd1689m.pth   # DINOv3 ViT-B — REPA target + layout-attn visual stream
├── README.md                           # this file
└── SCENE_GRAPH_CONDITIONING.md         # full merge notes (configs, scripts, training/eval commands)
```

## What each component is for

| Component | Purpose in the pipeline |
|---|---|
| `s3db_dataset` | Training/eval data. `ProcTHORDataset` reads `<root>/train/<ep>/` — RGB from the mp4, depth/poses from npz, and the scene graph from `all_scene_graphs.json` (parsed into node types/positions/sizes/edges + wall segments). |
| `vocab/s3db/sg_type_embeddings.pt` (CLIP) | Frozen per-type embeddings for the **SG** backbone (`u_vit3d_pose_sg`, FiLM + GCN, closed/open-vocab recon). |
| `vocab/s3db/sg_type_embeddings_minilm.pt` (MiniLM) | Frozen per-type embeddings for the **layout-attn** backbone's text prototype head (`u_vit3d_pose_layout`). |
| `vocab/s3db/class_freq.pt` | Class-frequency weighting for the layout-attn auxiliary CE loss. |
| `checkpoints/dinov3_*.pth` | DINOv3 ViT-B. Used as the REPA alignment target (both backbones) **and** as the per-pixel visual layout stream for layout-attn (`use_visual_layout=true`). |

## How to use (in the merged repo)

Point the configs at this bundle:
- `dataset.root_dir=<bundle>/s3db_dataset`            (dir containing `train/`)
- `dataset.vocab_dir=<bundle>/vocab/s3db`
- `model.encoder.backbone.sg_type_embeddings_path=<bundle>/vocab/s3db/sg_type_embeddings.pt`        (SG)
  or `.../sg_type_embeddings_minilm.pt`               (layout-attn)
- `model.encoder.backbone.class_freq_path=<bundle>/vocab/s3db/class_freq.pt`                         (layout-attn)
- `repa_encoder_weights=<bundle>/checkpoints/dinov3_vitb16_pretrain_lvd1689m.pth`
- `model.encoder.backbone.dinov3_weights_path=<same dinov3 path>`                                    (layout-attn visual)

Launch scripts (already in the repo under `scripts/training/`):
- SG conditioning:   `scripts/training/structured/train_s3db_film_clip_sg.sh`
- Layout-attn:       `scripts/training/structured/train_s3db_layoutattn.sh`
- Base init:         `scripts/training/structured/train_s3db_base.sh`
- Eval/inference:    `scripts/training/eval_s3db_sg_ablation.sh`, `eval_s3db_layoutattn_ablation.sh`

(Edit `DATASET_ROOT` / `VOCAB_DIR` / dinov3 path / `CUDA_VISIBLE_DEVICES` in each script header.)

## NOT included (transfer separately if needed)

- **Trained model checkpoints** (~20–21 GB each), at
  `…/structured_3d_belief/3d-belief/outputs/training/`:
  - `s3db_film_clip_sg_closed/model-34.pt`  (SG, closed-vocab; needs `n_object_types=751`)
  - `s3db_layoutattn*/model-*.pt`           (layout-attn)
  - `s3db_base/model-22.pt`                 (base init for fine-tuning the conditioned variants)
  Needed only for **eval/rollout/fine-tune**; not for training from scratch.
- **VGGT checkpoint** — only if training/eval with `use_vggt_alignment=true`.
- Per-episode files intentionally dropped (not read by the loader in this scope):
  `all_rgb.npz` (RGB comes from the mp4), `all_segmentation.npz`, `all_semantic_meta.json`,
  `all_class_pp_128.npz`, `structural_gt.json` (SG-LLM only), `predicted_poses.npz`
  (predicted-pose only), `*.bak`, `gt_obbs.json`, `scene_graph_full*.json`,
  `semantic_trajectory.mp4`, `top_down_view_*.png`.