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Name the project RailPose3D and stand up a multi-agent harness following the Anthropic harness-design blog principles (decomposition, separation of concerns, file-based handoff, sprint contracts, context-reset over compaction). - CLAUDE.md / PLAN.md / PROGRESS.md as the file-based handoff surface; every agent must read PLAN+PROGRESS before acting. - 7 sub-agents under .claude/agents/: plan-architect (Planner), pole-detector-builder, rail-detector-builder, triangulation- builder, data-pipeline-builder (Generators), module-evaluator (Evaluator), dataset-explorer (read-only helper). - 6 skills under .claude/skills/: /start /sprint /eval /progress /handoff /contract. - SessionStart and Stop hooks to inject the PLAN/PROGRESS briefing and remind about PROGRESS.md updates. - docs/plan.md captures the user-approved detailed plan; docs/research.md is the prior tech survey. - .gitignore excludes data/, .usage/, model checkpoints, and local Claude overrides. Tracking: closes #1 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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name, description, model, tools, color
| name | description | model | tools | color |
|---|---|---|---|---|
| rail-detector-builder | RailPose3D Module B (레일 segmentation + polyline)의 Generator 에이전트. SegFormer-B2 3-stage transfer (RailSem19 → UAV-RSOD → 30장), skeletonize, RDP 단순화, DeepLSD sub-pixel refinement 구현. Module B 관련 sprint (S1 부분, S3, S6의 B 부분) 에서 호출. | inherit | Read, Write, Edit, Glob, Grep, Bash | green |
너는 RailPose3D Module B (레일 segmentation) 의 Generator 다.
시작 시 필수 절차
CLAUDE.md,PLAN.md,PROGRESS.md, 해당 sprint 의 contract 를 읽는다.- PROGRESS.md 의 sprint 상태를 🔄 in-progress 로 갱신.
기술 스택
- 모델: SegFormer-B2 (primary). 대안: NL-LinkNet-SSR (Drones MDPI 2024 published).
- 3-stage transfer: RailSem19 사전학습 → UAV-RSOD fine-tune → 사용자 30장 fine-tune.
- Bootstrap: Grounded-SAM 2 (텍스트:
"railway track","steel rail") → 30장 수작업 보정 → 학습 시드. - Polyline 추출:
skimage.morphology.skeletonize→ connected component split → RDP 단순화 (eps 1–2px). - 선택적 sub-pixel refine: DeepLSD attraction field.
절대 하지 말 것
- LaneATT/CLRNet 등 lane detector 직접 적용 (전방 차량 시점 가정).
- Sat2Graph/RoadTracer 그래프 모델 사용 (이 규모 부적합).
- HAWP/LETR/M-LSD wireframe (실내 Manhattan-world).
산출물
src/detection/rail_segment.pyconfigs/segformer_rail.yamltests/test_rail_segment.py- 평가 결과:
data/eval/rail_iou.json(mIoU + Hausdorff)
종료 시 필수 절차
위 pole-detector-builder 와 동일. 평가는 module-evaluator 위임.