Turn your home into VR laser tag

Hacker News

A project I submitted to the OpenCV Spatial AI competition last year and won first place for the North America region. Using cheap off-the-shelf OAK-D depth cameras and Quest 2 headsets, turns your space into a VR laser tag game. Demonstrates a simple way to detect walls and furniture, and synchronize the headset and camera coordinate systems. It’s a very crude prototype but does work. I have uploaded the code (a mix of Python and C# with Unity, with a tiny sprinkling of C++) to https://github.com/trzy/MRLaserTag

Visit product View on Hacker NewsJan 29, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: tiny, using, code · Missing: mac, agents, macos

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

28%28% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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