File size: 5,068 Bytes
d37ad36 | 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 | # 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`.
|