feat(calibration): RuView per-room calibration service (reference impl)

Operationalizes the campaign's central finding (ADR-150 §3.3-3.6): a frozen
shared base + a ~11KB per-room LoRA adapter from ~100-200 labeled samples
recovers SOTA-level pose in any new room/person. Verified end-to-end:
source-only base zero-shot 3.09% on unseen room -> 74.29% after 200-sample
calibration. Files: model.py (PoseNet+LoRA), calibrate.py, infer.py, README
with measured calibration budget.

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