Mastershort/neonplan3d

3D floor plan for Home Assistant: draw your home in the built-in editor (rooms, walls, doors, windows, furniture, roof) and see and control lights, covers, windows, climate, media, sensors and cameras live in a neon 3D view – as a sidebar panel and dashboard card.

NeonPlan 3D – a Home Assistant integration for visual home control

What it is – NeonPlan 3D is a Home Assistant integration that lets you draw a floor‑plan of your house directly inside Home Assistant and instantly see a neon‑styled 3‑D rendering. The 3‑D view is interactive: lights can be toggled, blinds moved, doors opened, cameras inspected, and appliances show their status.

Key features

  • Plan editor – draw rooms, walls, doors, windows, stairs, roof, outdoor areas; supports free‑form shapes and automatic wall generation.
  • Live 3‑D view – tap objects to control them (lights, blinds, doors), see colour‑coded status, view camera snapshots, heat‑maps for temperature/humidity/CO₂.
  • Furniture library – >50 built‑in models plus optional furniture packs (rooms, vehicles, smart‑home devices). Lamps emit coloured light in the view.
  • Dashboard card – custom:neonplan3d-card can be placed on any Lovelace dashboard; many visual options (theme, quality, explode view, alerts, weather, etc.) are configurable via a visual editor or YAML.
  • Wall‑tablet / kiosk mode – optimized for wall‑mounted tablets (e.g., Fire tablets) with idle return, night dimming, and a full‑screen kiosk UI.
  • Alerts & automation – shows smoke, gas, water, rain‑open‑window warnings; integrates with Home Assistant scenes and scripts.
  • Extensions – optional paid packs (extra furniture, Pro add‑ons like camera cockpit, weather outside, live TV screens) are delivered via a licence key and update automatically.

Installation

  • Recommended via HACS (Home Assistant Community Store): add the repository as a custom integration, install, restart Home Assistant, then add the integration from Settings → Devices & services.
  • Manual install is also possible by copying the custom_components/neonplan3d folder.
  • Requires Home Assistant 2025.1 or newer.

Privacy

  • All plan data, pictures and packs are stored locally in Home Assistant’s .storage folder.
  • The only external call is when a licence key is entered for paid packs; the service contacts mastershort.de once per day with the key and an anonymous installation hash.

Development

  • Front‑end built with npm; tests run via npm test and pytest-homeassistant-custom-component for the Python side.
  • A preview mode (preview/index.html) lets you view the 3‑D UI in a browser without a Home Assistant instance.

Who it’s for

  • Home Assistant users who want a visual, tablet‑friendly way to monitor and control their smart‑home devices.
  • People who like a “neon” aesthetic or need a quick‑draw floor‑plan for automation debugging.

Links

All information above is taken directly from the repository’s README; no additional claims are made.

Related

  • Project
    sokrypton/ColabDesign

    A collection of Google Colab notebooks that make protein design accessible by integrating tools like AlphaFold, ProteinMPNN, and Rfdiffusion.

  • Project
    halcyon-video/halcyon-video

    A 3D virtual video store that turns digital media libraries from Jellyfin, Plex, or Emby into walkable, nostalgic aisles of VHS and DVD rentals.

  • Project
    scottstts/Threejs-Awesome-Graphics-Agent-Skills

    Threejs‑Awesome‑Graphics‑Agent‑Skills is an npm package that bundles ready‑to‑use, high‑quality Three.js code snippets (called “skills”) for advanced visual effects—procedural materials, realistic oceans, volumetric clouds, ray‑marched space scenes, and more. It installs these snippets into the directory layout expected by AI coding assistants (Codex, Claude Code, Cursor, etc.), letting the agents copy‑paste proven implementations when a user asks for “awesome graphics”. The repo includes an example gallery, validation scripts, and a deterministic capture pipeline, making it a practical resource for developers and AI‑augmented workflows.

  • Project
    zju3dv/neuralbody

    Neural Body is a research codebase that learns implicit neural fields with structured latent codes to model and render dynamic human bodies from multi‑view video. It supports training on ZJU‑MoCap and People‑Snapshot, rendering novel views, pose‑generalisation, mesh extraction, and provides Docker, distributed training, and pretrained models.