Publishes the live 3D point cloud viewer to gh-pages/pointcloud/ so it
can be linked from the README alongside the Observatory and Dual-Modal
Pose Fusion demos. The viewer auto-selects its transport from URL
parameters:
- default / ?backend=auto — try /api/splats, fall back to synthetic demo
- ?backend=demo — synthetic in-browser only, no network
- ?backend=<url> — fetch from a CORS-permitting host running
ruview-pointcloud serve
- ?live=1 — strict mode, show offline panel instead of demo fallback
The synthetic frame matches the live API JSON shape (splats, count,
frame, live, pipeline.{skeleton,vitals}) so a single render path drives
both modes. New workflow uses keep_files: true to preserve the existing
observatory/, pose-fusion/, and nvsim/ deployments on gh-pages.
See docs/adr/ADR-094-pointcloud-github-pages-deployment.md for the full
decision record and 6 acceptance gates.
This folder contains 44 Architecture Decision Records (ADRs) that document every significant technical choice in the RuView / WiFi-DensePose project.
Why ADRs?
Building a system that turns WiFi signals into human pose estimation involves hundreds of non-obvious decisions: which signal processing algorithms to use, how to bridge ESP32 firmware to a Rust pipeline, whether to run inference on-device or on a server, how to handle multi-person separation with limited subcarriers.
ADRs capture the context, options considered, decision made, and consequences for each of these choices. They serve three purposes:
Institutional memory — Six months from now, anyone (human or AI) can read why we chose IIR bandpass filters over FIR for vital sign extraction, not just see the code.
AI-assisted development — When an AI agent works on this codebase, ADRs give it the constraints and rationale it needs to make changes that align with the existing architecture. Without them, AI-generated code tends to drift — reinventing patterns that already exist, contradicting earlier decisions, or optimizing for the wrong tradeoffs.
Review checkpoints — Each ADR is a reviewable artifact. When a proposed change touches the architecture, the ADR forces the author to articulate tradeoffs before writing code, not after.
ADRs and Domain-Driven Design
The project uses Domain-Driven Design (DDD) to organize code into bounded contexts — each with its own language, types, and responsibilities. ADRs and DDD work together:
ADRs define boundaries: ADR-029 (RuvSense) established multistatic sensing as a separate bounded context from single-node CSI. ADR-042 (CHCI) defined a new aggregate root for coherent channel imaging.
DDD models define the language: The RuvSense domain model defines terms like "coherence gate", "dwell time", and "TDM slot" that ADRs reference precisely.
Together they prevent drift: An AI agent reading ADR-039 knows that edge processing tiers are configured via NVS keys, not compile-time flags — because the ADR says so. The DDD model tells it which aggregate owns that configuration.
How ADRs are structured
Each ADR follows a consistent format:
Context — What problem or gap prompted this decision
Decision — What we chose to do and how
Consequences — What improved, what got harder, and what risks remain
References — Related ADRs, papers, and code paths
Statuses: Proposed (under discussion), Accepted (approved and/or implemented), Superseded (replaced by a later ADR).
