[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$f1af4ufffbxrsr":3},{"code":4,"message":5,"data":6,"timestamp":26},10000,"success",{"full_name":7,"position":8,"description":9,"language":10,"stars":11,"forks":12,"stars_gained":13,"age_days":14,"hn_threads":15,"one_liner":16,"summary_md":17,"topic":18,"github_topics":21,"license":22,"homepage":23,"updated_at":24,"history":25},"wesbos\u002F3d-model-anything",8,"Capture iPhone LiDAR and photos, reconstruct editable Three.js rooms, and explore them in a browser.","Swift",1650,256,514,3,[],"Turn an iPhone LiDAR room scan into an editable Three.js scene through an AI coding agent workflow.","## The problem\n\nGetting a real room into a browser as geometry usually means either buying a photogrammetry pipeline and cleaning meshes by hand, or eyeballing dimensions from photos. Neither gives you a scene you can edit as code. This repo records a room with an iPhone (LiDAR depth plus photos), then hands that evidence to an AI coding agent guided by a skill file so the agent measures, models and iteratively compares renders against the source photos.\n\n## What's different\n\nThe interesting part is not the viewer — it is the skill at `.agents\u002Fskills\u002Fscan-to-threejs\u002FSKILL.md`. It encodes a reconstruction loop: read the capture export, check measurements, build the scene, render, diff against photos, adjust. That turns \"make me a 3D room\" from a one-shot prompt into a repeatable process with feedback, which is the main failure mode of agent-assisted modeling. The output is a hand-editable Three.js project rather than a black-box mesh from a scanning app. The README is explicit that this is not one-click scan-to-model, and the demo's Scan and Overlay modes are disabled because original scans aren't shipped.\n\n## Who it's for\n\nThree.js developers and creative coders who want a room or interior as editable scene code, and who already use an agent with local skill support. It also suits people doing architectural\u002Finterior visualization prototypes. It does not suit anyone without a LiDAR iPhone or iPad for capture, anyone wanting a finished, installable product (there is no App Store release yet), or anyone who needs outdoor\u002Flarge-scale scenes — the README limits the workflow to rooms and interiors.\n\n## Cost of adoption\n\nYou need Xcode to build the capture app, a LiDAR-capable device, and Node.js 22.12+ for the viewer (`npm ci` then `npm run dev`). Helper scripts need Python 3, Pillow for photo comparison, and optionally NumPy and Open3D for depth reconstruction; capture scripts call the `agent-browser` CLI. The code and skill are MIT, but the office images and textures carry separate asset terms — check them before publishing your own demo. The app is source-available and not yet distributed on the App Store, so treat it as a workflow you assemble yourself.\n\n## Verdict\n\nThe repo is three days old with roughly 1.6k stars and about +522 per day, but it has not appeared on the daily trending board and has no Hacker News discussion — that pattern reads as a launch spike from the author's audience rather than sustained adoption. There is real substance: a working viewer, a specified capture export format, and a skill that documents the iteration loop. But the end-to-end path is long and partly DIY, and the demo is curated. Worth cloning to study the agent-skill pattern even if you never scan a room; adopt it for actual work only if you already own the hardware and are comfortable maintaining the pipeline.",{"slug":19,"name":20},"developer-tools","Developer Tools",[],"NOASSERTION","https:\u002F\u002Fwesbos.github.io\u002F3d-model-anything\u002F","2026-10-24T21:05:58Z",[],1792890769]