I made a 3D rock climbing analysis tool using iPhone LiDAR [video]
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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
HN · launched Sep 25, 2026 · 5 upvotes (top 87%) · 0 views
Niche: 3d model and interactive web viewers · 54 launches, 5 in the last 7 days ▼ -44% · Explore this niche →
Category and tags
- Horizontal
- Analytics & BI
- Prosumer
- Not AI
- Hobby / open-source project
Visit website ↗ Source listing ↗
More like this: all 54 launches in 3d model and interactive web viewers →
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 : 122 launches clear the similarity bar, closest 8 shown.
Attention rank: #116 of 123 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 319 days after the earliest competitor.
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