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My local climbing gym from photogrammetry

Details

External ID
49250141
Source
HN
Company
—
Product
My local climbing gym from photogrammetry
Website domain
github.io
Launched
Aug. 10, 2026
Cohort
—
Upvotes
29
Upvotes percentile
0.8044354838709677
Tags
—
Fetched at
Sept. 10, 2026, 5:32 a.m.
Updated at
Sept. 10, 2026, 5:32 a.m.

Description

Commented on yesterday's Ask HN: What are you working on? (August 2026), thought I might as well submit a Show HN.If you see climbing world cup/championship, there is this 3D modelling thing [1]. I want that for my weekly bouldering.I scanned my local climbing gym into 3D mesh using iphone. Built a simple editor to trim, merge, move/rotate meshes. You can interact with the mesh, view routes, view climb, etc. So this is unlike many gaussian splatting projects where the main use is for viewing. I built a pipeline in updating climbing walls. Climbing routes are manually annotated. Climbing videos are registered against the 3D mesh. Based on one input video, body positions resolved into 3D/4D space. you can view the body landmarks from different angle.COLMAP, OpenMVS, fastapi, svelte, database is just local json files. A read only build is exported so I can host on github pageIf you climb, or know someone who climbs, would love to hear your feedback. Do you usually record your climbs? If I make this a service, what will make you use it?[1] https://www.youtube.com/shorts/8zdUOaCr6DY

Enrichment

Theme
fitness tracking and training apps
Vertical
Travel & hospitality
Function
—
Audience
B2C
AI stance
Not AI
Project type
Hobby / open-source project
Normalized one-liner
3d model of climbing gym from photogrammetry
Manually corrected
False

Could you build this?

No Generating a detailed, clean 3D reconstruction of a full climbing gym from smartphone photogrammetry and turning it into an interactive climbing route viewer requires specialized computer vision, LiDAR/NeRF/Gaussian splatting pipelines, and complex 3D asset processing.

What it would actually take: Requires processing large video/image capture sets through structure-from-motion (SfM) pipelines (COLMAP, Nerfstudio, or 3D Gaussian Splatting) alongside LiDAR mesh alignment. The backend needs heavy GPU computing for densification, surface reconstruction, mesh decimation, and texture baking, before delivering via WebGL/WebGPU with custom shaders.

Discussion

11 comments analyzed.

Competitors mentioned: Instagram for sharing beta, Kaya for sharing beta, Griptonite.io for route tracking and video analysis, Strava for climbing activity tracking

Concerns raised: Limited practical value compared to existing platforms like Instagram and Kaya, Low usefulness for advanced climbers who just want to climb, Utility declines with climbing ability level, Repetitive re-recording needed for weekly route rotations at gyms, Insufficient fidelity to show millimeter-level detail of holds

Feature requests: Weekly leaderboard for different walls and routes with automatic time calculation, QR codes on walls to scan and view recommended beta sequences for beginners, Help section for every route with route visualization, Visual identification of walls/problems without QR codes using computer vision indexing, Gym management integration for route rotation updates

Competitors

Other products that read as similar to this one — 102 launches clear the similarity bar, closest 8 shown.

Attention rank: #28 of 103 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 283 days after the earliest competitor.

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