I made a 3D rock climbing analysis tool using iPhone LiDAR [video]
Details
- External ID
- 49849064
- Source
- HN
- Company
- —
- Product
- I made a 3D rock climbing analysis tool using iPhone LiDAR [video]
- Website domain
- youtube.com
- Launched
- Sept. 25, 2026
- Cohort
- —
- Upvotes
- 5
- Upvotes percentile
- 0.12998405103668262
- Tags
- —
- Fetched at
- Sept. 29, 2026, 5:01 p.m.
- Updated at
- Sept. 29, 2026, 5:01 p.m.
Description
Having learned a lot from sharing my previous rock climbing demos, I realized that a lot of rock climbing analysis is well-suited for 3D. Even something as simple as supporting videos where the person filming moves with the climber requires 3D information.To get the depth information, I used my iPhone 15 Pro's LiDAR depth sensor through my local iPhone app. I recorded the video and depth measurements from my app, and I ran the rest of the analysis on my computer. I used ViTPose+ Large for pose estimation and SAM 3.1 to segment the holds, both models accessed through the VLM Run Gateway.I think the holds activation is better, and I like the final view of all of the holds in 3D. It's also interesting to see the distance traveled in meters. Plus, it looks cool and it feels like a video game.Let me know what you think!The analysis code is open-source on GitHub: https://github.com/jeremyipark/vision-demos
Enrichment
- Theme
- Vertical
- Media & entertainment
- Function
- Analytics & BI
- Audience
- Prosumer
- AI stance
- Not AI
- Project type
- Hobby / open-source project
- Normalized one-liner
- lidar-based 3d route analyzer for rock climbers
- Manually corrected
- False
Could you build this?
No Creating a real-time 3D rock climbing biomechanical analysis tool using LiDAR requires advanced computer vision, 3D point cloud processing, and camera tracking/pose estimation algorithms. Developing sensor fusion between mobile LiDAR, camera odometry, and 3D human body pose estimation goes far beyond standard AI-assisted coding.
What it would actually take: The architecture requires an iOS native app (Swift/Metal) capturing ARKit LiDAR point clouds and depth maps, combined with a 3D pose estimation framework (e.g., SMPL/MediaPipe adapted to 3D mesh spaces). The hard technical problem is SLAM and motion compensation when the cameraman moves, registering human skeletal joints accurately onto a 3D wall mesh, and calculating physical metrics like center of mass and force vectors. This demands specialized computer vision, spatial computing, and biomechanics expertise.
Discussion
2 comments analyzed.
Concerns raised: LiDAR accuracy at long distances
Competitors
Other products that read as similar to this one — 61 launches clear the similarity bar, closest 8 shown.
Attention rank: #57 of 62 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 319 days after the earliest competitor.
- My local climbing gym from photogrammetry · hn · 2026-08-10 · 29 upvotes · similarity 0.64
- PotholeScan · ph · 2026-09-22 · 2 upvotes · similarity 0.53
- Faceblind · hn · 2026-07-21 · 9 upvotes · similarity 0.44
- Scan or describe it. Get a 3D print · ph · 2026-09-26 · 1 upvotes · similarity 0.42
- Open source navigation project for GPS and Internet denied regions · hn · 2026-06-19 · 6 upvotes · similarity 0.41
- Feature detection exploration in Lidar DEMs via differential decomp · hn · 2026-01-01 · 8 upvotes · similarity 0.41
- 3D-Motion-Camera · github · 2026-09-26 · 7 upvotes · similarity 0.40
- OptiLens 3D · ph · 2026-09-24 · 1 upvotes · similarity 0.40
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a analytics & bi tool for Legal yet.