feat(worldmodel): ADR-147 Phase 3+5 — RuViewOccDataset domain adapter + retraining pipeline

Phase 3 — scripts/ruview_occ_dataset.py:
- RuViewOccDataset: WorldGraph JSON snapshots → OccWorld (F,H,W,D) tensors
- Indoor class remapping: person→7, floor→9, wall→11, furniture→16, free→17
- Zero ego-poses (fixed indoor sensor, no ego-motion)
- record_snapshot() helper for training data accumulation
- Validated: 5 windows, (16,200,200,16) tensor, person+floor voxels confirmed

Phase 5 — scripts/occworld_retrain.py:
- record: stream WorldGraph snapshots from sensing server REST API
- vqvae: fine-tune VQVAE tokenizer on RuView occupancy (200 epochs, AdamW)
- transformer: fine-tune autoregressive transformer with frozen VQVAE

wifi-densepose-worldmodel v0.3.0 published to crates.io

Co-Authored-By: claude-flow <ruv@ruv.net>

scripts/occworld_server.py

@@ -32,6 +32,17 @@ import os
 import signal
 import socket
 import sys
+
+# Phase 3 — RuViewOccDataset available for callers that want to build
+# training tensors directly from WorldGraph snapshots (see occworld_retrain.py).
+try:
+    _script_dir = os.path.dirname(os.path.abspath(__file__))
+    if _script_dir not in sys.path:
+        sys.path.insert(0, _script_dir)
+    from ruview_occ_dataset import RuViewOccDataset, snapshot_to_voxels, record_snapshot  # noqa: F401
+    _DATASET_AVAILABLE = True
+except ImportError:
+    _DATASET_AVAILABLE = False
 import time
 import traceback
 from typing import Any